High-Throughput Industrial Coatings Research at

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High-Throughput Industrial Coatings Research at The Dow Chemical Company Tzu-Chi Kuo,† Niranjan A. Malvadkar,† Ray Drumright,‡ Richard Cesaretti,† and Matthew T. Bishop*,† †

Core R&D Formulation Science and ‡Dow Coating Materials R&D, The Dow Chemical Company, Midland Michigan 48674, United States ABSTRACT: At The Dow Chemical Company, highthroughput research is an active area for developing new industrial coatings products. Using the principles of automation (i.e., using robotic instruments), parallel processing (i.e., prepare, process, and evaluate samples in parallel), and miniaturization (i.e., reduce sample size), high-throughput tools for synthesizing, formulating, and applying coating compositions have been developed at Dow. In addition, high-throughput workflows for measuring various coating properties, such as cure speed, hardness development, scratch resistance, impact toughness, resin compatibility, pot-life, surface defects, among others have also been developed in-house. These workflows correlate well with the traditional coatings tests, but they do not necessarily mimic those tests. The use of such high-throughput workflows in combination with smart experimental designs allows accelerated discovery and commercialization. KEYWORDS: high-throughput, industrial coatings, accelerated discovery, automation, parallel processing, property screening, cure speed, pot-life

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INTRODUCTION Industrial coatings for protection and decoration constitute a global $55 billion and 17 million MT per annum market.1 This market is fragmented with many different chemistries and formulations employed to meet the needs of diverse applications. The development of coating formulations is an inherently combinatorial problem because (1) coatings consist of multiple components, such as polymers, pigments, additives, and solvents; (2) for each component type, there is a multitude of materials from which to choose; and (3) coating processing conditions, such as temperature, time, and humidity, influence coating properties. For these reasons, thin films and coatings have been major areas where researchers have sought to employ combinatorial and high-throughput (CHT) capabilities to understand existing systems, discover new materials, and optimize formulations in an efficient, rapid, and robust manner.2−12 Prior reviews have discussed the promise and documented the progress for materials in general13−15 and coatings16 more specifically. Adding to the above motivations for employing CHT approaches for industrial coatings is the need to address the growing demand for more sustainable coatings (e.g., polymers derived from biological feedstocks and formulations with reduced organic volatile emissions.)17,18 This Account gives an overview of the CHT capabilities, equipment and methods, implemented at The Dow Chemical Company for industrial coatings R&D. These capabilities were designed for flexibility and use in different combinations as customized workflows to fit the needs of a variety of research problems. Some of the capabilities were developed by Dow and others jointly with Freeslate, Inc. (Sunnyvale, CA). In describing these capabilities, the focus is on those which are © XXXX American Chemical Society

relatively unique. Several examples are presented to illustrate the diversity of problems which have been addressed by these modular capabilities. Examples are also given of data visualization and modeling approaches, including challenges encountered for some types of studies in reducing a large body of results to practical, useful conclusions. A CHT workflow for coatings is often shown schematically as in Figure 1, which leaves aside details of what equipment lies at the different nodes and how that equipment works together as well as details of experimental design and data analysis, all of which details are crucially important to success. In this introduction, we take a step back to start with broader considerations which shaped our particular approach toward building CHT capabilities for industrial coatings: (1) the

Figure 1. Conceptual CHT coatings workflow. Received: April 11, 2016 Revised: June 29, 2016

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DOI: 10.1021/acscombsci.6b00056 ACS Comb. Sci. XXXX, XXX, XXX−XXX

