Paint Analysis 9783748602354

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Roger Dietrich

Paint Analysis

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Bibliographische Information der Deutschen Bibliothek Die Deutsche Bibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie ; detaillierte bibliografische Daten sind im Internet über http://dnb.ddb.de abrufbar.

Dietrich,Roger PaintAnalysis Hannover:VincentzNetwork,2009 (European Coatings Tech Files) ISBN 3978-3-7486-0235-4 © 2009 Vincentz Network GmbH & Co. KG, Hannover Vincentz Network, P.O. Box 6247, 30062 Hannover, Germany This work is copyrighted, including the individual contributions and figures. Any usage outside the strict limits of copyright law without the consent of the publisher is prohibited and punishable by law. This especially pertains to reproduction, translation, microfilming and the storage and processing in electronic systems. The information on formulations is based on testing performed to the best of our knowledge. Please ask for our book catalogue Vincentz Network, Plathnerstr. 4c, 30175 Hannover, Germany Tel. +49 511 9910-033, Fax +49 511 9910-029 E-mail: [email protected], www.european-coatings.com Layout: Maxbauer & Maxbauer, Hannover, Germany ISBN 978-3-7486-0235-4

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European Coatings Tech Files

Roger Dietrich

Paint Analysis

Roger Dietrich: Paint Analysis © Copyright 2009 by Vincentz Network, Hannover, Germany

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More than 50 variants of desk-, cabinet- and chest-type units with differently sized chamber volumes ranging from 300 to 2500 litres create the maximum space conditions for your test specimens.

Corrosion

i n Q u i c k - M ot i o n

CORROSIONTESTING EQUIPMENT wet chemical quality testing

Depending on the test specifications, the process systems Salt Spray (S), Condensation Water (K), Ambient Air (B) and Hot Air (W) can be installed individually or as combinations (alternation tests). The units are available for manual or automatic operation.

the sign of future Gebr. Liebisch GmbH & Co.KG Eisenstraße 34 D-33649 Bielefeld

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w w w .liebisch.com

Fon +49/521/94647-0

mail @ liebisch.com

Fax +49/521/94647-90

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Preface Materials have long been coated for decorative or protective purposes. When industrialisation was applied to coating processes and the mass production of coating materials, the need arose for quality controls. Quality needed to be measurable. Over the years, a great many techniques have been developed for testing both the coatings raw materials and the coatings themselves. But some knowledge of the chemical and physical properties of coatings, the intended substrates and their constituent raw materials is also needed. This can be provided by measuring the viscosity, solvent content, colour, gloss, haze and the like. Such measurements, to be sure, tell us if the coating meets certain requirements. The underlying techniques are well established and described elsewhere, and aren’t discussed here. However, what “standard methods” don’t tell is why a particular coating either meets or fails to meet a requirement. This aspect, then, forms the starting point for this book. In it, I present modern analytical techniques and their applications in the coatings industry that answer further complex questions, such as: • • • • •

Why does a coating peel off a substrate? Why does a coating become discoloured? What kinds of trace contaminant are present in a binder? What causes paint craters? What is the chemical composition of the surface of a polymer substrate for coating?

The techniques described herein can be used for performing failure analysis, production control and quality control, and also meet the requirements of modern high-level quality management. This book is therefore primarily aimed at engineers and technicians engaged in coatings production, coatings application and substrate production. It can act as a reference for explaining why a coating or production failure has occurred and at the same time provide inspiration for an analytical solution. It can also be used for educational purposes. The examples presented herein only scrape the surface, as it were, of the possibilities which the various techniques offer. Nearly every day, more and more fields of application are being developed. I welcome all your comments and suggestions and look forward to engaging in lively discussions with you ([email protected]). Dr. Roger Dietrich Osnabrueck, Germany, May 2009

Roger Dietrich: Paint Analysis © Copyright 2009 by Vincentz Network, Hannover, Germany ISBN: 978-3-86630-912-8

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Content

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Content Part I

Introduction...........................................................................................

10

1 1.1 1.2 1.3

Surface.......................................................................................................................... Relevance of modern analytical techniques to paint analysis.......................... General considerations............................................................................................. Instrumentation..........................................................................................................

10 12 13 17

Part II

Theory of analysis.................................................................................

18

1

Optical light microscopy...........................................................................................

18

2 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.7.1 2.7.1.1 2.7.1.2 2.7.1.3 2.7.1.4 2.7.1.5 2.7.1.6 2.7.1.7 2.8 2.8.1 2.8.2 2.8.3 2.8.4 2.9 2.9.1

Infrared spectroscopy................................................................................................ Physical background.................................................................................................. Characteristic absorptions........................................................................................ Instrumentation.......................................................................................................... Sample preparation.................................................................................................... Plotting of spectra...................................................................................................... Quantification.............................................................................................................. Surface infrared spectroscopy................................................................................. ATR-FT-IR spectroscopy............................................................................................ Physical background.................................................................................................. Depth of penetration.................................................................................................. Information depth...................................................................................................... Effective path length................................................................................................. Quantification.............................................................................................................. Detection limit............................................................................................................ Instrumentation.......................................................................................................... Infrared microscopy................................................................................................... Instrumentation.......................................................................................................... Infrared transmission spectroscopy....................................................................... Infrared microscopy, reflection mode.................................................................... ATR microscopy.......................................................................................................... Data evaluation........................................................................................................... Use of databases.........................................................................................................

20 20 22 23 24 25 26 27 27 28 30 31 31 32 33 33 36 37 38 38 39 40 40

3 3.1 3.2 3.3 3.4 3.5

Time-of-flight secondary ion mass spectrometry................................................ Physical background.................................................................................................. Instrumentation.......................................................................................................... Sample preparation.................................................................................................... Spectral evaluation.................................................................................................... Quantification..............................................................................................................

43 43 45 46 47 49

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Content

3.6 3.7 3.8 3.8.1 3.8.2 3.8.3 3.8.4

Summary...................................................................................................................... Imaging........................................................................................................................ Applications................................................................................................................ Studies of binders and resins.................................................................................. Quality control of raw materials.............................................................................. Structural analysis..................................................................................................... Fogging.........................................................................................................................

50 50 51 51 52 52 53

4 4.1 4.1.1 4.1.2 4.1.3 4.1.4 4.2 4.3 4.4

Scanning electron microscopy................................................................................ Physical background.................................................................................................. Secondary electrons.................................................................................................. Back-scattered electrons........................................................................................... Characteristic X-ray radiation.................................................................................. Resolution.................................................................................................................... Instrumentation.......................................................................................................... Sample condition........................................................................................................ Information depth......................................................................................................

54 54 55 56 57 58 58 59 60

5 5.1 5.2 5.3 5.4

Electron microanalysis.............................................................................................. Physical background.................................................................................................. Quantification.............................................................................................................. Detection limits........................................................................................................... Applications................................................................................................................

61 61 64 65 65

6 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.8 6.9 6.10

X-ray photoelectron spectroscopy........................................................................... Physical background.................................................................................................. Information depth...................................................................................................... Lateral resolution....................................................................................................... Information retrieval................................................................................................. Quantification.............................................................................................................. Instrumentation.......................................................................................................... Applications................................................................................................................ Technical Data............................................................................................................. Performance of selected methods........................................................................... Literature.....................................................................................................................

67 67 68 68 69 71 72 73 74 75 75

Part III Applications...........................................................................................

76

1 1.1 1.1.1 1.1.2 1.2 1.3

Quality control............................................................................................................ Binders......................................................................................................................... Identity control........................................................................................................... Detection of trace contaminants............................................................................. Solvents........................................................................................................................ Pigments and fillers...................................................................................................

76 77 78 80 82 85

2 2.1 2.1.1 2.1.2

Production control...................................................................................................... Analysis of filter residues......................................................................................... SEM/EDX analysis of filter residues...................................................................... FT-IR analysis of filter residues...............................................................................

89 89 90 92

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2.2 2.3 2.4

Analysis of fogging residues.................................................................................... Investigation of the degree of crosslinking in 2-pack paints............................ Investigation of paint additive migration..............................................................

94 96 99

3 3.1 3.1.1 3.1.2 3.1.3 3.2 3.2.1 3.2.2 3.2.3 3.3 3.4 3.5 3.6

Investigation of paint failures.................................................................................. Adhesion and wetting problems............................................................................. Paint delamination caused by surface contaminants......................................... Paint delamination caused by migration.............................................................. Delamination of polymer substrates...................................................................... Investigation of paint cratering............................................................................... Cratering caused by contaminants of the paint.................................................. Craters and pinholes cused by substrates contaminants.................................. Craters caused by paint additive agglomeration................................................. Investigation of paint blistering.............................................................................. Stains and deposits on painted surfaces............................................................... Analysis of paint spots.............................................................................................. Conclusion...................................................................................................................

103 103 104 108 109 111 114 117 118 121 121 123 126

References..............................................................................................

131

Author....................................................................................................

132

Index......................................................................................................

133

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Introduction

Part I  Introduction 1

Surface

This book deals with the application of modern techniques to paint analysis, with a special focus on surface analysis. If we pause to consider the word “surface”, we soon realise what a relative and vague term it is. To a painter, “surface” does not mean the same as it does to a surface chemist. To a painter, the surface represents that part of an object which is usually presented to the outside world and can be touched and observed directly. However, it can also be defined as the boundary layer between a solid or liquid material and a surrounding liquid or gaseous phase. A surface physicist would probably refer to it as a phase interface. Alternatively, it could be defined as the area of a solid or liquid thing at which the bulk physical and chemical properties change instantly, a so-called property boundary. A surface chemist, however, is talking about the uppermost molecular layers of a material when he uses the word surface. This is an area that can’t be observed without the help of analytical techniques. In fact, the uppermost layers of an object often determine the quality and behaviour of the material as far as (paint) adhesion is concerned. Definition of surface So let’s first define how we shall use the word surface in this book. A surface is a boundary layer which separates a substrate from the surrounding environment (air, liquid). It is typically 1 nm to 1 µm thick. In contrast, a “thin layer” is defined as being 1 µm to 10 µm thick. The surface plays a significant role in the physical and chemical properties of a material. Let’s look, for example, at a toll manufacturer who paints and coats coils and metal profiles. The surface of the raw material might well look clean. However, the material has a long history before it has been delivered to this company to be painted or coated. Production, storage and transport of a coil, for example, afford much opportunity for numerous substances to be adsorbed onto the surface. This surface layer of, say, contaminants may not be visible, but it Figure I-1: AFM (atomic force microscope) image of a paint surface (60 x 60 µm)

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Surface

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is there nonetheless. And sometimes even traces of contaminants can seriously impair the adhesion of a coating to a surface. When it comes to processing of the coil, the chemical composition of the outermost molecular layer plays a significant role. If the coil has been coated with a protective layer of oils to prevent corrosion during transport and storage, the paint will exhibit poor adhesion or craters after application. Even a monomolecular layer of some of these oils can have deleterious effects on coating procedures. As these ultra thin layers are invisible, the unfortunate manufacturer is in fact “blind” as far as the surface quality of his coils is concerned. In most cases, therefore, he will decide to install a cleaning process before applying the coating. But he will do so without knowing if it is necessary and, even worse, without knowing what to remove from the surface. Unfortunately, there is no “magic” process for eliminating all the various kinds of contaminants. His efforts might well produce a surface quality worse than before, due to the presence of oil residues and traces of cleaning chemicals, such as surfactants. The same is true of the coating material itself. As the paint and the painted substrate have to be a chemical match if good adhesion is to be obtained, a few questions need to be asked before the painting process is started. • What is the chemical composition of the substrate surface? • Which pre-treatment can be used to improve paint adhesion and what effect will it have? • How do the paint ingredients influence the surface of the material that has to be painted? • What influence do the paint additives have on paint adhesion? Unfortunately, these questions often can’t be answered by simple tests or classical chemical analysis because they require an ability to analyse tiny amounts of substances that have high surface sensitivity. Only the surface analysis techniques described in this book can answer these questions A growing field of application for modern surface analytical techniques is not only paint application but also paint production. Modern high-performance paints have to fulfil many requirements simultaneously that are sometimes hard to match. This not only creates a demand for characterisation of the raw materials and products. The chemical interaction of paint compounds and the reaction between each compound and the ingredients of the substrate (e.g. a polymer) are also key parameters. If, for example, a moulded polymer part has to be coated, it is not just the polymer which is of interest. The manufacturer or supplier of the raw material matches the original polymer to customer demands. In accordance with the requirements imposed on the polymer material, he adds additives to improve flame, light, impact or heat resistance. One parameter the supplier is not concerned about is the paintability of the product made from the granules which he supplies. That is a process which the polymer supplier does not see.

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Surface

However, it has been shown in the past that additives present in polymers “designed” to enhance moulding processes, especially in the offering good release from injection moulds, can prove disastrous for the painting process. Most of the additives incorporated into a polymer migrate to the surface, driven by temperature, humidity, time or solvents. This sometimes leads to unpredictable results, such as paint adhesion failure, chemical reactions, discoloration, and wetting failure. Many manufacturers of paint for automotive interior parts have therefore discovered that it is essential not only to know their own paint manufacturing process, but also to learn something about the polymers which have to be painted. This is a task that can easily be fulfilled by the techniques we are going to describe in this book.

1.1 Relevance of modern analytical techniques to paint analysis There are hundreds of techniques for analysing paints and coatings. They yield information about viscosity, gloss, haze, hardness, acid value, etc. In other words, they describe the product and its properties. They ensure that the desired level of quality is achieved. On the other hand, standard analytical tools often fall short when failures and production problems arise. The standard techniques are perfect for finding out the quality of a product. However, if a product is sub-quality and the question is asked as to why this happened, the standard techniques are not very helpful. For example, a monomolecular layer of a release agent on the surface can easily cause severe adhesion failure if the material is to be painted. The quantity of substance may be too low to be detected by standard techniques. Or, poor cleaning procedures in a paint shop might cause paint defects of a few microns in size. Before this problem can be solved, it is necessary to know what has caused the paint defect. The substance or inclusion particle causing this failure is too small to be characterised by standard techniques. This is an analytical gap that can be closed by the surface analytical techniques described in this book. They will help to answer the question: Why does a product have unexpected properties and why do failures happen? Typical topics in paint analysis are: • What do contaminants in paint layers or wet paint samples consist of? • What is the chemical composition of paint layers at a certain depth from the surface? • How are chemical bonds formed between paint components? • Why does a paint layer peel off a substrate and where does the delamination take place? • What is the reason for paint spots? It should be mentioned that there is no all-embracing technique that can answer these questions. In fact, there are many parameters which influence the decision as to which technique to employ for the analysis, including: • additional information about the appearance of the defect • preliminary sample investigation by optical light microscopy • chemical and physical properties of the coating • Desired detection limit In other words, it takes an experienced user to find the best tool that can answer the questions raised about the sample. These considerations will be discussed later in this book.

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General considerations

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1.2 General considerations Before we present the techniques that will be discussed in this book, it would be helpful to find a common basic principle to describe them. No matter whether we are talking about infrared spectroscopy or TOF-SIMS or SEM, the main principle consists in probing a sample with radiation.

Figure I-2: General concept of probing the surface of a sample with radiation

The sample is essentially analysed by radiation that probes for specific properties and characteristics of the material. This radiation, which is called the primary radiation, can consist of electrons, ions, neutral particles and photons, such as infrared waves and X-rays. The primary radiation triggers a reaction specific to the sample that may take the form of the emission of electrons, ions or X-rays. This “reaction” by the sample is detected by an electronic system composed of an analyser and a detector. The result can be displayed as a spectrum on a computer or be printed on paper. The last step of the process is data evaluation by an experienced analyst. The evaluation must include • plausibility check • comparison with databases • interpretation with respect to the analytical problem The nature of the interaction which occurs between the probing beam and the sample depends on the type, energy and angle of incidence of the probing radiation and, of course, the sample material.

Figure I-3: Interaction between the primary radiation and the sample

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14

Surface

The primary radiation interacts with the sample in a specific way. Each type of sample reaction can be detected separately and analysed to reveal the chemical and physical composition of the sample and its surface. The radiation emitted by the sample is called secondary radiation. Each type of primary radiation can produce a different type of secondary radiation. Probing with an electron beam, for example, may lead to the formation of: • secondary electrons • X-rays • back-scattered electrons • fluorescence

Figure I-4: Components of primary radiation

Figure I-5: Result of sample excitation by primary radiation

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Figure I-6: Surface analysis techniques

Each type of radiation conveys different information about the sample that all adds up to a comprehensive understanding of the sample’s properties. Not only the primary radiation, but the secondary radiation emitted by the sample, too, can consist of electrons, ions, neutral particles and photons that result from sample excitation or reflection of the primary radiation. The latter is a consequence of diffraction and dispersion that change the energy, angle and intensity of the primary radiation in accordance with the topography, structure and chemical composition of the sample. The secondary radiation emanating from the sample is detected, analysed and displayed in the form of an angle-, energy- or mass-resolved spectrum, which contains information about the sample and its surface. The various types of probing primary radiation and detected secondary radiation have spawned more than 50 different analytical techniques over the decades. Some of them are useful for solving practical problems and have made their way into routine work. Many of them, however, never passed the experimental stage and have very limited application to technical samples outside of academia. In this book, we will cover those techniques which have proven to be very useful for routine work and can deliver data in a reasonable time and at reasonable cost. The in Table I-1 (page 16) mentioned techniques yield different data about the sample. Each has its particular strengths and weaknesses. It is very important to appreciate this when trying to find the right combination for the given analytical problem. It is commonly said that one technique on its own is no use and so a combination is the best way of achieving the right results. The parameters to know about a technique are its • information depth • detection limits • information content • suitability for technical problems Some techniques, for example, allow only very limited sample sizes, which sometimes renders the technique useless for “real world samples”. Others require vacuum conditions, and that excludes liquid or volatile samples. Only a handful of techniques have proven useful for routine work. The limiting features are:

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Surface

Table I-1: List of analytical techniques Primary radiation

Secondary radiation

Technique

electrons

electrons

auger electron spectroscopy

AES

uppermost molecular layer

scanning electron microscopy

SEM

sample surface down to a depth of a few microns

X-rays

electron microanalysis

ESMA EDX WDX

sample surface down to a depth of a few microns

infrared

surface infrared spectroscopy

FT-IR ATR IRRAS

sample surface down to a depth of a few microns

Infrared microscopy

IRM

infrared

Abbreviation

Analysed area

X-rays

electrons

X-ray photoelectron

XPS ESCA

sample surface down to a depth of a few nanometres

ions

ions

secondary ion mass spectroscopy

SIMS TOF-SIMS

uppermost molecular layer

• measuring time per sample • suitability for technical samples • comprehensive databases of reference materials • sample preparation Another important question is the sample area to be analysed. If, for example, a paint crater a few microns in diameter has to be analysed for possible surface contaminants capable of causing cratering, the technique to use must allow for spot analysis. This means the investigation of a very small spot with a lateral resolution of a few microns. If, on the other hand, it is the general surface quality of the sample which is of interest, a larger area measurement must be performed in order that a representative image of the surface composition may be obtained.

Figure I-7: Measuring modes in surface analysis

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Analysis of the distribution of a specific substance over a certain

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Instrumentation

17

area calls for a scanning technique that generates a chemical map of the analysed area. On the assumption that not only the chemical composition of a surface area has to be analysed but also the depth distribution, a depth-profiling mode needs to be chosen. That entails sputtering the sample layer by layer and analysing the surfaces as they become exposed.

Figure I-8: Schematic diagram of instrumentation

1.3 Instrumentation Many of the analytical techniques we will describe here require vacuum conditions. Although they differ greatly in detail and in their chemical background, the instrumentation used follows a general concept. The instrumentation setup for all techniques consists of an excitation system (the primary system) that generates photons, electrons or ions. The primary beam is directed by a focusing system onto the sample surface and into the desired area. Some techniques have an additional sputtering system (e.g. an ion gun) that allows for subsequent sputtering of layers and thus for depth profiling. After interaction of the primary beam with the sample (surface), the excited secondary radiation is collected by a ray optics system which directs the secondary beam towards the analyser. The analyser separates the secondary radiation spectroscopically by energy, direction or mass. The detector records the separated or resolved signals and measures their intensity. The signals generated by it are displayed as a spectrum, which is a chart of intensity versus wavelength, mass, or energy.

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Theory of analysis

Part II  Theory of analysis 1

Optical light microscopy

One of the main topics of this book is failure analysis. Years of experience of failure analysis show that a great deal of the serious damage is due to very small defects, particles, fibres, cracks or material defects. The first step in failure analysis is to find out what kind of problem we are dealing, and in most cases the tool to use is (optical) light microscopy. This basic technique can be used to carry out a preliminary sample inspection to gain an overview of the problem. Light microscopy reveals initial, basic answers to such questions as • What might be causing craters and spots in paint layers? • Where does paint delamination originate in a multi-layer system? • What does a residue in a raw material look like? The basic theory of light microscopy (LM) has been described elsewhere and will not be repeated in this book. However, it is worth focusing on a special method of LM that was developed a few years ago and offers highly interesting possibilities with respect to material surfaces.

Figure II-1: Painted key panel showing paint adhesion failure after laser treatment; A= light microscopy image of the border between lasered symbol and paint, B= EFI-3D image of the same area

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Optical light microscopy

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A main disadvantage of conventional light microscopy is the lack of depth of focus. At high resolution, rough material surfaces, such as those of structured polymers or metals, cannot be inspected both very sharply and at high resolution at the same time. Therefore, in the past, a scanning electron microscope had to be used to scan the surface topography of rough and structured samples, even for low-resolution purposes. Thanks to the latest developments in digital cameras and software solutions, the depth of focus of light microscopy can be extended virtually by a so-called EFI option (Extended Focal Imaging). This is one module of the image-processing software “AnalySIS” developed by the company SIS/Olympus. It automatically takes pictures of several focal planes in rough objects, extracts only the sharp details of each focal plane and adds them together to produce an image of unlimited depth of focus.

Figure II-2: Light microscopy image of a paint crater (top) and calculated EFI-3D image of the same paint failure

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Infrared spectroscopy

It is thus an easy matter to obtain topographical images of extremely rough surfaces or paint failure without the help of scanning electron microscopy for magnifications of less than x1000. This additionally permits the layer thickness and topography to be measured.

2

Infrared spectroscopy

Infrared spectroscopy (IR) is an analytical tool that has been well known for decades but only lent itself to routine work with the application of Fourier transform to data processing. The basic principle of IR spectroscopy is the structural characterization of materials through the absorption of infrared radiation by inter-atomic bonds. A defined wavelength range is scanned with infrared light to yield a collection of absorption information which can be displayed as bands in an infrared spectrum. The spectrum is evaluated by comparison with reference spectra and by examining the individual peaks to identify the various functional groups in the molecule or material, such as esters, hydrocarbons, acids, amines and the like.

2.1 Physical     background

Figure II-3: Basic principle of infrared spectroscopy

The probe used in IR spectroscopy is radiation from the infrared region of the electromagnetic spectrum. This corresponds to energies between 0.001 and 1.6 eV. These photons excite characteristic vibrations of the interatomic bonds in a molecule. The energy needed to excite the vibrations is absorbed from the incident infrared radiation. As the bonds between different atoms have distinct bond energies, it takes characteristic energies to excite them; this is known as the “chemical shift”. These energies correspond to certain wavelengths of the infrared beam.

Figure II-4: Simplified model of an inter-atomic bond consisting of two masses m1 and m2 joined by a spring

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Physical background

21

Figure II-5: Stretching and bending vibrations of a CH2 group

To understand this mechanism, consider a model of a chemical bond between two atoms in which two masses m1 and m2 are joined by a spring. This is called an “harmonic oscillator”. (Of course, a real inter-atomic bond is not an harmonic oscillator, but the model illustrates the basic principles very well.) The spring between the two masses (atoms) has a force constant, k. If the two masses oscillate against each other, the frequency of the vibration can be described by Hooke’s law. k µ

Equation II-1:

ν=

1 2πc

where

µ=

m 1 m2 m1+ m2

If one mass of this harmonic oscillator is changed (i.e. one atom is replaced by a different one), clearly the frequency of the vibration must change. The molecular structure governs the type of vibrations which can be excited – stretching, rocking, or bending. The excitation of these vibrations follows certain rules: • Interaction of an inter-atomic bond with the radiation is only observed if the excited bond is a dipole, e.g. an -N-H bond. If a bond or molecule is completely symmetrical, it cannot be excited by infrared radiation • Chemical bonds that represent strong dipoles absorb very strongly in the infrared spectrum • The number of degrees of freedom and therefore the number of fundamental vibrations of a molecule consisting of n atoms is n = 3N-6 for a nonlinear molecule and n = 3N-5 for a linear molecule. Infrared methods can be categorised by the wavelength of the exciting beam, see Table II-1 (page 22). These different wavelength or wave number regions in the electromagnetic spectrum are used for specific applications in paint analysis. NIR mainly serves quality control purposes

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Infrared spectroscopy

Table II-1: Wavelength of infrared radiation Infrared

Wavelength

Wavenumber

near infrared, (abbr.: NIR)

λ= 760 to 2550 nm

λ = 13,200 to 4000 cm-1

mid infrared, (abbr.: MIR)

λ = 2.55 µm to 25 µm

λ = 4000 to 400 cm-1

far infrared, (abbr.: FIR)

λ = 0.025 mm to 1 mm

λ = 400 to 10 cm-1

whereas MIR spectroscopy is used for structural identification. FIR doesn’t play a role in paint analysis. Having passed through the material, the infrared beam is analysed and the transmitted portion of the infrared radiation is detected. The level of absorption at each infrared wavelength is recorded to yield the infrared spectrum. The infrared spectra of many organic and inorganic compounds are unique patterns of peaks and serve as a fingerprint for identifying such compounds. In mixtures, the intensity of the pattern produced by each compound is proportional to the concentration of that compound.

2.2 Characteristic absorptions For routine structural organic determinations by a battery of spectroscopic methods, the most important absorptions in the infrared region are the simple stretching vibrations. The stretching vibrations of typical organic molecules tend to fall within distinct regions of the infrared spectrum, as shown below. To illustrate the above-mentioned rules, consider the spectrum of methanol: Starting at 4000 cm–1, which is very typical of mid-infrared spectra, the spectrum displays a broad peak between 3600 cm–1 and 3000 cm–1 that can be attributed to the O-H stretching vibrations of the hydroxyl groups. The C-H stretching vibrations of the methyl group appear Table II-2: Examples of some characteristic infrared vibrations Functional group

Characteristic absorption (cm-1)

Substances

Typical paint compounds

alkyl C-H stretch

2950 to 2850 (m or s)

alkanes or alkyl side chains

waxes, hydrocarbon solvents

alkenyl C-H stretch alkenyl C=C stretch

3100 to 3010 (m) 1680 to 1620 (v)

unsaturated hydrocarbons or substances with unsaturated side-chains

unsaturated alkyd resins

aromatic C-H stretch

~3030 (v)

aromatic hydrocarbons

solvents such as toluene, aromatic resins

O-H stretch C-O stretch

3550 to 3200 (broad, s) 1000 to 1200 (s)

alcohols, polyesters, polyols

polyester resins, butanols, PEG, PPG

N-H stretch

3500 to 3300 (m)

amines, polyurethanes

HALS, DABCO, 2-pack PUR resins

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Instrumentation

23

Figure II-6: Infrared spectrum of methanol Table II-3: Characteristic peaks in the spectrum of methanol Type of vibration

Description

Peak assignment [cm-1]

ν, CHx

stretching vibration of the methyl group

2943 2831

δ, CH2

H-C-H deformation vibration, bending of the methyl group

1448

ν, OH

stretching vibration of the hydroxyl group

3316

ν, OC

stretching vibration of the C-O group

1010

at a lower wave number between 3000 cm–1 and 2800 cm–1. They split up into asymmetric and symmetric vibrations that can be distinguished in the spectrum. Further down the wave number scale, the bending vibrations of the methyl group can be detected at about 1448 cm–1. Finally, the C-O stretching vibration of the hydroxyl group is to be found at 1010 cm–1. This simple example demonstrates how the structure of a molecule (which may be a solvent) correlates with its infrared absorption spectrum.

2.3 Instrumentation The technical principle behind infrared spectroscopy is to compare the intensity of an infrared beam which has passed through a sample with a second one, called the reference beam, which has had no interaction with the sample. In the past, this was achieved with a two beam

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Infrared spectroscopy

Figure II-7: Principles of Fourier transform infrared spectroscopy

alignment. Nowadays, the technique is based on a Fourier transform of one beam that has been divided by a beam splitter before passing through the sample. The device used for this is called a Michelson interferometer and it works as follows. An interferometer is an optical device that splits a light beam into two beams and then recombines them after each has travelled along a unique path. When this radiation recombines in the FT-IR spectrometer, a complex beam of undulating intensity is generated. This periodic change in beam intensity happens because one light path in the interferometer is constantly changing. Application of a Fourier transform to this time-varying intensity produces the infrared spectrum. The sample is placed in a sample chamber of the FT instrument. The sample chambers are designed to fit a variety of sampling tools, such as the above-mentioned transmission experiment, ATR-FT-IR and IRRAS accessories. Data acquisition, processing and evaluation are normally performed with the aid of software solutions which are distributed by the instrument suppliers.

2.4 Sample preparation For classic transmission infrared spectroscopy in the mid-infrared region (MIR), a very small amount of the sample is mixed with a surplus of a matrix substance that exhibits little interaction with the infrared beam, e.g. KBr or PE, and pressed into a disc. The disc is placed in the beam such that the beam passes through the disc in a rectangular shape. Liquids such as solvents or diluted binders are placed between two thin discs of KBr or NaCl or filled into cuvettes to again form a thin layer which can transmit the infrared beam. This preparative technique can be used for:

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Plotting of spectra

25

• analysing solvents and binders • characterizing clearcoats • identifying spots in clearcoats • identifying contamination or segregation layers on paint surfaces However, it is usually limited to clearcoats. For the analysis of pigmented and filled paints (which in fact represent the majority of samples), ATR-FT-IR (an infrared reflection method) is ideal.

2.5 Plotting of spectra The acquired sample spectrum I(ν) displays the • emission spectrum of the IR source • transmission of the optical components of the spectrometer • characteristic of the detector and, of course, • absorption by the sample. So, there is a great deal of information in the spectrum that has nothing to do with the chemical composition of the material which we want to analyse. To get rid of this data, the measured spectrum I(ν) is divided by a reference spectrum I0(ν). This reference spectrum, which is normally measured with a beam which does not pass through a sample, includes all the non-sample specific information. The resulting transmission spectrum T(ν) contains information on the chemical composition of the sample: T(ν) = I(ν)/I0(ν) A more versatile plot of an infrared spectrum is its absorbance spectrum A(ν), which is derived from the transmission spectrum by: A(ν) = log 1/T(ν)

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Figure II-8: Transmission and absorbance spectra of a paint additive

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Infrared spectroscopy

The absorbance A or extinction E is proportional to the sample concentration and the sample thickness and therefore can be used for quantification purposes.

2.6 Quantification Quantitative determination of the concentration of a substance requires a linear relationship between the sample concentration and sample thickness on one hand and the detected extinction or absorbance on the other. The linear relationship between absorbed radiation and substance concentration for the transmission experiment is given by the Lambert-Beer law: Equation II-2:

I0 = αcd = E λ I

where   Eλ is the absorbance and   α is the extinction coefficient. Quantitative analysis consists in connecting the concentration c of an analyte to the intensity of a characteristic peak by the absorbance Eλ. The thickness of the analysed layer is defined, for example, by the width of the infrared cell where a classical transmission experiment on a solvent is concerned. But what does this mean for the actual analysis? If, for example, the concentration of a paint solvent such as butyl acetate in a mixture of paint solvents has to be determined, you start with the calibration curve. That means you need to prepare at least ten samples containing a known concentration of the analyte. The second step is to record the absorbance spectra of these ten standards. From the spectra, you then pick a characteristic peak that is of reasonable intensity and doesn’t interfere with peaks from the rest of the sample. The intensity of this peak is measured by integrating the peak area or the peak height. The intensity data can now be plotted against the known concentration of the analyte in an x/y chart. Linear regression of the data points should yield a straight line indicating a linear relationship between concentration and peak intensity. The correlation between the slope and the intercept of this straight line fit can now be used to determine the concentration of the solvent in an unknown mixture by relating the peak intensity of the analyte of the unknown mixture to the calibration curve.