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ACS Combinatorial Science diversity of coatings problems; (2) the types of data required to accomplish the goals of different types of projects; (3) the breadth of chemistries in terms of factors, such as reactivity and viscosity; and (4) practical issues, such as scale, library formats, and extent of integration of different workflow steps. As might be gathered from this listing, we placed a premium on building sufficient flexibility to deal with a wide and to some extent unknown variety of current and future industrial coating challenges. To achieve this flexibility at acceptable level of investment, it was necessary to augment a core set of automated HT capabilities with manual operations for more specialized and occasional needs. However, the inclusion of some manual operations in a HT workflow does not necessarily compromise throughput, many manual operations are quite efficient, although it does generally require more human resources than a fully automated workflow. Weighed against this, however, is the cost or delay associated with automating a little-used or hard-to-automate or very efficient manual operation. By no means do we consider our decisions and approach prescriptive for all cases but merely suitable for the needs of our company. Surveying coatings R&D problems, they generally fall into one of two categories: the development of a full formulation for a specific application;8,12,19−25 or the development of structure−property understanding to define strengths/weaknesses of new materials.9 For the first type of problem, the property requirements are well-defined and the answer does not necessarily have to be the best formulation; instead, formulations which meet the needs and cost constraints of the application are sufficient. Many of the CHT coatings workflows and problems reported in the literature fall into this first conceptual category. In this category, it is often possible to define a few key properties to use as primary screens which streamlines and simplifies the CHT workflow. However, for a materials supplier, many problems fall into the second category. To be successful in designing, developing, and commercializing new materials, it is necessary that they provide a value proposition over existing raw materials by being able to provide improved performance at equivalent cost, lower cost at equivalent performance, or both lower cost and improved performance. The importance of providing customers with the formulation guidance that they need to work successfully with a new material cannot be overstated. Significant changes in technology, for example, waterborne coatings replacing solventborne coatings or swapping an incumbent resin for a new one from a different chemical family, usually require the formulators to follow different (and often contradictory) rules than those appropriate for the technologies that they are familiar with. For this second category of coatings problems, it is the balance of key properties which is most important. Confidence in conclusions demands replicates of high precision data from multiple tests in order to define subtle differences in property balance. Furthermore, such studies often span a large formulation space, stemming from the number of materials which need to be compared as well as the potential for the same material to be used by formulators in very different application/ property spaces; for example, in ambient-curing flexible coatings or in highly cross-linked solvent-resistant bake enamels. Another useful way of classifying coatings problems is by where they fall in the overall sequence from material synthesis to application performance (Figure 2), recognizing that projects will typically involve elements from multiple stages in different

Figure 2. Conceptual breakdown of CHT coatings workflow by stages in process from materials to application.

unique combinations. The CHT capabilities for industrial coatings include both integrated systems as well as standalone tools for library preparation and testing at each of these stages. In our experience, the research objectives spanning a market space like industrial coatings vary too widely to make a highly integrated “universal” workflow feasible. Instead, standalone stations flexibly bridged together by design factors such as a few standard library formats allows for the rapid assembly of new workflows. This approach also helps accommodate multiple projects concurrently. Coating performance testing within CHT workflows is a topic which has been dealt with extensively, including novel CHT-suitable tests for many specific application properties as well as more general issues such as the importance of assessing the reproducibility and variability of tests. Coatings is an area of applied technology which historically has relied on a number of specialized qualitative tests which can be readily employed by researchers in the lab or even in the field with minimal or inexpensive equipment. Convincing those entrenched in the use of standard coatings tests to accept correlations between CHT and conventional tests is often challenging. However, correlations are powerful bridges connecting the established common knowledge base of coatings practitioners to the conclusions drawn from CHT experimentation. Therefore, establishing and agreeing upon correlations between CHT and conventional tests is a critical part of development of CHT tests. Revisiting correlations between conventional and CHT testing must be done with each workflow to ensure satisfactory outcomes. CHT tests are generally designed to provide continuous quantitative responses in contrast to conventional coatings tests which are often observational, ordinal rankings with varying degrees of subjectivity.26,27 As mentioned above, developing new materials requires understanding how they impact the balance of key coating performance properties. Conclusively proving improved performance balance compared to competitive materials requires good statistical discipline. Therefore, CHT tests must not only have good repeatability but also high precision. These considerations drive our selection of tests and proper ways to implement them. Fundamental materials properties such as Tg, modulus, hardness, and degree of cross-linking can often be measured with greater precision than application properties such as abrasion resistance, solvent resistance, and flexibility. Fundamental material property tests bring several advantages over conventional application tests: (1) they facilitate building mechanistic understanding of the dependence of applied properties on underlying materials properties; (2) they provide confidence in statistical trends observed in more B