2.7 Surface infrared spectroscopy Standard absorption/transmission infrared spectroscopy of thin layers (KBr discs or liquid cell) is useful for identifying and characterizing even low concentrations of substances. However it has one disadvantage: it is not surface sensitive, since the infrared beam passes through the sampling area in a rectangular manner. With paint samples, we are often dealing with surface phenomena which can’t be resolved by transmission techniques. For all these cases, surface-sensitive infrared reflection techniques,

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Surface infrared spectroscopy

27

such as ATR-FT-IR, permit analysis of very thin surface layers, of thin layers of filled and pigmented paints, and of the surface quality of the substrate to be painted. They all have a low detection limit and a high surface sensitivity. When we switch over from transmission to reflection spectroscopy, we compare the intensity of an infrared beam that is reflected by a sample surface with that of a reference beam that has not interacted with a sample. However, in contrast to the transmission method, there are some physical processes and parameters (such as refraction, multiple reflection, and polarisation) that influence the appearance of an infrared reflection spectrum. To distinguish between chemical effects in the infrared spectrum from physical effects such as asymmetric peaks and peak inversions, it is useful to know something about the “behaviour” of infrared radiation passing through the interface between two media of different refractive index. The methods which we describe now are based on external reflection (such as IRRAS and infrared microscopy) and internal reflection (ATR-FT-IR). Therefore, we have to deal with the transmission of infrared radiation in different optical media and the reflection and refraction of light at the interface of the sample and, for example, air.

2.7.1 ATR-FT-IR spectroscopy Paints are normally very strong absorbers of infrared waves. Consequently, they don’t transmit infrared radiation and therefore are not amenable to analysis by infrared transmission techniques. The best way to characterize these layers is to use internal reflection infrared spectroscopy, called MIR, ATR, or FTR. Attenuated total internal reflectance (ATR) spectroscopy is a versatile and powerful technique for infrared sampling. It makes for rapid analysis

Figure II-9: Basic principle behind ATR-FT-IR spectroscopy

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Infrared spectroscopy

as little or no sample preparation is usually required. ATR is ideal for those materials which are strong absorbers. In addition, ATR provides useful information about the surface properties or conditions of a material. The infrared light is transmitted by a crystal made of infrared-transmitting material which is in physical contact with the sampling area. The interface between the sample and the ATR-FTIR crystal is where interaction of the infrared beam takes place. The sample molecules of the contacted surface absorb infrared radiation and characteristic vibrations of the inter-atomic bonds are excited in line with the selection rules discussed earlier. In contrast to the classical transmission technique, only a few microns of the surface of the material are involved in this process, not the whole material. Thus, even strongly absorbing samples can be characterized without difficulty, a fact which is very useful for all kinds of paints, and especially for waterborne paints that can’t be analysed by infrared transmission spectroscopy. The theoretical background for this method was described by Harrick in the 1960s. 2.7.1.1 Physical background The phenomenon of internal reflection of infrared radiation was first reported in 1959. It was observed that, in certain conditions, all of the infrared radiation entering a prism made of a high-refractive-index, infrared-transmitting material (ATR crystal) would be reflected internally when a critical angle is exceeded. This internal reflection creates an evanescent wave (see Figure II-10) which extends with a penetration depth dp beyond the surface of the crystal into the sample in contact with the crystal. The condition for obtaining total internal reflection is that the angle of the incident radiation θ must exceed the critical angle θc. The critical angle is a function of the refractive indices of the sample and the ATR crystal and is defined as: Θc = sin–1

n2 n1

Figure II-10: Internal and total internal reflection of an IR beam at the interface between ATR crystal and sample

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Surface infrared spectroscopy

29

where n1 is the refractive index of the ATR crystal and n2 is the refractive index of the sample. High-refractive-index materials are chosen for the ATR crystal in order that the critical angle may be minimized. One property of the evanescent wave which makes ATR a powerful technique is that the intensity of the wave decays exponentially with distance from the surface of the ATR crystal. The distance, which is in the order of microns, makes ATR generally insensitive to sample thickness, and so thick or strongly absorbing samples can be analysed.

Figure II-11: Evanescent infrared wave in the case of total reflection at the sample/ATR crystal interface

Figure II-12: Transmission and ATR-FT-IR spectra of a polyethylene layer

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Infrared spectroscopy

That portion of the totally reflected radiation which penetrates the uppermost layers of the sample is attenuated due to the absorption of energy by the chemical bonds of excited molecules. As this excitation of characteristic bond vibrations is comparable to the transmission experiment, the resulting spectra (after some corrections) can be compared with transmission spectra. 2.7.1.2 Depth of penetration As we have seen, the sampling region in ATR-FT-IR analysis is the uppermost layers of the sample, and not the whole sample. But how far does the infrared beam penetrate into the sample and which regions are analysed? For routine analytical purposes, it is very important to know the depth of the sample that is yielding the information displayed in the ATR spectrum. A useful relationship in ATR spectroscopy that can serve as a qualitative measure of the depth to which the evanescent wave extends into the sample is the depth of penetration dp. It is the distance from the crystalsample interface at which the intensity of the evanescent wave decays to 1/e (approximately 37 %) of its original value. Equation II-3:

z

E = E0e - dp

It is calculated by: Equation II-4:

dp =

λ1 –1 2π(sin²θ - n²21) 2

where λ l is the wavelength of infrared radiation, n1 is the refractive index of the ATR crystal, θ is the angle of incidence, and n21 is the ratio of the refractive indices of the sample and the ATR crystal. This penetration depth is a measure of the decay of the electromagnetic wave E as a function of the distance z between the interface of the ATR crystal and sample and can be described as: Equation II-5:

E=

E0 e

The penetration depth should not be confused with the real information depth of the method, which is in fact bigger. Nevertheless, the above-mentioned relationship (see Equation II-5) reveals the relationship between the information depth of a certain sample, the angle of incidence of the exciting infrared beam    the wavelength of the infrared radiation λl and the refractive indices of the ATR crystal and sample material. The following rules apply to the use of the ATR technique: The penetration depth of the infrared beam does not have a constant value. Dp increases when the angle of incidence decreases. Changes in the angle of incidence of the infrared radiation have two additional effects on the ATR spectrum of a sample. The first is that the

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Surface infrared spectroscopy

31

chosen angle of incidence must exceed the critical angle in order that an ATR spectrum may be obtained. The angle of incidence also has an effect on the number of reflections in the ATR crystal and that affects the infrared absorbance intensity of the spectrum. As the angle of incidence increases, the number of reflections decreases and the absorbance intensity decreases. Dp increases as n2 /n1 increases. Increasing the refractive index of the ATR crystal decreases the depth of penetration. This will decrease the effective path length and therefore decrease the absorbance intensity of the spectrum. In work involving samples which have a high refractive index, distortions in the infrared spectrum of the sample will be observed if the angle of incidence does not greatly exceed the critical angle. The refractive index affects the depth to which the evanescent wave penetrates into the sample. Dp increases in line with the wavelength. As a result, the relative band intensities in the ATR spectrum decrease with increasing wave numbers, compared with a transmission spectrum of the same sample. Since band positions and shapes are nearly identical for the ATR and the transmission spectra, many spectrometers have an ATR correction program which makes the ATR spectrum appear more like a transmission spectrum. As rule of thumb, we can say that the penetration depth is almost one quarter of the wavelength dp ~ ¼   ! 2.7.1.3 Information depth As described earlier, a clear distinction must be made between the penetration depth, which is an academic value, and the real information depth, which gives practical information about the depth of the sample where the information comes from. This is very important when it comes to the analysis of thin layers between a few hundred nanometres and 1 micron thick. As mentioned before, the electrical field decreases to 37 % of the initial value when the infrared beam reaches the penetration depth d p. In other words, more than 60 % of the information displayed in the infrared spectrum comes from a greater depth. Reference measurements on thin polymer layers have shown that the real information depth is about three times the penetration depth: ds ~ 3d p. 2.7.1.4 Effective path length A second useful relationship is the effective path length (EPL). The EPL can serve as an approximate comparison between the expected absorbance intensity of an ATR spectrum and a transmission spectrum. In an IR transmission spectrum, the path length is the thickness of the sample penetrated by the infrared beam. This can be determined very easily. The thickness (or path length) is directly related to the absorbance intensity by: Equation II-6:

T=

I = e – ad I0

where T is the transmission, α is the extinction coefficient, and d is the sample thickness

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Infrared spectroscopy

In attenuated total reflection infrared spectroscopy, it is more difficult to calculate the path length. In fact, an effective path length is calculated instead, as follows: EPL = penetration depth x = number of reflections. The EPL is also directly related to the absorbance intensity. An increase in either the depth of penetration or in the number of reflections will increase the absorbance intensity of the spectrum. The equation corresponding to Equation II-6 for the ATR measurement is Equation II-7:

R = e –αde

In Equation II-7, the thickness of the sample is replaced by the effective path length EPL or d e. It is defined as the thickness of a surface layer that causes the same absorption as a transmission measurement under rectangular conditions. The implications for an actual analysis are that very good and reproducible optical contact between sample and ATR crystal is essential for reproducible measurements. Since the evanescent wave decays very rapidly with distance from the surface, it is important for the sample to be in intimate contact with the crystal. This is easily achieved for most liquids because they wet the surface of the ATR crystal. For solids, it is important to use a pressure device which presses the sample against the crystal. For very hard materials, it is important not to damage the crystal while the pressure on it is being increased. The optical contact is influenced by the roughness of the sample surface and the pressure applied to the sample. For quantitative reproducibility, both parameters have to be the same if a certain number of samples are to be compared. We will see later how this can achieved. 2.7.1.5 Quantification The high sensitivity of ATR permits qualitative and quantitative studies of surface layers. Generally, where quantitative measurements are concerned, the same considerations apply to ATR spectra as to transmission spectra. The absorbance is replaced by the reflectance which is: Equation 2-8:

R = e – ade

R = 1 – ade

However, quantitative detection of substances by ATR-FT-IR is coupled to certain requirements. As described earlier, the parameters • refractive index of sample and ATR crystal • angle of incidence of the IR beam • wavelength • number of internal reflections • optical contact

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Surface infrared spectroscopy

33

strongly influence the spectrum and hence quantification. It is therefore essential to ensure that all these parameters are constant when a set of ATR spectra is being compared that has been obtained for calibration and quantification purposes. For example, the area or, preferably, the intensity of sample contact directly affects the intensity of the absorbance spectrum. If the whole crystal surface is not covered, the intensity of the absorbance spectrum will decrease. For maximum reproducibility in a series of measurements, the whole crystal surface should be covered by the sample. 2.7.1.6 Detection limit As for the sampling or information depth, the detection limit of ATR-FT-IR spectroscopy varies with the type of sample for the following reasons: • If substances have to be analysed that are embedded in a matrix or in the case of substrates and substrate surfaces whose thickness d >> dp , the effective path length d e is the key parameter affecting the detection limit. All parameters which cause an increase in the effective path length lead to a better detection limit. • If the thickness of the detected layer d 10 µm. As far as the paint industry is concerned, this means for example:

• trace analysis • analysis of paint craters Figure II-16: Example of a horizontal ATR unit equipped with a • identification of spots and diamond ATR crystal (open pressure device); tip of the scalpel   fibres in paint samples or on points towards the sample position   painted substrates • chemical characterization of sieving residue from a production line or from a paint shop • identification of unknown particles in paint powders, fillers and pigments • characterization of spots in paint films IM lets you see small samples through a light microscope and analyse microscopic domains by infrared spectroscopy. It is based on the physical principles of internal reflection (ATR microscopy) and external reflection and transmission.

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Infrared microscopy

37

2.8.1 Instrumentation The IR microscope must be a high-quality imaging system and capable at the same time of transmitting infrared radiation. Reflecting lenses are used to transmit the long wavelength infrared radiation needed for spectral analysis. Reflecting microscope objective lenses are not new; early microscopes used them to eliminate chromatic aberrations. Schwarzschild’s classic treatise on the design of reflecting objectives was written in 1907. The lenses manufactured today for IR microscopes are based on his definitive work. The most common IR microscope system comprises a microscope attached directly to a FT-IR spectrometer bench. IM requires a special microscope and spectrometer. Fourier transform infrared (FT-IR) spectrometers provide the high performance needed to power IM systems. In general, an infrared microscope is a light microscope with minor modifications. Switching from visible light as information carrier to infrared radiation rules out the use of glass lenses because glass absorbs infrared radiation. Therefore, mirror optics or “caissegrain” optics is used to focus and direct both the infrared and the visible light.

Figure II.17: Ray traces for mirror optics used in infrared microscopes (transmission mode (left) and reflection mode (right))

These “cassegrain” optics focuses the visible and infrared light onto the sample and permit an easy change-over from visual inspection to infrared analysis. Lateral resolution is limited by the laws of optical refraction and is about >10 µm. The infrared microscope is normally coupled to a standard infrared spectrometer and the IR beam is coupled to the infrared microscope by several mirrors. After the IR beam has hit the sample, it is analysed by an MCT

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Figure II-18: Example of an infrared microscope (source: Bruker GmbH)

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Infrared spectroscopy

detector cooled with liquid nitrogen. Standard DTGS detectors are not sensitive enough for infrared microscopy. Before measurement commences, the MCT detector has to be cooled down in order that its own thermal background noise may be eliminated. That is one of the reasons why infrared microscopy in a commercial routine laboratory only makes sense if there are numerous samples to be analysed during one session. For only one sample per day, preparation takes too long.

2.8.2 Infrared microscopy, infrared transmission mode One of the operating modes of the IM is that of transmission. The infrared beam is focused onto the microscopic sample area, which it penetrates. The transmitted (and attenuated) beam is directed by the mirrors towards the detector (see Figure II-17). For paint analyses, this mode is not very popular since infrared radiation is strongly absorbed by most paint samples. Before a sample can be analysed in transmission mode, a few requirements must be met. The sample must • transmit IR radiation • be a weak absorber, and • be very thin. If even just one of these requirements is not met, it is impossible to obtain a spectrum. A typical application is the analysis of a microtome cross-section of painted polymer samples for the purpose of identifying small spots or unknown particles.

2.8.3 Infrared microscopy, reflection mode This mode is used for inspecting thin layers or small particles on metallic or even infrared reflecting substrates. Again, the analysed layer must not be a strong absorber and must not be too thick. The physical phenomenon behind this is external reflection of infrared light by a metallic surface. The incoming and the outgoing IR beams are perpendicular to the surface. The measuring process starts with a reference analysis of the uncoated surface. This serves as the background

Figure II-19: Ray traces during infrared microscopic inspection of a thin layer A, a micro particle B, and an inclusion in a surface layer or coating on an infrared reflecting substrate C

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Infrared microscopy

39

spectrum which is familiar to us from the standard infrared transmission experiment. If, for example, a clearcoat layer on an aluminium panel is to be analysed so as to act as reference measurement, a hidden surface area should be used which is not coated. After this, a reference measurement serves as a background to eliminate influences of the substrate itself. The next step is to analyse the object of interest, which can be masked by rectangular slits placed in the infrared beam. This will help to minimize the influence of the surrounding matrix. The spectra resulting from the Fourier transform of the reference (background) and sample spectrum are sometimes hard to interpret. Multiple reflections in multi-layer systems, and scattering and refraction are factors that can alter the spectrum and thus necessitate the services of an experienced spectroscopist to distinguish between these effects and the spectrum of the sample. Let’s take as an example a small particle on a metallic substrate. The infrared beam is focused on that spot, the beam penetrates the particle, passes through it, is reflected at the metallic surface, passes again through the particle and, on leaving it, is focused onto the detector by several mirrors. To an extent depending on the type, shape and size of the particle, the infrared beam will undergo total reflection, refraction or scattering. This will alter the spectrum so much that a great deal of experience is needed for filtering the desired information out of the peak distortions.

2.8.4 Infrared microscopy, ATR mode Whereas the above-mentioned techniques of infrared microscopy only apply to sample areas which do not absorb very strongly and which are transparent to infrared light, this does not apply to ATR microscopy. This special technique is as versatile as H-ATR and is not affected by the thickness of the sampling area and can even accommodate strong absorbers. Its limitations are the same as those of H-ATR. For ATR microscopy, a special lens replaces the cassegrain transmission optics of the instrument. It consists of a very small ATR crystal that makes contact with the sample surface in the manner of a needle for vinyl phonographs. The infrared beam is focused onto the crystal, which directs the radiation only to the point of sample contact. The contact point is only a few micrometers in size. Therefore, no optical effects can influence the spectrum and there is no sample preparation needed in most cases. That is why this method is as versatile as H-ATR but is also ideal for spots which are much smaller. Typical applications are: • spot and crater analysis of all paints and coatings • particle analysis even when water is present. • identification of fibres Sample preparation Analysing traces of contamination is a major IM application. When contaminants are found in a raw material or a formulated paint, the product is rejected. Identifying the contamination often entails separating the fine particles first. Gold-coated Nuclepore filters afford a simple and direct method of separating particles. Any fine particles on the gold surface of these “mirrors” can be analysed by IM.