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when the formulation is prepared to when it is coated). In contrast, for reactive coatings a serial workflow similar to an assembly line is required. In the serial coatings workflows, samples are passed from each subprocess to another with controlled timing; for example, the time from completion of mixing to time when the coating is made. To keep one slow subprocess from being a bottleneck, some subprocesses operate in staged parallel fashion; for example, multiple samples can be mixing at the same time but they all enter and leave the mixing substation with the same relative timing. The accommodation of diverse chemistries does not stop with viscosity and reactivity considerations. Other special features of the formulators are low moisture enclosures (for isocyanate-based components and coatings), addition of components during mixing, and heated dispense for resins which are semisolid or extremely high viscosity at room temperature. Nonetheless, despite the flexibility built into the basic workfows, other needs often arise which must be handled manually. Above we have mentioned some of the high-level considerations to ensure that our industrial coatings CHT capabilities were designed to address Dow’s particular needs. We now briefly discuss two practical decisions which were of great importance toward achieving our objectives: scale and format. The formulations for Dow’s industrial coatings CHT workflows are generally prepared on a scale of 5−25 g, a lesser amount than typical bench scale but a good bit greater than used in many other CHT workflows at Dow. Although this scale can be a problem when studies involve custom synthesized components, this deficiency is far offset by a number of benefits derived from operating on this larger scale, which include: (i) the dispense accuracy and precision needed for higher viscosity materials, stoichiometric control for reactive systems, and addition of low-level components; (ii) more robust mixing solutions; and (iii) multiple large-format coatings to allow for a variety of tests from a single formulation. The larger coatings format allows for multiple properties to be measured directly on the same coating panel and thus the same drying and cure history. It also allows panels for conventional tests to be generated from the same formulation and coatings preparation process as used for CHT tests. This results in a selfconsistent set of properties, especially important for: (i) property balance type problems; (ii) developing correlations between CHT and conventional tests; and (iii) separating the variability in each step of the cumulative coating preparation and testing process. Larger coatings also remove issues associated with small-size coatings such as edge effects and nonscalable drying and curing. Since a bottleneck is often formulation preparation and not coating preparation (at least for complex real-world formulations), there is often little gain in throughput by going to array coating format. Furthermore, the diversity of chemistries and formulation types can get in the way of developing a “universal” method for coating array preparation, e.g., spreading may need to be done one-by-one because of kinetics of drying, curing, and film formation. Another important decision was to standardize on a unique large-coating format, using microtiter-plate-sized “picture frames” as stackable carriers for coating substrates. This format enables the use of common microtiter plate handling robots to transfer coatings onto and off of both preparation and test stations. This in turn allows for seamless interoperability of standalone preparation and test stations−magazines containing stacks of coatings are simply transferred from one station to another. This format aids in running multiple projects

applied performance tests; and (3) for experimental designs based on molecular and materials properties descriptors, they provide a response which is more directly and scientifically linked to the input variables. Aside from being preferred for hypothesis-driven research, these latter descriptor-based experimental designs provide balance and reduce redundancy which enables efficient development of robust statistical models. As guidance for material discovery and development, models based on fundamental properties are powerful. On the testing side of CHT, the above considerations have driven a strong preference toward quantitative screens. Information-rich tests such as microindentation which return not only modulus and hardness but a variety of other parameters (e.g., elasticity-plasticity ratio) are very useful. Despite this testing preference, many applied properties are still important to measure because of a lack of clear correlation to a single or small set of fundamental properties. However, defaulting to measurement of application properties is not only lacking in scientific rigor but is also impractical because developing automated/parallel CHT tests for all applied coatings properties is impractical. As a result, CHT tests have been developed only for those applied properties with broad applicability. The large near-standard-size coating format of the coating preparation stations allows panels to be prepared for additional “manual” property tests when needed. Lastly, many testing platforms were designed with the flexibility to be adapted for different tests. For example, the tribometer can be used for measurements such as drying or curing time, coefficient of friction, wear resistance, and tackiness.28 At Dow there is a broad span of chemistries in our commercial and development pipelines that are relevant for industrial coatings. The two aspects of chemical diversity which most impact CHT capability development are viscosity and reactivity. Rather than designing universally applicable systems for formulating and applying coatings, we instead chose to build multiple stations, each optimized for working with a certain class of formulations. For example, separate low viscosity and high viscosity robotic formulators as well as nonreactive and reactive coatings stations were implemented. The automated formulators and coatings stations were designed to allow them to be used alone or in combination as needed. A further benefit of this multistation approach is that it better allows running multiple projects concurrently. Dow’s expertise in fluid mechanics and mixing was very useful in ensuring that the proper mixing technology was employed in the various stations, based on factors such as scale, overall viscosity, component ratios, viscosity mismatch of components, and so forth. The dispensing and dispersion of dry pigments and fillers was not incorporated directly into the robotic formulators. Instead, we rely on efficient small-scale manual preparation of pigment and filler master batches and grinds to be used as components for robotic formulators. Nonreactive (e.g., film forming latex) and reactive coatings (e.g., two-component polyurethane or epoxy) required implementing two fundamentally different workflow approaches, batch and serial, respectively. For nonreactive and slow-reacting coatings, 12 to 48 or more formulations are processed on each station and transferred from one station to another in batches (e.g., from formulator to coating station). For the most part these batch workflows process samples sequentially, not in parallel. These batch workflows are suitable when the variations in timing of subprocesses among the samples in a batch do not affect the results (e.g., timing from C