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Infrared spectroscopy

The infrared radiation focused on the particle will pass through it, reflect off the gold and return through the particle to the spectrometer. Infrared spectral analysis of these contaminants is direct, and requires no additional sample handling or manipulation. Combining this special gold-coated filter with IRM provides a rapid means of separating and identifying contaminants.

2.9 Data evaluation Nowadays, instrumentation for performing all kinds of analysis, and especially for infrared analysis, is easy to handle. It is easy to produce a spectrum after brief instruction and without any knowledge of the underlying principles. On the other side of the coin, it’s easy to get the wrong result. Correct interpretation and evaluation of spectra requires fundamental understanding and experience. The spectrometer industry provides customers with plenty of computer assistance and software that surports to do the job. But, in the end, an experienced spectroscopist is needed. The easier automatic interpretation by software tools seems to be, the more likely it is that the result is wrong. But what is the right way to properly evaluate a spectrum? There are several. The first one is to generate a table of all peaks and correlate it with peak tables that are available in book form or computerised format. This peak table can normally be generated automatically by a software tool. Comparison with the acquired spectrum of the sample leads to the identification of major functional groups, such as esters, acrylates, urethanes, hydrocarbons, and so on. This is actually quite general information that won’t lead very far in most cases. Peak assignments are quite often very ambiguous. For example, polyesters and acrylates share several characteristic peaks although they are different as far as their chemical backbone is concerned. To learn more from the spectra, it is necessary to compare the data to databases of infrared spectra either bought from a supplier or collected by you.

2.9.1 Use of databases Knowledge of how databases work is helpful for using them in an evaluation. However, a database, even if supplied with elaborate software, cannot do everything. It is simply a collection of spectra that someone else has measured and collated at a certain time. These spectra have been rendered searchable by means of digitization. Therefore, they are nothing more than digits in a data file. The search software uses a mathematical algorithm to compare the digitized data of the reference spectrum with the digitized data of the unknown spectrum. This might, for example, take the form of a comparison of peak intensity via the correlation coefficient of a linear regression. The result of this mathematical operation is a probability that the unknown spectrum has something to do with the saved reference spectrum. It is very important to realize that this mathematical result has nothing to do with the

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Data evaluation

41

actual chemical composition of the sample. This is a common source of confusion because it’s easier to present a list of probabilities than to think about the problem and analyse the probability that the mathematical result is the desired answer. The computer-generated hit list displays the degree of mathematical correspondence between the saved spectra and your data. So, what you get from computer analysis is a hit list of probabilities that has to be checked thoroughly for plausibility. All available information about the sample therefore must be taken into account. This includes the following: • Is the sample a liquid, a solid, a paste or a powder? • What circumstances led to the problem? • Is the sample a mixture or a pure substance?

Figure II-20: Example of a database search: sample spectrum showing a polyester polyurethane (above) and the database results derived from a commercial database below

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Infrared spectroscopy

A chemical background is therefore needed for an understanding of the spectra. If, for example, a waterborne binder has to be analysed by H-ATR and it is analysed as received, more than 90 % of the ATR-FT-IR spectrum will be dominated by the characteristic peaks of water. The database will tell you that there is a 90 % probability that the sample is water. An inexperienced spectroscopist might therefore conclude that this is the wrong sample unless he knows that he has to wait until the water dries off before the polymer itself is measured. Figure II-20 shows an example of a 2-pack polyurethane paint. The commercially available database yields a list of suggestions. The first three of these are shown in Figure II-20. The “best” search result from the standpoint of mathematical compliance shows the spectrum of a vinylidene chloride polymer, which is completely misleading. The spectrum of a compound consisting of polyurethane is ranked only as second best and even the third proposal has nothing to do with the sample’s real chemical composition. This, of course, is a dreadful outcome and, was the result of such research to be published, would cause a furore. This demonstrates that a computer which is unable to evaluate the search result is no substitute for a reasonable amount of experience. A second problem with commercial databases is their “expiry date”. As quickly as commercially available products or raw materials are measured and stored in the database, they are either taken off the market just as the database is published or their brand name is changed. This means that a search result is useless for identifying an unknown compound. Companies that produce the spectra often take purchased products from the market, measure the ATR spectrum and save it in the database under the brand name without knowing anything about the chemical composition. For practical use in a production laboratory, it might therefore be more useful to create a database of the products and raw materials used in the process rather than to buy one.

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Physical background

3

43

Time-of-flight secondary ion mass spectrometry

The physical principle behind secondary ion mass spectrometry (SIMS) dates back to 1910. It is based on the fact that the impact of charged particles, such as ions, on a surface causes the emission of secondary particles. The exciting beam consists of high-energy noble gas or liquid metal ions. The emitted secondary ions resulting from the sputtering process, which will be described later, descend from the uppermost layers of the sample surface. If the density of the primary ion dose (PIDD) is high enough, continuous sputtering of the surface can be observed. This mode is called “Dynamic SIMS”. As the secondary ions resulting from this process are small fragments of the chemical substances present on the surface, this is a destructive form of analysis. All organic compounds are broken into small pieces that are not very characteristic. For paint and polymers, it is more useful to analyse the surface without destroying it. This can be achieved by choosing a PIDD that is low enough to preserve the original surface and using only a small part of one monolayer for the characterization. Only a small fraction of 10,000 and a low detection limit that can be as low as femtomoles of some substances.

3.2 Instrumentation The TOF-SIMS technique, like SEM and XPS, requires a vacuum. The sample is placed in a vacuum chamber where the volatile compounds are pumped off before the sample is transferred to the central ultra-high-vacuum chamber. A built-in primary ion gun produces the high-energy, pulsed excitation beam of positively charged ions (Ar+, Au+). The primary ion beam is focused and pulsed onto the surface of the sample. This excitation causes energy to be transferred from the primary ion into the surface of the material that has to be analysed. The resulting collision cascade leads to the emission of secondary ions that have to be separated by mass. This is done with a time-of-flight analyser. In other words, the secondary ions are separated by the flight time needed to cover a given flight distance. They are drawn from the cloud above the surface by an extractor, which gives them all the same energy. Ultra-short pulses of the primary ion beam define the starting point for measuring the flight time until

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Time-of-flight secondary ion mass spectrometry

they arrive at the detector. The detector records all the ions and transfers the signal to specially designed software that displays the result as a mass spectrum which shows the intensity (counts/channel) for each secondary ion mass.

3.3 Sample       preparation In general, the TOF-SIMS technique needs little sample preparation. The geometrical design of the instrument’s sample holder imposes certain requirements on height and length. Thus, big samples need to be trimmed somewhat to the desired size. Apart from these geometrical restrictions, there are few limiFigure II-24: TOF-SIMS spectrometer tations on the samples. Almost any material that can withstand a vacuum can be analysed without any further sample preparation. Dried paint layers, pigments, fillers and all other solids are analysed directly. Powders need to be affixed to a special tape. Liquid samples are prepared as thin films on a neutral, clean target, such as aluminium. The latter might be a surprising choice because TOF-SIMS was originally a surface method and not intended to be used for bulk analysis. And it should be mentioned that liquid samples tend to emit volatile compounds which will affect the vacuum system. In fact, special preparation methods have been developed over recent years to extend TOF-SIMS to liquid samples as well as solids. This makes it a powerful tool that can even replace GC-MS sometimes. For the analysis of liquids, a trick is employed. Whereas surface contaminants are normally detected and characterized by TOF-SIMS, liquids are analysed by “contaminating” a clean surface of a neutral target, such as aluminium, with a thin layer of the substance to be analysed (e.g. a binder). An ultra-thin layer of the diluted substance is applied to the cleaned surface of the target, e.g. by spin-coating. This so-called monolayer preparation enables relatively non-volatile liquids to be analysed. It can’t be used for highly volatile liquids, such as some paint solvents.

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47

3.4 Spectral evaluation What kind of information does TOF-SIMS provide and how can it be accessed? As described earlier, the sputtered species can be divided into specific groups. First, they are either negatively or positively charged ions. The spectra of both species always belong together and complement each other. One group of SIs is called quasi-molecular ions. These are molecules which are charged by the addition of a proton, alkali ions (Na+, K+), silver ions (Ag+) or other metal ions or by the loss of small molecules, such as OH. These molecular ions are very useful because they enable the whole molecule to be detected and thus the molecular weight to be determined. Negatively charged molecules derived from the loss of OH from the molecule are called molecular ions as well, although they are not complete. Another group of ions, called fragment ions, originates from fragmentation of the molecules due to the impact of PIs. They tell us a great deal about the chemical composition of the molecules. As well as in the dynamic mode (see dynamic SIMS), element ions are always present in the spectrum. In addition, there are meta-stable ions which exhibit broad peaks of poor mass resolution. They cannot be used to identify substances. Thus, plenty of information is contained in just one spectrum (or two, if the spectrum of the negative secondary ions is included). A great deal of work has been done in recent years on showing that the SIMS spectrum is influenced by ionization and by intermolecular and intramolecular forces. One important aspect is the correlation between the secondary ion yield and the chemical composition of the sample. This is called the “matrix effect”. The degree of oxidation, for example, has a major influence on the mass spectrum. Therefore, sample preparation, in as far as it is necessary, influences the outcome of the analysis. If a liquid is prepared as a thin layer on a metal substrate, the quantity of secondary ions depends on the metal used as target. The best results will be obtained when the substance is prepared as a monolayer on a gold or silver surface. These metals cause substrate cationization, which enables neutral secondary ions to be detected as (M+Ag)+ or (M+Au)+. With polymers, it has been found that the nature of the secondary ions varies with the thickness of the analysed layer. Whereas

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Figure II-25: Mass spectrum of the positive SIs of polydimethyl­ siloxane

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48

Time-of-flight secondary ion mass spectrometry

monolayer preparation on a noble metal surface leads to the formation of fragment ions with a mass 5) are detectable simultaneously Figure III-11 shows the SEM-BSE image of a filter residue. The grey scale image contrast reveals the different chemical composition of each particle. Lighter areas consist of elements that produce a high amount of back-scattered electrons, that is to say, they consist of heavier elements, such as barium, titanium and iron. This means that lighter areas are indicative of fillers, pigments or contaminants made up of heavier elements. Darker areas consist of substances made up of lighter elements, such as carbon, oxygen, and aluminium. A binder made from carbon, oxygen and hydrogen, for example, appears completely dark in the BSE image. So, this grey scale BSE image of the filter residue reveals that the sample is an inhomogeneous mixture of inorganic (e.g. fillers) and organic components (binders, additives or organic contaminants). Of course, the image contrast does not reveal the exact composition. This has to be explored by subsequent EDX analysis at different points selected in the BSE image. The BSE image, therefore, provides orientation as to where the spot analysis should be conducted and represents a map of the sampled area. Figure III-11: SEM-BSE image of a filter residue

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cps O 30 Sl 20 Al

10

Ti

Ca

Cl

Mg Zn Zn

S

Ti K

Ca

Fe

Ti

Zn

0 2

4

6

Zn

8 Energy (keV)

Figure III-12: EDX overview spectrum of the filter residue (see Figure III-11)

cps Ca

250

150

100

O

50 Ca Ca 0 2

4

6

8 Energy (keV)

Figure III-13: EDX spectrum of area 2 (see Figure III-11 and 12) cps O

100

Si Fe

50

Al

Fe

CI S

Ca

Ti

Fe

Ca

0 2

4

6

8 Energy (keV)

Figure III-14: EDX spectrum of area 3 (see Figure III-11 and 12)

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O

150

Si

Mg

100

Al

50 Fe

Cl

Ca

S

Ca Ti

Ca

Fe

0 2

4

6

Fe 8 Energy (keV)

Figure III-15: EDX spectrum of area 4 (see Figure III-11 and 12)

Figure III-12 is an overview EDX spectrum showing the integral element composition of the whole sample area displayed in Figure III-11. The following elements have been detected: • Oxygen, zirconium, zinc, magnesium, aluminium, silicon, sulphur, chlorine, potassium, calcium, titanium, iron Following acquisition of this overview spectrum, selected particles were analysed by a spot analysis consisting in focusing the electron beam onto a small area and reducing the acceleration voltage of the probing beam (see Figure III-13 to Figure III-15). Spot analysis of the particle in area 4 reveals that this material consists of oxygen, magnesium, aluminium and silicon. This is a typical composition of alumosilicates and talc. Particle 2 (area 2) consists of calcium, oxygen and carbon, and thus indicates the presence of calcium carbonate, which is a typical filler, too. However, it must be mentioned that, although we have made conclusions about the chemical composition, EDX elemental analysis does not provide information about the chemical environment of an atom. Therefore, EDX cannot serve in the detection of chemical compounds. If, for example, paint consists of several different silicon compounds, such as siloxane additives and silicate fillers, EDX analysis is unable to distinguish between them. For this purpose, additional FT-IR micro-analysis is needed.

2.1.2 FT-IR analysis of filter residues In general, there are two different procedures for subjecting filter residues to FT-IR analysis. The easiest consists in performing an ATR-FT-IR measurement on a diamond single-reflection element (e.g. “The Golden Gate”). The first step might even be just to place all the filter residues on the ATR crystal if there is enough material on the screen. Although the contact area is a few square millimetres, smaller particles or fibres can be placed on the diamond even if they do not cover the whole diamond surface. This is just a question of micro-preparation experience and is easy to accomplish after a few trials.

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Figure III-16: ATR-FT-IR spectrum of paste- like filter residues compared with binder reference samples

Thus, ATR-FT-IR is a versatile tool that yields an overview of the chemical composition of a filter residue. For this kind of ATR-FT-IR measurement (or “micro ATR-FT-IR”), it is not important if the filter residue • contains aggressive solvent residues, • is wet or dry, • contains strong absorber (such as a dark-coloured paint), • is sticky, liquid, solid or powdery. Solvent residues can be detected simultaneously, the pretreatment of the samples is unnecessary, even very little quantities of substance can reveal an informative spectrum. It is a quick method that delivers much information in a short time and it can be done almost everywhere (if necessary, directly at the production site). Figure III-16 shows an ATR-FT-IR spectrum of a paste-like filter residue. The most prominent features of this spectrum are characteristic signals of polyester binders. Comparison of the sample spectrum with database spectra for binders used in this production plant reveals that the main components of this filter residue are agglomerations of two binders. More than that, the spectrum exhibits some additional peaks from at least one further compound. From the spectrum, it can be concluded that this must be a carbonyl compound. A detailed characterisation of this component is not possible because most of the characteristic peaks are overlaid with the strong peaks from the main (binder) compounds. Where chemical separation of the filter residue is not possible, sometimes there is a “digital solution” that can reveal the features of these additional components. If the main compounds of the spectrum

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have been identified unambiguously and reference spectra acquired under the same spectral parameters are available, spectral subtraction can help to extract the desired spectrum of the minor compounds from the sample spectrum. However, such digital separation requires a great deal of spectroscopic experience if misleading artefacts are to be ruled out. The resulting differential spectrum can be compared with database spectra again, but allowance must be made for the fact that the mathematical procedure of spectral subtraction can cause peak distortions and intensity changes. Consequently, evaluation of this difference spectrum must be performed even more carefully than that of the root spectrum. Nevertheless, the digital option is very helpful when preparation of the sample is not an option. The previous example concerned a filter residue consisting of just three components. If the residues are more complex, the procedure just described may cause problems. If the sample residue, for example, is composed of a mixture of several silicates, fillers, matting additives and residues of binders, the resulting spectrum could never be deconvoluted by spectral subtraction. Either the filter residue must be separated by micro-preparative techniques or selective particle analysis must be performed with ATR microscopy. As an ATR microscope is equipped with a very small diamond tip that contacts the sample, it can be used for spot analysis of small areas of diameters >15 µm. Thus, even micron-sized particles or fibres in a sample residue can be characterised selectively. The absolute detection limit is a few nanograms or micrograms, depending on the type of sample. On the other hand, ATR microscopy is not a trace-analysis technique. For trace analysis of a sample residue, which is not very common, TOF-SIMS must be chosen.