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ACS Combinatorial Science Table 1. High-Throughput Capabilities for Industrial Coatings Research capability

type

Hamilton MicroLab STAR

liquid handler/ liquid characterization liquid handler

high viscosity formulator Freeslate MTM powdernium fluids rheometer Freeslate coating station Freeslate reactive coating station Freeslate tack and friction station tribometer Freeslate color/ gloss/thickness station cross-hatch adhesion microindenter impact station imaging station white light interferometry

solid handler formulation characterization coating coating and curing coating characterization coating characterization coating characterization

description

applications

liquid formulator capable of handling viscosities up to 7500 cP with on-deck digital balance and on-deck heating capability

reactive formulations (e.g., synthesis of prepolymer), liquid viscosity measurement

dispenses liquid systems ranging from 1 cP to 1 × 106 cP in viscosity granular solids formulator with on-deck balance

epoxy resin formulation, let-down of paints

automated sample loading/removing to perform standard Anton Paar cone and plate rheological measurements for up to 96 samples automated application of one-part coating systems on a variety of substrates with ambient curing automated application of reactive coating systems on a variety of substrates with UV, IR, thermal curing capability lateral and normal load cells with ball probe (tack and friction measurement) or sharp tip (for scratch application) automated tribometer with disposable ball probes for tack, friction and wear measurements robotic arms equipped with BYK-Gardner micro-trigloss meter and Ocean Optics color meter

viscosity, viscosity index, rheology, yield point

pigments and fillers in paints

coating of paints, latex, dispersions, and adhesives coating of reactive epoxy and polyurethane systems measurement of coefficient of friction, cure speed, tack-free time, and scratch resistance friction, coefficient of friction, tack, wear resistance measurement of color, gloss, thickness; evaluate scratch resistance

coating characterization coating characterization coating characterization coating characterization

robotic application of cross-hatch on coated surfaces

measurement of adhesion to substrate

Fischerscope HM2000 microindenter modified for automated sample testing automated impact testing of coatings (front and back) with imaging capability imaging station integrated with automated coating substrate handler

measurement of coating hardness, elastic modulus, compliance, and scratch resistance measurement of impact resistance of coatings

coating characterization

Veeco NT9080 white light interferometer

morphology and imaging of coatings for sag, leveling, cross hatch, chemical resistance, corrosion resistance evaluation of amine blushing, scratch/wear depth, and imaging of surface features, defects

Figure 3. Photograph of the Dow integrated industrial coating system: (a) mixing and coating work cell, (b) UV and IR curing work cell, (c) thermal curing station, and (d) tack and friction station. The reactive coating station consists of all the white-fronted work cells, the left half of figure.

equipment systems would be little more than the usual set of standalone equipment found in typical coatings R&D laboratories. However, with these linkages, these CHT equipment systems can be flexibly used together in custom ways to provide high quality data for addressing diverse coatings problems of interest to our customers.

concurrently and putting together workflows tailored to the specific needs of different projects. This format also accommodates both rigid and flexible substrates, with the only requirement being standard size of the substrate; and this format has also been adapted for arrays of small coating patches on a single substrate. In this introduction, we have covered the principles and guiding philosophy behind the industrial coatings CHT capabilities which have been developed at Dow. We have also covered two important details of implementation, in particular scale and format, which serve to tie together the various equipment systems to be discussed in the remainder of this account and to integrate conventional application testing into the CHT workflows. Without these linkages, these CHT

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OVERVIEW OF HIGH THROUGHPUT TOOLS FOR INDUSTRIAL COATINGS Over the past decade, The Dow Chemical Company (Dow) has made significant capital investment in the area of high throughput (HT) industrial coatings research capabilities. Table 1 shows the list of HT capabilities at Dow frequently used for industrial coatings projects. While some HT tools are D


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Library Studio and makes the formulations (or portions thereof) based on the compositional and experimental parameters in the library design. For formulations containing solids, a Freeslate many-to-many powdernium is used to dose solids to be added into the formulations. Once formulations have been prepared, it is often important to measure selected properties of the formulations such as solubility, phase behavior and compatibility, surface tension, wettability, particle size, viscosity, and stability. Several Dowbuilt imaging-based HT tools (not described here) are available for characterizing phase behavior, compatibility, and solubility as a function of temperature, aging time, and other parameters.31 Commercial tools are used for surface tension and particle size measurements. Formulation viscosity and stability are typically measured using a HT method (TADM viscosity), which is described below (Viscosity and Pot-Life section). Coating and Curing. The weighing, mixing, coating, and optionally UV or IR curing of each two-part formulation are carried out in the reactive coating station (RCS, Freeslate, Inc.), which is the the white-fronted section in the left half of Figure 3. The RCS includes several individual work cells (mixing and coating, curing, material storage, and substrate storage), a flexible automation platform for transfer between work cells, computers and sample tracking equipment for system control, and safety devices. The mixing and coating work cell has two robotic arms that control all necessary motions connected with aspiration, dispense, mixing, and coating drawdown operations (Figures 4 and 5). The computer reads the library design from the

designed to mimic conventional coating and testing methods, others are designed to correlate with the traditional approaches but not exactly mimic them. In the latter case, validation of the correlation is performed before using these tools in product development. Although, product development in industrial coatings depends heavily on these HT tools, conventional experimental approaches are indispensable and are therefore used frequently to augment HT approaches. Conventional measurements often used include Koenig (pendulum) hardness, MEK double rub, chemical resistance tests, and various microscopic and spectroscopic analyses. However, the core coating and curing capabilities are integrated, as shown in Figure 3, to allow fully automated sample preparation. Library Design and Formulations. Library Studio (Freeslate, Inc.) software allows users to define formulation libraries, which consist of materials compositions plus, as needed, experimental parameters for formulation preparation (e.g., order of addition, mixing time/speed), coating and curing (e.g., coating thickness, flash time, oven time/temperature), and testing. The “library design” is an array containing all the compositional and experimental parameters which are needed as inputs for the programs and methods which the robots and operators use to carry out the experiments. The library design is passed to liquid and solids handlers for formulation preparation, to coating and curing stations, to testing stations, and to humans for manual tests. Library Studio allows users to enter compositions and experimental parameters in convenient units (e.g., stoichiometric %) and then converts them into units needed by the equipment (e.g., weights subject to an overall sample volume constraint). Statistical experimental designs based directly on compositional and experimental variables can also be imported directly into the Library Studio software. However, experimental designs based in whole or part on more fundamental variables, for example, molecular descriptors of components, properties of components or the formulation, and variables in mechanistic models (e.g., temperature in cure kinetics models), must first be translated into composition and process variables which can then be entered into Library Studio. We rely heavily on a wide variety of predictive models in creating fundamentals-based experimental designs, and in practice the use of these predictive models serves as a valuable combinatorial prescreening of components and compositions to yield experimental designs based on the more fundamental governing variables. Over the years Dow has developed a number of proprietary predictive models which are useful for coatings. Examples are models which predict: Hansen solubility parameters29 and evaporation rate profiles for solvent blends; rheokinetics of reactive systems; and properties of components, formulations, and cured systems based on molecular structure and composition (e.g., Tg, modulus, toughness, and solubility parameter). For reactive epoxy and polyurethane systems, Dow has developed such structure-composition-property predictive models based on a rather novel group contribution approach30 which accommodates diverse chemical structural moieties and is readily adaptable to include the effects of cross-linking in thermoset systems. Formulations are generally prepared using robotic formulators, alone or in combination. An eight-channel liquid handler (model Microlab Star, Hamilton Robotics, Inc., Reno, NV) is used for general lower volume/low viscosity liquid formulations. A Dow custom-made high viscosity formulation robot is used for either high volume or high viscosity formulations. In general, the formulator system reads the library design from

Figure 4. Photograph of the mixing and coating work cell and its components: (a) left arm with a gripper; (b) right arm with a pipetter and a drawdown head; (c) mixer motor; (d) impeller rack; (e) mixing station; (f) source and destination vial trays; (g) in-deck balance; (h) pipet tip racks; (i) drawdown blade magazines; (j) gap-set station; (k) drawdown coating station.