2.2 Analysis of fogging residues Environmental and consumer safety reasons have boosted the importance of analysis and detection of gaseous emissions from paints and paint ingredients. In particular, the automotive supplier industry is very sensitive about emissions from paints and polymers. All organic substances and, especially, paints and polymers emit gaseous compounds to a certain extent. This is called “fogging”. This fogging can consist of • solvent residues • monomers and oligomers of the polymer or binder • additives • reaction products and by-products of the paint drying process The quantity of volatile compounds emitted by a solid material depends on the • temperature • age of the material • production process From a chemical point of view, it is almost impossible to produce a “zero emissions” polymer. The question is not if there are any emissions but rather how many emissions can be expected from a material and (more important) what kind of emissions does the consumer

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have to face. Evaluation of fogging with respect to smell, nuisance and health risks requires chemical characterization of the components. If the level of fogging reaches a critical value, the analysis must be extended to the raw material and intermediate compounds. Expected emissions from paint, polymer part or raw material are simulated by treating the sample as follows: The material is placed in a glass container, which is then Figure III-17: Optical light microscopy image of fogging by a binder closed and heated for a certain time to a specified temperature (e.g. 80 °C). The glass container is closed with a glass plate, which is cooled during heating. The gaseous compounds emitted by the sample precipitate on the surface of this glass plate. The film of precipitated substances is analysed photometrically or gravimetrically at regular intervals. The result is a percentage or microgram value which shows the expected degree of fogging from this material. What these analytical data do not tell us is the composition of the emissions, knowledge of which is necessary for any evaluation of possible hazards to consumer health and the environment. The infrared spectroscopic and mass spectroscopic analysis of the precipitated film yields the information about the components and serves as a basis for certifying the analysed material in respect of safety data.

Figure III-18: TOF-SIMS spectrum of a fogging film emitted by a painted automotive polymer panel compared with a reference spectrum of a binder used for the production of the paint

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Figure III-19: ATR-FT-IR spectrum of a fogging film emitted from a binder compared with a database spectrum of sodium lactate

The TOF-SIMS spectrum of fogging from a painted automotive polymer panel shown in Figure III-18 is compared with a reference TOF-SIMS spectrum of the binder which was used in the making of the paint. This mass-spectroscopic comparison shows that the main component of the precipitation is a trimethylol-caprylic acid ester, which is a major ingredient of the binder. Thus, the fogging can be unambiguously attributed to a raw material. Armed with this knowledge, it is possible to reduce or eliminate the fogging problem by changing the recipe or pretreatment of the binder. Detailed studies of the fogging problem with the aid of modern analytical techniques thus lead to improved quality and that benefits the paint manufacturer, the consumer and the environment. The same is true of raw materials which have been contaminated by unexpected by-products due to production problems or transportation in a contaminated container. As well as TOFSIMS, ATR-FT-IR can be useful for identifying fogging emissions. This is demonstrated in Figure III-19, where a volatile contaminant emitted by a binder at 90 °C has been identified as sodium lactate.

2.3 Investigation of the degree of crosslinking in 2-pack paints Let’s now turn our attention from paint production to paint application, where there are some issues which have been investigated successfully by means of probing analytical techniques. One of those aspects is the degree of crosslinking in 2-pack polyurethane paints. Although the dosage of masterbatch and hardener in modern paint plants is very precise, malfunctions

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Figure III-20: Comparison of ATR-FT-IR spectra of 2-pack polyester-polyurethane paint films of different binder/ hardener ratio

and human error sometimes lead to a wrong mixing ratio. One goal of failure analysis is to verify and prove the binder/hardener ratio of the paint film. This has been done successfully with ATR-FT-IR. The paint film is pressed onto the ATR crystal and measured without any further sample preparation. As Figure III-20 shows, there is a relationship between the intensity of the characteristic peaks of the isocyanate hardener (marked by an arrow) and the ratio of masterbatch/binder and hardener. The relationship is linear if all the parameters of the measurement (optical contact, pressure, coverage of the ATR crystal surface) are identical. This is a prerequisite for quantitative determination of the binder/hardener ratio by measuring the intensity of the peak intensity of hardener and binder (see Figure III-21).

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The procedure is as follows: 1.  Prepare a set of paint samples of different masterbatch/hardener ratios. 2.  After the paint has dried, obtain the ATR-FT-IR spectra of these reference samples. 3.  Measure the intensity of one characteristic peak of the binder (e.g. the carbonyl stretching vibration of polyester) and the hardener. The majority of instrument suppliers offer a software module for this digital operation. The intensity can be determined via the peak height or the peak area. As the peaks of the carbonyl stretching vibrations of binder and hardener overlap, determining the peak height is the method of choice for this task. 4.  Divide the measured intensity of the selected characteristic vibrations of the binder by the intensity of the corresponding peak of the hardener to obtain a relative peak intensity value: Equation III-1:

I rel =

Ib Ih

where I b is the intensity of the C=O stretching vibration of the binder, Ih is the intensity of the C=O stretching vibration of the hardener and I rel is the relative peak intensity 5.  For each spectrum of a reference paint sample produced with a given binder/hardener ratio, the result is a relative peak intensity value which is displayed in a diagram of I rel vs binder/hardener ratio (see Figure III-21). 6.  From these readings, mathematical software calculates a linear regression line which serves as a calibration curve for determining the binder/hardener ratio of an unknown sample. 7.  After this calibration process, determine the binder/hardener ratio of each unknown sample by measuring I rel of the sample and reading the corresponding value off the calibration line. During acquisition of the sample spectrum, ensure that the same measuring parameters apply as for the reference samples. Example Evaluation of the intensities of the characteristic binder and hardener peaks of 2-pack polyester-polyurethane paint yields the values shown in Table III-1. These data yield rel. peak intensity I rel of 1.182. Taking this value, a binder/hardener ratio of 100 : 7.2 can be read off the calibration line. Table III-1: Allocation of evaluated peak and peak intensity of IR spectroscopy Wavenumber of evaluated peak

Peak assignment

Peak intensity [peak height]

1725 cm-1

ν, C=O stretching vibration of polyester binder

0.31

1685 cm-1

ν, C=O stretching vibration of isocyanate hardener

0.26

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Figure III-21: Calibration line displaying the rel. peak intensities Irel vs. binder/hardener ratio for 2-pack polyester-polyurethane paint

As the rel. peak intensity I rel does not change during paint drying if the paint components are non-volatile, this method can be used to determine the binder/hardener ratio with an accuracy that is sufficient for technical purposes. From the viewpoint of chemometric analysis, the method is not precise enough but it has worked very well for those technical applications which don’t need that level of precision. It has to be mentioned that the values of the binder/hardener ratio reveal nothing about the real degree of crosslinking. If, for example, inhibition of reactivity has occurred because a catalytic additive was forgotten, this is not apparent from the infrared spectrum. Where reaction inhibition of binder and hardener is suspected, some additional preparative operations can help to find out. First, a spectrum of the paint layer is acquired and the rel. peak intensities are determined as described above. Then, the paint layer is extracted with ethanol or propanol to wash out the fraction of unreacted binder. After drying, the paint layer is measured again and the resultant binder/hardener ratio after rinsing is expressed in terms of the value determined before rinsing. This procedure yields the amount of binder that has actually reacted with the hardener.

2.4 Investigation of paint additive migration Paint formulations feature a lot of additives which are included to enhance the performance of the paint with respect to appearance, adhesion, wetting etc. As these additives don’t take part in the crosslinking process, they are mobile and tend to migrate within the paint system.

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Some of them can be found at the surface, whereas others will accumulate at the interface of the paint and substrate or between two paint layers. In a multi-layer system, additives can migrate between two, three or more paint layers. A detailed knowledge of the distribution of an additive can thus help to improve the technical properties of the polymer coating. For example, a light-stabilizer additive incorporated into a clearcoat unfolds its greatest effect if it is homogeneously distributed in the paint layer, whereas surface-flow-control compounds in waterborne systems form self-assembling, highly ordered structures in the uppermost paint layer and could prove problematic for repair coats applied as a second layer. In all these cases, the main question is: How does an additive migrate and distribute itself in a coating layer? What do we need to know in order to be able to answer this question? a.  The chosen analytical technique must yield molecular chemical information for unequivocal identification of the additive. b.  The distribution has to be examined along a cross-section of a single-layer coating or a multi-layer coating. c.  Given that the absolute quantity of an additive in a coating layer is very low, the chosen technique for detecting and identifying it must have very high sensitivity. Therefore, what we need is a molecular-depth profile of an organic molecule in an organic (multi-layer) system. This seems to be a huge, if not impossible, challenge. Depth-profiling methods utilise a sputtering beam that removes one layer at a time, but have the great disadvantage that the sputtering process destroys organic substances. On the other hand, non-destructive ablation is a technical contradiction. Yet non-sputtering techniques have been developed that show the distribution of certain organic substances in a multi-layer system. One is TOF-SIMS imaging, as demonstrated in Figure III-22. This figure shows a TOF-SIMS image of the distribution of a siloxane additive in a multi-layer system. First, a microtome section or a metallographic cross-section through the paint system has to be performed. After this mechanical preparation step, the area of interest in the cross-section is scanned by a micro-focused pulsed ion beam. A mass spectrum is collected at every pixel point of a, say, 300 x 300 pixel array. Once this collec-

Figure III-22: Pos. TOF-SIMS image of the distribution of a polysiloxane additive in a multi-layer system

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tion of mass spectra has been stored, an image is generated by extracting the characteristic peak(s) of the substance to be analysed. In this example, the characteristic mass peaks of a siloxane additive (which are 73 u, 147 u, 207 u, 221 u, 281 u, corresponding to fragments of the siloxane network) have been removed from the overall data set to generate images of their intensity over the scanned area. All five images of the intensity distribution for masses 73  u, Figure III-23: Arrangement of analysis spots along a wedge cut of 147 u, 207 u, 221 u and 281 u a multi-layer paint system (schematic side view) in Figure III-22 display the distribution of the polysiloxane additive over the scanned area of the cross-section and thus help to detect migration and segregation processes. The second technique is that of TOF-SIMS analysis of a wedge cut of the paint system. Again mechanical preparation consists in making a microtome section. However, this cut does not slice rectangularly through the layer(s) but rather at a very small angle nearly parallel to the paint surface. The result is a cross-cut area which presents the whole layer system from the paint surface down to the substrate. This cutting zone is readily accessible to surface analytical methods and thus can be analysed by TOF-SIMS. The test procedure is as follows: Starting on the paint surface, a set of spot analyses is performed along a line that crosses the paint layers down to the substrate as shown in Figure III-23. The analysis spot must be equidistant, to allow for the size of the cutting zone, to make sure that there are enough points for each layer. Each spot analysis returns a full set of information about the paint composition at that specific point. The full data set of spot analyses lined up from the paint surface to the

Figure III-24: Arrangement of analysis spots along a wedge cut of a multi-layer paint system (schematic plan view)

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Figure III-25: Analysis result: Distribution of different paint components across the cut through a multi-layer paint system as shown in Figure III-23

substrate offers a complete image of the distribution of paint components over the paint layer system. From this mass spectroscopic data set, characteristic peaks for a certain additive can be extracted. The intensity of these characteristic peaks is evaluated by integrating the peak area and normalising it to the peak area of an internal reference substance. The concentration or peak intensity of this reference substance must be independent of the quantity of additive. As a rule, good results are achieved by taking the sum of integral intensities of hydrocarbons as an internal reference. The outcome is that a normalized intensity for this additive is achieved for each spot on the cutting zone. The analytical result, as shown in Figure III-25, is an image of the distribution of several additives along a virtual cut through the paint system.

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Adhesion and wetting problems

3

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Investigation of paint failures

The main application of surface analytical methods is without doubt that of failure analysis. Types of paint failure are spots, adhesion, wetting and flow problems, craters and stains. Very often, this issue demands the detection and identification of very low quantities of paint components and contaminants in a small sample area (micro-analysis). For these tasks, surface analysis provides an extensive set of instruments:

3.1 Adhesion and wetting problems There are many reasons for adhesion and wetting failure between paint and substrate. Some of them are caused by the substrate itself. Processing of the material to be painted afterwards can leave residues of processing aids, release agents, oils, cleaning agents etc. on the surface. If not removed properly, these may lead to a variety of failures, such as cratering, and adhesion and wetting problems. Table III-2: Application of surface analytical methods in paint failure analysis Defect

Possible cause

Method of investigation

spots

inclusion of contaminants

SEM/EDX infrared microscopy

adhesion failures

wrong mixing ratio of binder and hardener

IR (ATR, transmission)

contamination by release agents

TOF-SIMS

oil, grease or cleaner residues

TOF-SIMS

wetting problems

oil, grease or cleaner residues

TOF-SIMS

paint craters

gel particles

paint blisters

stains and residues on paint surfaces

REM/EDX IR microscopy Micro-ATR

Oil, grease or cleaner residues

TOF-SIMS

Aerosols

TOF-SIMS

degassing of substrate cracks

metallographic cross-section optical light microscopy SEM

residues of salts on the substrate

SEM/EDX

adhesive water

-

application failure

-

migration of paint components to the surface

ATR-FT-IR

external contaminants

TOF-SIMS

Roger Dietrich: Paint Analysis © Copyright 2009 by Vincentz Network, Hannover, Germany

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As far as large-scale delamination between paint and substrate or between two paint layers is concerned, the main task is to characterise the surface or interface in terms of chemical composition.

3.1.1 Paint delamination caused by surface contaminants A sub-monolayer of, say, a perfluorinated polyether, which is a very common high performance grease left on the surface of a metal or polymer substrate, will cause severe adhesion failure. To detect this trace quantity of contaminant you need an analytical technique which combines: • very low detection limit with • very high surface sensitivity, and • molecular information

Figure III-26: Different types of paint adhesion problems

It is not sufficient to perform elemental analysis of the surface or interface by, for example, Auger electron spectroscopy (AES) or EDX. Besides the lack of detection sensitivity, the information that there is fluorine contamination on the surface does not allow the contaminant to be identified unambiguously. The fluorine may be a fluoride, a fluorosurfactant or a fluorinated polymer. That makes a big difference as far as adhesion problems are concerned. For the following reasons, then, elemental analysis is not the method of choice when it comes to adhesion failures: • insufficient detection sensitivity • no molecular information

Figure III-27: Coated aluminium showing adhesion failure

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Nor is ATR-FT-IR surface analysis the best choice. ATR-FT-IR delivers molecular information but is not sensitive enough to detect mono-molecular contaminant layers.

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Figure III-28: Detail of a positive TOF-SIMS spectrum of an aluminium surface exhibiting paint adhesion failure, compared with a reference sample without adhesion problems. (Selected mass range 176 u to 290 u; → detection of a mono-molecular contaminant layer of polydimethylsiloxane)

In order that these very low levels may be detected, it is crucial for the technique employed to offer molecular information, high surface sensitivity and very good detection sensitivity. These demands are met by X-ray photoelectron spectroscopy and TOF-SIMS. Figure III-28 shows an extract from a positive TOF-SIMS spectrum of an aluminium surface that exhibits paint adhesion failure, compared with a reference sample without adhesion problems. The TOF-SIMS spectrum of the metal surface beneath the delaminated paint contains characteristic fragments of a polydimethylsiloxane. On the surface of the reference sample, no polydimethylsiloxanes can be detected. The conclusion, therefore, is that the aluminium surface was contaminated just before the painting process. This raises the question as to the source of the contaminants. Indeed, there many possible reasons: • contamination of the raw material • improper transportation • failure in storage • residues from substrate-treatment operations • aerosol precipitation due to ventilation systems in the production plant • residues of substrate handling A detailed, critical and comprehensive review of the production process for possible contamination sources must start by auditing production of the raw material, followed by the storage and transportation conditions, and subsequent processing. This does not mean simply checking the documentation but rather stepping into the production line to take a close look at the processing steps. This preliminary review of the production process and reporting of potential risks is followed by surface analysis helps to confirm the facts. Figure III-29 shows how this procedure might be applied to the above-mentioned aluminium sample. On the metal surface beneath the delaminated paint, a polydimethylsiloxane contaminant was detected. A critical review of

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Figure III-29: Cross-section of a pos. TOF-SIMS spectrum of an aluminium surface after a cleaning process, the cleaning agent, an aerosol target and a glove used for handling (selected mass area 125 u to 264 u; → detection of polydimethylsiloxane on the surface of the disposable glove; red arrow)

the whole process revealed a few potential sources for this contaminant. These areas were sampled and subjected to TOF-SIMS analysis. The following samples were investigated: • an aluminium panel taken from the production line after a cleaning procedure but before the painting process. • a sample of the cleaning agent used to treat the aluminium surface • a so-called aerosol target which is meant to detect precipitation by aerosols contaminating the air inside the production plant. • a piece of a glove used to handle the aluminium This set of reference analyses clearly shows that the polydimethylsiloxane causing the paint failure originated from the glove used to handle the aluminium samples (red arrow in Figure III-29). Replacement of this glove by a silicone-free one solved the adhesion problem. Some remarks on “aerosol targets” This special sampling technique was developed to answer a very special analytical question. Contaminants on surfaces can originate from different sources as mentioned above. One

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source is the ambient air. A production plant equipped with some fifty injection moulding machines, for example, has hundreds of sources of oil contaminants. Many machine parts are driven by air pressure which produces a fine oil dust that is expelled into the ambient air. This dust is invisible because the droplets are microscopic. But the oil can be smelled in such production plants. This “aerosol” can cause major adhesion problems. If, say, an automotive interior part that has to be finished afterwards by painting or chemical vapour deposition is stored in this contaminated air environment, the oil dust precipitates on the material surface to form a very thin, invisible film that can cause severe adhesion problems. Furthermore, the aerosol can and will be transferred to other sections of the production plant that are linked up to the ventilation system. The analytical challenge here is therefore to detect and identify the aerosols present in the ambient air around the production line – or, to be more precise, to identify those aerosols which actually precipitate and are critical as regards adhesion, e.g., cratering. This specific task has proved amenable to “aerosol targets”. The underlying idea is to place small pieces of a clean metal surface at different places on the production plant and expose them for a certain time to the actual ambient air conditions. Those aerosols which tend to precipitate will contaminate the metal surface and are held to the surface structure of the metal, which has been chemically treated to be clean at the molecular level and is very rough as far as surface topography is concerned. This passive contamination process has two effects: it traps the aerosol such that it can be identified by surface analysis and it separates that fraction of aerosols which can contaminate the products from those which do not. After this precipitation/contamination step, the samples are placed in the production line for a day or a week, collected and sealed in special containers, and transferred to the laboratory for TOF-SIMS analysis. Although the aerosols were volatile while in the ambient air, they are held on the specially treated target surface as a result of the surface pretreatment and lend themselves to analysis even by a vacuum method such as TOF-SIMS. It might come as a surprise that the cleaning process is a target of contamination analysis, as shown in Figure III-29. Yet, the fact is that many cleaning processes (even those sold by suppliers of painting plants) fail to do their intended job. Many recent reference analyses of “cleaned” parts have shown that cleaning processes can create many more problems than expected. Here are some examples: • The cleaning of a polymer part by solvents, such as isopropyl alcohol and other organic solvents, can lead to thin film contamination by additives extracted from the polymer. • Cleaning by power-washing or other water-based industrial cleaning processes can give rise to residues of the cleaning agent or cross-contamination by other parts if the cleaning program is not properly adjusted. • Corona or flame treatment and fluorination can destroy the uppermost monolayer of the substrate, and lead to material degradation. The fragments of the substrate form a loose layer on the material and, in the past, have caused wetting problems and adhesion failure. On the other hand, there may be areas of the substrate, especially, if it has a complex shape that cannot be adequately treated.

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3.1.2 Paint delamination caused by migration Up to now, we have been talking mainly about external contaminants. But segregation or migration of components from the substrate itself is an issue that must addressed in the context of polymer substrates. Pigments, for example, are incorporated into polymers by means of dispersing agents. These additives are essential for ensuring that the pigments will be uniformly dispersed. Provided they are present in low concentration, they do not cause problems. But if they manage to migrate to the surface of an injection moulded polymer part, they form a film on the surface that can impair adhesion and wetting. The following example illustrates a paint adhesion problem for 2-pack PUR paint on a PA 6.6 segment due to poor polymer quality. The root cause of this adhesion problem was studied by analysing the underside of the delaminated paint, i.e. the side facing the polymer surface and forming the paint/polymer interface. As this problem requires high surface sensitivity and a low detection limit, TOFSIMS was used to characterize the interface composition of the delaminated paint sample as well as the surface of the polymer surface beneath the delaminated paint. The main goal was to detect and identify traces of release agents.

Figure III-30: Paint adhesion failure of a 2-pack PUR paint on a PA 6.6 substrate

Figure III-31: Overview picture showing the analysed areas of the polymer segment

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These analyses revealed the presence of low-molecular-weight oligomers of the polyamide network on the underside of the delaminated paint, as well as on the polymer surface. The oligomers are short-chain molecules composed of 2, 3 or 4 repeat units of the monomer. The low-molecularweight amides tend to migrate to the surface, where they form a loose boundary which is soluble in different organic solvents and is renowned for preventing paint adhesion. Sometimes they cannot be detected until a KK treatment has been performed to verify the long-term quality of a moulded and coated part.

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Figure III-32: Detail of the pos. TOF-SIMS spectrum of the underside of the delaminated paint (→ Detection of traces of polydimethylsiloxane and oligomers of PA 6.6)

3.1.3 Delamination of polymer substrates For the automotive supplier industry, coating of polymer parts is a critical matter. Uncoated polymers for, e.g., seat faceplates, dashboards, door handles, are only acceptable for lowcost cars. For modern standard and premium cars, a sophisticated surface finish represents the state of the art and acts as major proof of quality. At the same time, the tendency to cut production costs every year has been shown to impair the quality, e.g., of the moulded parts. Each company in the supply chain of any given automotive interior part clearly seeks to turn a profit and must reduce costs. Companies therefore sometimes use low-quality materials and recycled polymers or widen the parameters of the injection moulding process to an extent that leads to polymer degradation. While this approach might boost production economics, it very often yields moulded parts that are difficult to paint. One result of inappropriate moulding parameters is a layered structure within the moulding. The polymer forms thin layers that are only loosely bonded together. This fault is not visible, but attempts to paint such a part lead to delamination of the uppermost polymer layers. The impression gained is that a paint failure has occurred, but in fact it is the polymer which fails due to improper treatment during the moulding process. An example of this failure is shown in Figure III-33 (page 110). This is a very severe delamination that is detectable by optical inspection. In most cases, though, identification of this type of polymer failure requires analytical methods. If this paint defect is to be avoided, its origin needs to be determined. The question to ask is whether a thin paint layer is delaminating from the polymer surface or if it is the uppermost layer of

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Figure III-33: Delamination of a water-borne softcoat caused by delamination of the uppermost polymer layers

the polymer which is peeling off. This is a question that can easily be answered by ATR-FT-IR spectroscopy. The underside of the delaminated paint (which is the side facing the polymer surface and formerly formed the paint /polymer interface) is analysed without any further preparation by forcing it into contact with the ATR crystal. If a cross-cut has been performed for the purposes of quality control, the small pieces of paint that came off make suitable samples for the ATR. As for the substrate side of the delamination area, there are various approaches possible. In most cases, the moulded polymer part is too big for direct contact with the ATR crystal. Thus, a piece of the polymer matching the size of the ATR crystal contact zone has to be cut out of the failed area. This approach can fail if the moulding is too thick or the surface has a strong surface topography. If sufficient contact cannot be obtained between the polymer surface and the ATR crystal for the above-mentioned reasons, the solution is a thin scalpel-cut parallel to the polymer surface. A 0.5 mm slice of the sample is cut out can easily be used for ATR spectroscopy. As ATR-FT-IR has a penetration depth of about 1 to 2 µm, it readily answers the questions raised above. In addition to the analysis of the interface, reference measurements of the polymer substrate and the paint may be necessary in order that the spectra may be evaluated accurately. An appropriate reference sample of the polymer can be produced by cutting into the substrate or cutting a granule that was used for the moulding process. Figure III-34 shows a spectrum of the underside of the delaminated paint, a reference spectrum of paint taken from an area without defects and a reference spectrum of the polymer. The ATR spectrum of the underside of the delaminated paint contains characteristic signals of acrylonitrile-butadiene-styrene (ABS), which is the composition of the polymer. Comparison of the spectra of the paint and the polymer clearly reveals that the delaminated paint has a thin

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Figure III-34: 1st ATR spectrum of the underside of the delaminated paint of a failed layer, 2 nd Reference spectrum of a paint sample taken from an area without failure, 3 rd Reference spectrum of the polymer material [S = substrate ABS, L= paint]

layer of substrate on its underside, this indicating that the failure is caused by a substrate weakness and not by poor paint or painting quality. Paint adhesion to the polymer itself is excellent. However, the instability of the uppermost paint layers causes delamination of the paint together with a thin layer of the polymer. This polymer layer has been detected on the underside of the paint after delamination. Sometimes the solvents in paint can reinforce this effect. Irrespective of the reason for adhesion or wetting problems, as far as the chemical composition is concerned, the techniques of surface analysis provide the tools for identifying the root causes. The decision as to which technique to choose is driven by the imposed detection limit and information depth. At any rate, it the chosen technique ought to deliver molecular information because the aim is to identify the substance responsible for the failure. Against this background, there are only three candidates: XPS, TOF-SIM and ATR-FT-IR. Indeed, a combination of ATR-FT-IR and TOF-SIMS has proven to be the best choice for characterizing interface chemistry in the analysis of adhesion and wetting problems. If ATR-FT-IR is used, it might be expedient to employ ATR microscopy for very small areas (diameter < 1 mm).

3.2 Investigation of paint cratering One of the most frequent painting problems is cratering. As with adhesion problems, there is more than reason for paint failure. Here is a selection:

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• contaminants on the surface • agglomeration of paint components • unexpected side reactions of paint components • substrate failures • overdose of a paint component • aerosol precipitations into the wet paint film • paint bubbles • paint spray mist

Figure III-35: SEM pictures of paint craters of different sizes, shapes and origin

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Modern surface analytical methods offer a variety of tools for discovering the root causes (Figure III-35 and 36). • TOF-SIMS, IR microscopy and SEM/EDX (see Figure III-35) permit chemical micro-analysis with visual inspection. Thus, focused characterization of gel particles, dust particles or oil droplets in the centre of the crater is possible. Comparing the chemical composition of the paint crater centre and the paint surface outside the crater provides the chemical differences that may have caused the cratering. • If the microscopic inspection of the crater hints at burst paint bubbles, a micro-analysis of the inside walls of the crater might produce residues of substances that may have caused degassing. Finally a cross-cut of the crater zone can be performed to answer the following questions: Is there an inclusion particle in the paint system? How many layers have been applied and where in a multi-layer system is the origin of the crater? The sample dictates whether a simple scalpel cut or a microtome section is enough or whether a metallographic cross section is needed. Let’s now look at the most common causes of cratering problems and how they can be identified by modern analytical tools.

Figure III-36: Investigation of paint craters by modern analytical methods

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3.2.1 Cratering caused by contaminants of the paint The whole production chain of painted parts, starting from the production of raw materials and ending with the painting process, offers plenty of scope for contamination. Internal quality control should discover most of the contaminants of raw materials before a paint is delivered to the customer. We discussed the aspects of quality control in Chapter 3.1. The next production step that needs to be reviewed is that of paint production. At various steps in paint production, the product can be contaminated by • machines and tools • storage containers • humans • aerosols • transportation • storage conditions If the cratering substance has been detected and identified, the production steps can be sampled individually. The chosen sampling procedure determines whether the samples are subjected to TOF-SIMS, scanning electron microscopy (SEM) or infrared spectroscopy (ATR-FT-IR). Abraded particles or lubricants for machines and equipment can easily be sampled by wiping the target areas. For this purpose, a clean paper tissue or paper filter should be used which can be analysed by TOF-SIMS or ATR-FT-IR for organic substances or SEM/EDX to identify inorganic material, without any further sample preparation. Wipe samples are also suitable for containers and tools, such as stirring devices. Aerosols or dusts are collected by “passive sampling” in which cleaned target material, such as aluminium and silver, is exposed for a defined period at certain positions. The aerosol or dust precipitates on the target material and is subsequently analysed by optical light microscopy, mass spectrometry (TOF-SIMS) and scanning electron microscopy (SEM). The next step is to inspect the paint shop. As well as during paint production, each machine, container or transportation device, such as a conveyor belt, are contamination sources that must be inspected when cratering problems occur. With conveyor belts, for example, there have been numerous cases of paint cratering due to the use of fluorocarbon lubricants to grease the transportation device or of fluoro-polymers to coat the belt. The sampling procedures listed in Table III-4 therefore apply to paint shops, too. The following areas have to be sampled and analysed step by step: • paint delivery • storage • preparation (stirring devices, pumps, containers) • painting (robots, tubes, spray guns etc.) • paint drying (machines, oven, transportation devices)

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Table III-3: Source and type of contaminants during paint production (sampling and detection methods) Source of contamination

Type of contamination

Sampling technique

Analytical technique

machines and equipment

abrasion

wipe sample sample of product or intermediate products filter sample

SEM/EDX ATR/FT-IR IR microscopy

lubricants

wipe sample sample from oil separators sample from tubes

TOF-SIMS ATR-FT-IR

production and transportation containers

paint residues residues of cleaning agents release agents

wipe sample

TOF-SIMS ATR-FT-IR

ventilation/ air conditioner

aerosol dust

aerosol targets

TOF-SIMS SEM/EDX

transportation

aerosol dust

aerosol targets

TOF-SIMS SEM/EDX

storage

inside coating of containers additives migrating from seals and filler necks

sample of stored product sample from inside coating of container

SEM/EDX ATR/FT-IR IR microscopy

Table III-4: Sources and types of contaminants during paint application (sampling and detection) Contamination source

Sampling

Analytical technique

paint

sample of product screen analysis

- SEM/EDX - ATR/FT-IR - IR microscopy

painting booth

wipe sample sample from separators sample from tubes

- TOF-SIMS - ATR-FT-IR

paint pretreatment

wipe sample from containers and equipment sample from inlets

- TOF-SIMS - ATR-FT-IR

ventilation systems

aerosol targets

- TOF-SIMS - REM/EDX

air supply

sample from separators sample of tubes

- TOF-SIMS - ATR-FT-IR

transportation between painting and paint drying

aerosol targets

- TOF-SIMS - SEM/EDX

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Besides machines and equipment, there is also the “human factor” to be taken into consideration when it comes to paint defects. Many failures are attributable to wrong treatment of the paint or the raw substrate, inappropriate storage or faulty operation of painting installations. Example: Paint crater caused by dust or cross-contamination by foreign paint

Figure III-37: SEM/BSE image of a clearcoat paint crater centre with particle inclusions

Where cratering is caused by external contaminants, such as dust and dried foreign paint, it is obvious that only microanalytical techniques promise success. In most cases, this sort of crater is caused by very small solid particles. This means that there is a very low quantity of a substance or a mixture of substances that has to be

Figure III-38: EDX spectrum of a crater centre with particle inclusion

Figure III-39: EDX reference spectrum of the paint outside the crater

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identified and that it is concentrated in a microscopic area of about a few microns. As we demonstrated before, the micro-analytical methods of TOF-SIMS, micro-ATR and SEM-EDX are suitable for this task. However, there are a few practical considerations that narrow the choice of technique. As far as TOF-SIMS is concerned, its high surface sensitivity is a drawback for this special analytical task even though it permits micro-analysis. If, for example, a dust particle has fallen into the wet paint and caused a crater, it might be covered with a thin layer of paint or surface-active paint additives. This “protective coating” on the particle renders the particle “invisible” to TOF-SIMS. Thus, SEM/EDX is more suitable in this case as it delivers images of the topography and chemical composition as well as elemental information about the composition of the dust particle (see Figure III-38). Elemental analysis of the inclusion particle in the centre of the paint crater (Figure III-37) shows that it consists of aluminium, silicon and calcium as well as trace components. It can be concluded, then, that fillers from a foreign paint have been introduced into this clearcoat due to improper handling. This conclusion is confirmed by the reference analysis of the paint, which shows that it does not contain these elements on a regular basis.

3.2.2 Craters and pinholes caused by substrate contaminants Example: A powder coating contains deep pinholes as shown in Figure III-40. As the majority of the pinholes penetrate as far as the substrate, surface contamination has to be assumed.

Figure III-40: Optical light microscopy image (EFI-3D) of a powder coating pinhole

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The procedure for detecting and identifying this contaminant is as follows: The TOF-SIMS spectrum of the centre of the crater (Figure III-42) contains characteristic peaks of a release agent (polydimethylsiloxane) and a fluorocarbon lubricant (perfluorinated polyether, typical trade names: “Krytox” or “Barrierta”). Both are substances which can cause adhesion problems as well as pinholes and craters. This information raises the question as to how this contamination found its way into the paint layer. In general, there are several possibilities:

Figure III-41: Powder coating pinhole x1.0³ 7.0

Polydimethylsiloxan Krytox

counts/channel

6.0 5.0 4.0 3.0 2.0 1.0 0.0 50

100

150

200

250 mass/u

Figure III-42: TOF-SIMS spectrum of the centre of a crater on a painted metal panel

•  contaminants in the paint   material •  malfunction of the painting   installation •  contaminants on the sub  strate

These questions can be answered by reference measurements on: • sample film of the paint on a neutral panel (e.g. aluminium), • random sample from different paint lots • random samples from the painting installation • random samples from raw, unpainted parts As all these samples are random, the sampling method must ensure that they are representative. The results of this analytical series help to narrow the problem and link it to a certain production line. In this case, the reference measurements showed that the root cause of the cratering was a punching machine which was emitting an oil aerosol that left microscopic oil droplets on the surface. After the machine was cleaned thoroughly, the cratering stopped.

3.2.3 Craters caused by paint additive agglomeration The following example shows that the cause of cratering is not always detectable on the surface. TOF-SIMS analysis of the crater centre of the shallow corrosion protection paint and a reference analysis outside the crater failed to reveal any chemical difference. No contaminants or inclusions were found.

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If the cause of the crater is not detectable at the surface, it might be located in a deeper layer. The analytical techniques that have been discussed cannot sufficiently penetrate a paint layer system of 10 to 100 µm. A metallographic crosscut of the paint layer system should help to identify the cause. This crosscut offers various analytical techniques to be used. Optical light microscopy of the cut, for instance, could be followed by SEM/EDX. The optical microscopy shows that the centre of the crater is inhomogeneous. The additional SEM/EDX analysis reveals the nature of this inhomogeneity.

Figure III-43: Corrosion protection coating exhibiting shallow craters

The BSE image (Figure III-45) reveals that there is a round particle inclusion beneath the paint surface in the centre of the crater. The material contrast indicates chemical differences compared with the surrounding paint. As we discussed before, EDX delivers chemical information about the defect zone and the paint (Figure III-46, page 120). Comparison of the elemental compositions of the particle inclusion and the paint reveals that the inclusion is qualitatively of the same composition as the paint, but that there is significant agglomeration of tin compounds. In the absence of any knowledge of the paint’s composition, this information cannot be evaluated. However, the knowledge that organo-tin compounds are added as catalysts makes sense of the analytical result: local agglomeration of the catalyst is causing the cratering. Not only catalysts but also levelling agents and fillers or surfacers can cause craters if they are not dissolved properly.

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Figure III-44: Optical light microscopy image of a crosscut of a corrosion paint crater (fluorescence/incident light)

Figure III-45: SEM/BSE image of a metallographic crosscut through the corrosion protection paint crater (BSE, 20 kV)

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Figure III-46: EDX analysis of the particle inclusion in the paint crater centre (20 keV)

Figure III-47: EDX reference analysis of the paint (20 keV)

Figure III-48: SEM/BSE image of a metallographic cross-cut of a basecoat paint bubble (caused by a substrate failure)

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Figure III-49: SEM/BSE image of a basecoat paint bubble caused by degassing of paint components

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Stains and deposits on painted surfaces

Figure III-50: Optical light microscopy image (darkfield illumination) of a metallographic cross-cut through a paint bubble caused by substrate cracks

121

Figure III-51: REM/BSE image of a metallographic cross-cut through a paint bubble caused by substrate cracks

3.3 Investigation of paint blistering The reasons for the formation of paint bubbles are as numerous as for cratering. Sometimes both problems are connected if a bubble of the paint bursts and forms a paint crater. Here are several reasons: • rust or water on the workpiece • salt residues • high heating rates • layer thickness too high • microscopic cracks in the workpiece • foam in the paint • (corrosion) creep of a paint layer • degassing of the substrate As the bubble in most cases hides its origin, it is good to get an overview of the topography first by optical light microscopy. As the focusing depth is low for a topographic image like this, it is good idea to enhance it by EFI. The other option, even for low resolutions, is SEM/ EDX which has a better focusing depth and allows for additional chemical analysis. After that it has to be investigated if there are residues of substances that hint at the possible cause of the paint defect. This can be done by cutting into the bubble under a microscope. A better choice is a metallographic cross-cut combined with scanning electron microscopy. Thus, a selected area of the bubble can be analysed directly by EDX with respect to the chemical composition.

3.4 Stains and deposits on painted surfaces Stains or deposits on paint surfaces can be caused by migration of additives or binder components to the surface. Normally, these layers have a thickness of a few microns or less. ATR is thus a good tool for identifying these layers and investigating the chemical composition of the

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surface layer and the paint as it has an information depth of a few microns. For good quality results, it is important that the paint surface with the stains be kept in intense optical contact with the ATR crystal and that the selected area is representative of the whole sample. Figure III-53 show the results of such an ATR-FT-IR analysis of a waterborne softcoat displaying white, crystalline stains. Comparison of the spectra of: • paint layer with the white deposit, • regular paint and • reference measurement of a paint additive identified the deposit as a paint additive which had migrated to the surface.

Figure III-52: Painted polymer surface showing white stains

If the deposit is visible and can be removed from the surface with a little bit of solvent, recourse can be made to the high surface sensitivity of ATR-FT-IR. The dissolved surface layer is transferred to the ATR crystal. The solvent evaporates to leave the deposit on the ATR crystal surface as an ultra-thin layer. In this case, extremely low quantities of material are sufficient to identify the layer. For

Figure III-53: ATR spectra of the surface of a PUR paint exhibiting a white deposit, of the paint surface without deposit, and of a reference sample

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the reference, a spectrum of the clean ATR crystal is acquired. Thus, reasonable spectra can be obtained even with a few micrograms of substance.

3.5 Analysis of paint spots Paint spots, along with craters and adhesion problems, are the most common types of failure. There are as many reasons for this kind of failure as there are for cratering: • inclusions of foreign particles • agglomerations of paint components, such as fillers and structure additives • paint spray caused by wrong application • substrate failure • residues from pretreatment processes Optical light microscopy is the first tool to use for inspection and evaluation. Unfortunately, though, the size and shape of a paint spot provide no indication as to its origin. The paint spot shown in Figure III-55, for example, seems to have been caused by a fibre. Yet ATR-FT-IR analysis shows that it consists of agglomerations of a paint additive. As this example shows, an optical examination which produces a result of the kind “it looks like a...” is very often misleading. Such a preliminary evaluation often has to be corrected on a regular basis. Also, optical inspection does not tell the story of the paint spot, but instead furnishes little hints as to the best technique for analysing the spots. There is no fixed procedure for analysing paint spots that stipulates“. If it looks like A, use method B and you will be successful”. Actually a combination of techniques will deliver the best results. The choice of method rests with the experienced analyst and his intuition. Nevertheless, inspection by optical light microscopy yields certain facts that are useful for evaluating subsequent analytical steps:

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Figure III-54: Optical light microscopy image (EFI) of a paint spot (plan view and 3-D view)

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Figure III-55: Optical light microscopy image of a paint spot of a metallic polymer paint (left) and ATR-FT-IR spectrum of the spot compared with a reference measurement of a paint additive

• shape and size of the failure • fibres sticking out of the paint spot • colour and structure of the paint spot These facts lead on to the next step of the investigation, which could be a cross-cut through the centre of the spot. The easiest way to do this is to perform a scalpel cut. But a microtome section or metallographic cross-cut (Figure III-56) is much better, because it does not produce as many artefacts as the scalpel blade. Optical light microscopy or, better, scanning electron microscopy, of the cross-section can answer the following questions: • Is substrate failure causing the visible spot? • Are there agglomerations of paint components? • Is there an inclusion particle of foreign material? • Are there cavities visible in the paint spot?

Figure III-56: Scanning electron microscopy image (BSE) of a metallographic cross-cut through the centre of a paint spot in a paint layer on a phosphated steel

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Figure III-56 in which shows a BSE image of a metallographic section through the centre of a paint spot reveals that there is an agglomeration of particles in the centre of the spot. This inclusion is visible through its material contrast because the components of this inclusion are characterized by a yield of back-scattered electrons that differs significantly from that of the surrounding paint. The enhanced brightness of the particles in the BSE image hints at substances of higher atomic weight than the paint itself. Possible sources of these particles are pigments, dust or further for-

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eign contaminants. EDX analysis of the particle (Figure III-57, below) compared with the surrounding paint (Figure III-57, above) helps to identify the inclusion. The summary of the EDX results shows that the particle inclusion consists of iron, zinc, manganese, nickel, potassium and phosphorus. These are elements typically found in zincphosphate coating processes. Thus, it can be concluded that the root cause of the paint spots are residues from the phosphating process.

Figure III-57: EDX spectrum of the surrounding paint (above) and EDX spectrum of the particle inclusion (below)

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Table III-5: Summary of EDX results Element

EDX spectrum of the paint

EDX spectrum of the inclusion particle

carbon

+

+

oxygen

+

+

iron

+

+

sulphur

+

-

chlorine

+

-

tin

+

-

zinc

-

+

aluminium

+

+

silicon

+

-

phosphorus

-

+

potassium

-

+

titanium

+

+

manganese

-

+

nickel

-

+

If a metallographic cross-cut shows that there are agglomerations of particles in the centre of the spot, micro-analysis is needed to characterize the inclusions, as the previous example illustrates. But not only SEM/EDX is suitable for this task. TOF-SIMS analysis (either the imaging mode or spot analysis) might be helpful as well. Even ATR-FT-IR microanalysis has been successfully employed, as demonstrated by the following example. Figure III-58 shows an optical microscopy image of a metallographic cross-cut of a paint spot and the ATR-FT-IR microanalysis of the centre of the paint spot. The spectrum contains characteristic signals of a polyamide which is a structure additive for the paint. Thus, the root cause of this paint spot is an agglomeration of a paint additive.

3.6 Conclusion At the end of this book, we would like to point out further key considerations. The aim of all the methods and examples we have discussed up to now is to answer questions about a sample. The question may be • Does the quality of a product (or a raw material) comply with my requirements? • Why does a certain paint lot cause cratering? • Why does a paint layer soften after a certain length of time?

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Figure III-58: Optical light microscopy image (dark field illumination) of a metallographic cross-cut through the centre of a paint spot and ATR-FT-IR microanalysis of the inclusion particle compared with a reference spectrum of a polyamide structure additive.

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The process of answering these questions is divided into five steps: 1.  Problem analysis 2.  Sampling and sample preparation 3.  Data acquisition 4.  Data evaluation 5.  Expertise Unfortunately there is always a simple and plain, yet wrong answer to each question. In fact, there are a few stumbling blocks at each step that need to be avoided if satisfactory and correct results are to be obtained. Accurate problem analysis is the first step towards finding the correct direction. A runner may be the fastest in the world. But if he runs in the wrong direction he will never arrive at his destination. Before starting an investigation or analysis, it is always best to think about the problem that has to be solved and to be sure about “What do I really want to know?” That is at once both simple and difficult. For example: In a painting installation for automotive supply parts, paint adhesion problems start to occur. Some of these failed parts are removed from the process for analysis. Now it is time to think about the aim of the upcoming investigation. You might ask “What caused the adhesion failure?” or “Why does this adhesion failure appear?” These seem to be the same question, but they are not. The answer to the first question can be found by means of surface and interface analysis as described earlier. The analyses will deliver a list of chemical substances detected by analytical techniques. After reference measurements on the substrate and the paint, followed by data evaluation, this result will be narrowed to one or several substances that have caused the adhesion problem. Although this is a very precise result, it may be disappointing because it does not tell how the failure may be avoided. That is because the question was wrong. It should have been “Why does this adhesion failure appear?” The answer to this question requires more than physical and chemical analysis. Every known detail about the appearance of the failure must be taken into account. • When did the failure appear? • How often does it show up? • Which machine parameters can be linked to the failed parts? • Were there any changes in parameters (such as paint lot, substrate lot, processing time etc.) before or during the appearance of the failure? • What has been done to correct the failure? This, then, is a more comprehensive process which needs more input than mere analytical results but which, on the other hand, delivers a mosaic of facts that help the right measures for solving the problem to be found. The next step in the analytical procedure is sample preparation. This is the step which readily produces baffling and surprising results. This can be demonstrated by an example. I asked

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one of our laboratory personnel to compare three paint samples by ATR-FT-IR spectroscopy that were provided by three different manufacturers. When he came back with the results, he told me that the three samples were identical. A close look at the spectra instantly revealed what had gone wrong. All three spectra contained broad and intense absorption peaks of water. The operator had not realized that the three paint samples were waterborne. He had measured the specimen simply by dropping a representative aliquot onto the ATR crystal, as is the standard procedure for liquid samples. However, water (which is the main solvent) exhibits strong and broad absorption peaks which overlay all spectroscopic features of the paint itself. The correct procedure would have been to prepare a dried film of the paint. This kind of sample treatment, however, is not useful for TOF-SIMS analysis because surfaceactive additives which migrate to the uppermost layer of the film whilst the film is drying form a homogeneous layer on top that masks the main ingredients. This example shows that sample preparation is one of the key operations for correct results. It should be performed by experienced analysts who have the analytical background to determine whether a standard routine is suitable for the sample to be investigated or whether it has to be modified. Analytical data acquisition has changed a great deal over the last decades due to progress in computer performance. The main aim of the instrument suppliers is to make data acquisition more convenient and faster. An “easy to use” desktop interface based on standard computer desktop features makes the work much easier and makes people believe that even inexperienced employees are able to perform an analysis. That is wishful thinking. Modern instrumentation not only makes the work easier, it also makes it easier to produce wrong results. Besides the skills needed for operating the instrument, a deep understanding of the processes happening during the analytical process is needed in order that artefacts may be separated from data. At the very least, the data should be reviewed by an experienced analyst. Another aspect is time. A good analysis needs a certain length of time. To improve the results and get the best information, it is sometimes necessary to repeat the measurements a few times. As in the saying “Grass won’t grow faster if you pull it!”, samples deserve to be treated thoroughly so that the best results will be obtained. In contrast to the modern tendency to do everything faster, efficiency is not counted in minutes or hours when it comes to analytical data acquisition. Data evaluation is one of the two most complex steps, after data acquisition. Once the data have been stored, evaluation falls into the following steps: • calibration check (if applicable) • data listing • comparison to databases • quantification (if needed) • plausibility check A calibration check is needed, for example, for TOF-SIMS measurements. TOF-SIMS data are recorded as counts per channel. Only a calibration process correlates the channels to atomic mass units (amu), yielding a mass spectrum displaying the data as counts per amu. Besides the physical data recorded by the instrument, the data listing and evaluation are heavily dependent on the calibration process. In particular, when it comes to high resolution spectra

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Figure III-59: Extract from the TOF-SIMS spectrum of a paint surface, survey of the mass range 49 u to 58 u (left) and detail of peak 57 amu (right) showing characteristic fragments of different paint ingredients with a mass resolution >5000

like the one displayed in Figure III-59, thorough calibration is essential for a correct mass assignment. This figure illustrates that the TOF-SIMS high-resolution mass spectrum of a paint surface, e.g. it can resolve five different fragments at the nominal mass of 57 u. These fragments can be assigned to a pigment (FeH+), a filler (CaOH+), an organo-fluoro additive (C3H2F+), a wax or hydrocarbon contaminant (C4H9+) and a binder (C3H5O+). This assignment is only true if the calibration has high precision. Even if the sample preparation and data acquisition were properly conducted, a calibration deviation of 0.01 amu can change the assignment and, as a result, the entire analytical result. The calibration of spectra can be performed automatically by the instrument software but needs to be regularly cross-checked by the user because physical effects, such as surface charging, can distort the calibration in such a way as to render correct peak assignment impossible. This is a detailed example of TOF-SIMS measurements showing how calibration influences the results. XPS and EDX sometimes need calibration checks as well. These should be reviewed by an experienced spectroscopist to ensure sure that the analytical results based on this calibration are reliable. The use of databases for data evaluation was discussed in the first section of this book (Chapter 2.9). This is the way to proceed from peak assignment to substance assignment and thence to the solution of the analytical problem defined in step 1. The last step of data evaluation is a plausibility check, which is essential for making sure that the data yielded by the measurement and the peak and substance assignment could be correct or may be distorted by preparative failure or physical effects.

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References

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References [1]

P.R. Griffith, J.A. deHasech; “Fourier Transform Infrared Spectroscopy”; S. 338-367; Wiley (New York) 1986

[2]

MIR-Multiple Internal Reflection; ATR-Attenuated Total Reflection; FTR-Frustrated Total Reflection

[3]

N.J. Harrick; Internal Reflection Spectroscopy, Harrick Scientific Corp., New York (1967)

[4]

Example: „Refrection index of Germanium is 4,0 and IR radiation is opaque from 2 to 12 µm (5000 to 833 cm-1). At interface of Germanium/air the penetration depth dp at 45° angle of incidence 120 nm (5000 cm-1) or720 nm (833 cm-1)

[5] Thomson; Phil. Mag. 20 (1910) 752 [6] H. Feld; Dissertation (1991) Münster, Germany [7] D. van Leyen; Dissertation (1993) Münster, Germany [8] K. Meyer; Diplomarbeit, Münster, Germany [9] R. Dietrich; Diplomarbeit, Universität Münster, Germany (1990) [10] D. van Leyen; J.Vac.Sci.Technol. A7 (1989) 1790 [11] Wiley [12] www.ofg-analytik.de [13] P.F.Schmidt et al., “Praxis der Rasterelektronenmikroskopie und Mikrobereichsanalyse”, Expert Verlag 1994 [14] NIST Data Base [15] Klewe-Nebenius, “Elektronenspektroskopie zur Oberflächen- und Dünnschichtanalytik”, www-ifia.fzk.de

Roger Dietrich: Paint Analysis © Copyright 2009 by Vincentz Network, Hannover, Germany

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Author

Author Dr. Roger Dietrich (OFG-Analytik GmbH) studied chemistry at the University of Muenster, Germany. In the framework of his doctoral thesis he concentrated on applications and advancement of practice oriented surface analytical methods for the characterisation of technical surfaces. In 1993 he founded the company OFG-Analytik GmbH together with two fellow partners which have also been engaged in the development of surface analytical methods. OFG-Analytik GmbH offers service analyses and expertises concerning failure analyses and quality control for industrial costumers. Dr. Dietrich, who is now CEO of the company, has focussed his activities on surface and materials analysis of paints and coatings. Besides he has been assistant lecturer for “Applied Surface Analysis” at the University of Applied Science in Muenster and hosts analytical training courses for industrial and institutional costumers.

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Index Symbols 2-pack polyurethane paints  96

A AAS  90 abraded particles or lubricants  114 absorbance spectrum  25 absorbed electrons  54 absorption  22 acrylonitrile-butadiene-styrene (ABS)  110 additive  52, 73, 77, 80, 90, 94, 102 additive migration  99 adhesion  103 adhesion failure  50, 103 adhesion problems  103 adipic acid  80 aerosol  114 aerosol precipitation  105, 112 aerosol target  106 AES  16, 104 AFM  75 agglomeration  112, 126 air conditioner  115 aircraft industry  78 air supply  115 alkyl  69 ambient air  107 analyser  17 analysis of insulating samples  50, 74 analytical data acquisition  129 angle of incidence  30, 31, 32 antioxidant  84, 87 aromatic  69 atomic absorption spectroscopy  90 ATR  16, 27, 30, 32 ATR crystal  33, 110, 122 ATR crystal material  33

Roger Dietrich: Paint Analysis © Copyright 2009 by Vincentz Network, Hannover, Germany ISBN: 978-3-86630-912-8

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ATR-FT-IR microanalysis  127 ATR-FT-IR  24, 25, 27ff, 30, 32, 33, 48, 75, 78, 83, 84, 96, 103, 110, 111, 115 122 attenuated total internal reflectance  27 Auger electrons 54 automotive  95, 96, 107, 109 availability  74 availability of instruments  66, 74 available information  74

B back-scattered electrons  14, 54, 56 back-scattering coefficient  56 BaSO4  56 bending vibration  21 binder  50ff, 59, 77, 90, 130 binder/hardener ratio  97 binder-production  52 bond energy  67 BSE  55, 56, 60 by-product  80, 82, 94

C calibration check  129 catalyst  77, 119 cathodoluminescence  54 CHA  72, 73 characteristic vibrations  20 characteristic X-ray emission  60 characteristic X-ray radiation  57 chemical (molecular information)  75 chemical maps/imaging  50 chemical reactions  12 chemical shift  20 cleaning  107 clearcoat  39, 100 CMA  73 CDS  61, 62 container  114 contaminant  12, 50, 78, 81, 82, 86, 106, 112

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contamination  105 Corona  107 cosolvent content  76 critical angle  28 cross-contamination  116 cross-cut  119, 124 crosslinking process  99 cross-reaction  78 curing  50 cuvettes  24 cyclic compound  80

D database  13, 40, 129 data evaluation  40, 66, 74 data listing  129 defracted electrons  54 degradation process  78 degree of crosslinking  96 delamination  12, 65, 109 deposit  121 depth of focus  19 depth of penetration  30 depth profiling  16, 17, 50, 66, 74, 75 depth resolution  50, 66, 74 detection limit  15, 33, 50, 65, 66, 74, 75, 89 detection of elements  50, 66, 74, 75 detector  17, 72 diamond  34, 35 diffraction  63 digital separation  94 discoloration  12, 78 distribution  100 DTGS  38 dust  114 dynamic SIMS  43

E EDS  61 EDX  16, 56, 57, 61, 104, 125, 130 effective path length  31 effect pigment  73, 86 EFI (Extended Focal Imaging)  18, 19, 55, 121 EI  48 Elastic scatter of electrons  54

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Index

electrodeposition primer  69, 70 electron lens  72 electrons  13 EMA  61 equipment  115 ESCA  16, 67 ESMA  16, 61, 75 ester  69 ether  69 evanescent wave  28 expenditure of time for measurement  66 external reflection  36 extinction coefficient  31

F failure analysis  76 failure spot  65 far infrared  22 fatty acid  84, 87 fatty acid amide  84 ferrous oxide  87 fibres  36 filler  56, 85, 86, 90, 119, 123, 130 filter residue  89, 92, 93 flame treatment  107 fluorescence  14 fluorescence emission 60 fluoride  104 fluorinated polymer  104 fluorination  107 fluorocarbon lubricant  118 fluoro-surfactant  104 fogging  53, 95 fogging residue  94 Fourier transform  24 FT-IR  16, 24 FTR  27

G gas  80 gas-chromatographic analysis  82 GC-MS  80, 82 germanium  35 glove  106

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H

L

handling  77 hardener  96 hardener ratios  98 harmonic oscillator  21 H-ATR  83 hexanediol  80 Hooke’s law  21 horizontal ATR  36 HPLC-MS  80 HPT  102 human  114 hydrocarbon contaminant  130

Lambert-Beer law  26 lateral resolution  50, 66, 68, 74 levelling agent  119 light-stabilizer additive  100 liquid chromatography  80 LM  18 loose boundary  108 lowest grasped sampling depth  75

I ICP  90 identification of chemical bonds  74 identity checks  52 identity control  78 imaging of element  75 inclusion  123, 124 inclusion particle  113, 127 inductively-coupled plasma  90 inelastic scattered electrons  54 information  66 information content  15 information depth  15, 31, 50, 60, 66, 68, 74 infrared microscopy  36ff, 38 infrared radiation  28 infrared spectroscopy  20 infrared waves  13 injection moulding  107 instrumentation  33, 58 inter-atomic bonds  20 internal reflection  34, 36 investigation of chemical bonds  66 ions  13 IR  103 IRM  16 IR microscopy  36ff, 103, 115 IRRAS  16, 24, 27 isocyanate hardener  97

K kinetic energy  67 KK treatment  108 KRS-5  34, 35

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M machine  114, 115 manganese  125 mass range  50 mass spectrometry  48, 80, 92, 114 masterbatch  96, 98 matrix effect  47 MCT  37 measuring area  75 melamine  102 metallographic cross-section  59, 103, 113, 124 Michelson interferometer  24 micro-ATR  103 micro-extraction  87 microtome cross-section  38, 59, 113 mid infrared  22 migration  50 MIR  22, 24, 27, 34 mirror optics  37 molecular information  50 molybdenum oxide  87 monomer  77, 94 moulded  108 multi-layer system  100, 113 multiple reflection  34

N near infrared  22 near-infrared spectroscopy  78 neopentyl glycol  80 neutral particles  13 NIR  21, 78 non-volatile  84 not deflected electrons  54 number of internal reflections  32

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O oil dust  107 oligomer  77, 94 optical contact  32 optical light microscopy  18, 103, 114 organic contaminant  90 organo-fluoro additive  130 organo-tin compound  119 oven  114

P packaging  77 paint  115 paintability  11 paint additive agglomeration  118 paint adhesion failure  12 paint blister  103 paint blistering  121 paint bubble  112, 120 paint crater  36, 50, 65, 111, 116, 117 paint delamination  104, 108 paint flaw  65 painting booth  115 paint pretreatment  115 paint raw materials  50 paint shop  114 paint spot  123, 127 paint spray  123 paint spray mist  112 particle inclusion  116 PDMS  49, 52 PE  55 penetration depth  28, 60, 68, 110 perfluorinated polyether  118 perpendicular  38 phosphate  87 phosphorus  125 photons  13 PIDD  43 pigment  85, 86, 124, 130 pinhole  118 PMMA  102 plausibility check  13, 129 polyaddition  77 polyamide  108 polycondensation  50, 77 polydimethylsiloxane  47, 49, 52, 81, 105, 118

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Index

polyester  80 polyester binder  93 polyether  70 polyethylene glycol  70 polymerisation  77 polypropylene glycol  70 polysiloxane additive  100 power-washing  107 primary electrons  54, 55 primary ion dose  43 primary radiation  13 processing auxiliaries  87 production control  76 pump  114 powder coating  117 production  115

Q qualtitative analysis  89 quality control  52, 76 quantification  26, 32, 49, 50, 64, 66, 71, 74, 75, 129 quantitative analysis  89 quantitative measurements performed  65

R reaction product  94 rearrangement  78 reference spectrum  25 refractive index  30, 32 REM/EDX  103 residue  84, 85, 105 resins  51, 52 robot  114

S sample  46, 66, 74 sample preparation  46, 66, 74, 129 sample properties  50 sample size  50, 66, 74 sample spectrum  25 sampling depth  75 scanning analysis 16 scanning electron microscopy  54ff, 114 SE  55, 60

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Index

secondary electron  14, 54, 55 secondary neutral mass spectrometry  43 secondary radiation  14 SEM  13, 16, 61, 68, 75, 84, 103 SEM-BSE image  90 SEM/EDX  59, 89, 90, 103, 115, 117, 119, 121 side-chain modification  52 side reaction  78, 112 silicate  87 silicone-free  106 SIMS  16, 43 SNMS  43 silicon  35 single reflection  34 slowdown continuum  60 solvent  59, 77, 82 solvent blend  83 solvent residue  94 spatial resolution  75 spin-coating  46 spot  103, 124 spot analysis  16, 92, 101 spots  36 spray gun  114 sputter system  17 SSIMS  43 stabiliser  87 stage of development  66 stain  121 static SIMS  43 stirring device  114 storage  77, 115 storage condition  114 storage container  114 stretching vibration  21, 22 structural analysis  52 structural characterization  20 structural identification  22 structural information  50 structural studies  74 structure additive  123, 127 structure determination  66 study of insulating material  66 substrate handling  105 suitability  15 surface  10 surface-active  85 surface-active paint additive  117

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137

surface area analysis  16 surface charging  67 surface contaminant  73 surface infrared spectroscopy  26 surface modification  86 surfacer  119 surface topography  19 surfactant  77

T time-of-flight analyser  45 time-of-flight secondary ion mass spectrometry  43 ff time per analysis  50, 74 time per measurement  50, 74 Tinuvin  102 TiO2  65 TOF-SIMS  13, 16, 43ff, 45, 48, 68, 75, 80, 81, 84, 89, 96, 103, 108, 115, 117, 130 TOF-SIMS imaging  100 topography imaging  75 trace analysis  36 trace component  117 trace contaminant  52, 80 transmission  36 transmission infrared spectroscopy  24 transmission spectrum  25 transport  77 transportation  114, 115 transportation container  115 transportation device  114 tube  114

U uncured paint film  59

V vacuum  66, 74 vacuum chamber  72 vacuum technique  59 ventilation  115 ventilation systems  115 volatile compound  94

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Index

W waterborne softcoat  122 wavelength  30, 32 wavelength-dispersive spectrometer  62 wax  102 wetting failure  12 wetting problems  103 WDS  61, 62 WDX  16, 57, 61, 62

X XPS  16, 67, 68, 75, 84, 111, 130 XPS survey spectrum  69 X-ray  13, 14 X-ray emission  54 X-ray excitation  68 X-ray photoelectron spectroscopy  67 X-ray source  72

Y yellowing  78

Z zinc  125 ZnSe  34, 35

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Buyers‘ Guide Accelerated shelf life and formulation test equipment L.U.M. GmbH D-12489 Berlin www.lum-gmbh.com [email protected] Tel. +49 30 67806030

Accelerated weathering apparatus Gebr. Liebisch GmbH & Co. KG D-33649 Bielefeld Tel. +49 521 94647-0 www.liebisch.de

Colour matching booths www.polytec.de

Colour matching equipment www.tqc.eu, Tel. +49 2103 25326-0

Condensation/ air conditioning testers Gebr. Liebisch GmbH & Co. KG D-33649 Bielefeld Tel. +49 521 94647-0 www.liebisch.de

Condensed moisture testers

Corrosion test sheets Gebr. Liebisch GmbH & Co. KG D-33649 Bielefeld Tel. +49 521 94647-0 www.liebisch.de www.tqc.eu, Tel. +49 2103 25326-0

Corrosion testers Gebr. Liebisch GmbH & Co. KG D-33649 Bielefeld Tel. +49 521 94647-0 www.liebisch.de www.tqc.eu, Tel. +49 2103 25326-0

Cross-hatch adhesion test instruments Gebr. Liebisch GmbH & Co. KG D-33649 Bielefeld Tel. +49 521 94647-0 www.liebisch.de

Dispersion stability analysers L.U.M. GmbH D-12489 Berlin www.lum-gmbh.com [email protected] Tel. +49 30 67806030

Emulsification testers

Gebr. Liebisch GmbH & Co. KG D-33649 Bielefeld Tel. +49 521 94647-0 www.liebisch.de

L.U.M. GmbH D-12489 Berlin www.lum-gmbh.com [email protected] Tel. +49 30 67806030

Contact angle measuring equipment

Film forming temperature meters

Thermo Scientific, www.thermo.com/mc

www.tqc.eu, Tel. +49 2103 25326-0

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Buyers’ Guide

IR spectrometers

Screening equipment

www.polytec.de

HAVER & BOECKER, 59302 Oelde Germany, Phone +49 2522 300 www.weavingideas.com

On-line particle size measuring  equipment HAVER & BOECKER, 59302 Oelde Germany, Phone +49 2522 300 www.weavingideas.com

Particle analysers HAVER & BOECKER, 59302 Oelde Germany, Phone +49 2522 300 www.weavingideas.com L.U.M. GmbH D-12489 Berlin www.lum-gmbh.com [email protected] Tel. +49 30 67806030

Separation analysers L.U.M. GmbH D-12489 Berlin www.lum-gmbh.com [email protected] Tel. +49 30 67806030

Sieve analysis equipment HAVER & BOECKER, 59302 Oelde Germany, Phone +49 2522 300 www.weavingideas.com

Spectrophotometers www.polytec.de

Particle size measuring equipment

Tensiometers

HAVER & BOECKER, 59302 Oelde Germany, Phone +49 2522 300 www.weavingideas.com



L.U.M. GmbH D-12489 Berlin www.lum-gmbh.com [email protected] Tel. +49 30 67806030

SITA Messtechnik GmbH Phone: +49 351 871-8041 www.tensiometer.info

Pendulum hardness testers

Test sheets/materials

www.tqc.eu, Tel. +49 2103 25326-0

www.tqc.eu, Tel. +49 2103 25326-0

Rheometers Thermo Scientific, www.thermo.com/mc

Ultrasonic measuring instruments

Salt spray testers

HAVER & BOECKER, 59302 Oelde Germany, Phone +49 2522 300 www.weavingideas.com

Gebr. Liebisch GmbH & Co. KG D-33649 Bielefeld Tel. +49 521 94647-0 www.liebisch.de

UV curing equipment www.polytec.de

www.tqc.eu, Tel. +49 2103 25326-0

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143

UV meters www.polytec.de

Viscosimeters proRheo GmbH Rheometer Viscosity measurement Lab and Online www.proRheo.de, [email protected] Thermo Scientific, www.thermo.com/mc

Weathering instruments Gebr. Liebisch GmbH & Co. KG D-33649 Bielefeld Tel. +49 521 94647-0 www.liebisch.de

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