database, and then utilizes positive displacement tips to aspirate and then dispense programmed amounts (per the target stoichiometry) of part A and part B from source vials into a third vial at the weighing station. Dispensing the higher viscosity part first (part A) allows any viscosity-related inaccuracy in the delivered weight to be compensated by adjusting the target weight of part B to minimize the error in stoichiometry. This automated adjustment results in a reduction of the error in molar ratio of two-part systems from ±2% to less than ±1%. Once the proper amounts of materials are added, the destination vial is brought to the mixing station where the sample is mixed with a polypropylene E


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Barcodes associated with elements of the library design are used to manage and track all coating samples within the database. A BenchCel/V-code system (Agilent Technologies, Santa Clara, CA) is used for automated barcoding. The BenchCel is an autosampler which will move and store the coating substrates in their stackable holders, and the V-code is a label machine. The barcode is applied on the side of substrate holder, and it is read by each of the automated systems for sample preparation and characterizations. Most of the automated coating characterization instruments use a BenchCel for autosampling and the unique barcode IDs for all data handling and identifications. The following sections describe an array of coating characterizations commonly used for industrial coatings. Some of them are related to the surface properties, such as tack and friction, and others are related to mechanical properties, such as modulus, hardness, impact resistance, and crosshatch adhesion. Tack and Friction. Tack force and friction coefficient of the coatings can be measured using the tack and friction station (Figure 6, TFS, Freeslate, Inc.) which is a HT probe-tack

Figure 5. Photographs of RCS mixing and coating components in action: (a) the left arm gripper picks up an overhead mixer motor with an impeller; (b) the drawdown head picks up a disposable blade from the magazine under it; (c) the gap is set by placing the drawdown head on the gauge block; (d) the drawdown head makes a coating.

impeller. The mixing time and rpm are controlled based on the library design. An induction time between mixing and coating can also be added in the library design. The mixture is then dispensed onto a coating substrate (4 in. × 3 in. in dimension) and drawn down with a stainless steel blade at a preset thickness (gap height). Substrates can be rigid (glass, metal, wood, plastic) or flexible (paper, plastic film); sometimes properties of the substrates such as surface energy, roughness, pretreatment and so on are variables of the studies. Typically, choice of substrate is dictated by the application (e.g., cold rolled steel for epoxy protective coatings); but some tests may require a particular substrate (e.g., glass for optical clarity measurement). Substrates can be mixed and matched based on the application and testing needs. In almost all cases, some sort of cleaning or pretreatment of substrates is required to provide valid and reproducible results. Each coating panel is held in a barcoded stackable microtiter-plate-sized picture-frame holder which provides a common coating handling format across all automated coating and testing stations. Typically, three coatings are made from one mixture to allow various characterizations. Because the mixing time often exceeds the time for all other operations, there are four mixing stations in this system to allow parallel mixing in order to eliminate this bottleneck and increase overall throughput, but their utilization requires sufficiently long pot life. Typically, two mixers are used for thermoset coating projects. The coated substrate is then either transferred back to its hotel (e.g., for ambient cure or solvent flash-off) or directly to one of the curing stations. This automated system contains ultraviolet and infrared curing (inside the curing workcell of the RCS), and a thermal curing station (see Figure 3). All curing schedules and associated robotic transfers are controlled according to the times programmed into the library design.

Figure 6. High-throughput approach to measuring cure speed using tack and friction station.

adhesion and dynamic friction testing apparatus. A Benchcel with a barcode scanner serves as an autosampler to move a coating substrate in and out of the measurement module. There are two robotic arms to perform substrate movement, aspirate/ dispense, and tack/friction measurements. The left arm is the testing portion containing two bending-beam load cells attached to an end measuring probe for measuring the normal and transverse loads. The probe is typically a stainless steel ball held by vacuum, and the robot uses a new ball for each measurement. For the probe tack measurement, the probe moves down to the coating until the desired normal force is reached, and is then removed from the coating perpendicularly at a programmed constant displacement rate after a preprogrammed dwell time. The tack force (adhesion) is determined as the force at the instant of separation of the probe from the coating. For the friction measurement, the probe moves down to apply a desired normal load, and then moves F


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ACS Combinatorial Science parallel to the surface for a distance at a constant sliding speed. The lateral and normal forces are monitored and used to determine the coefficient of friction by calculating the ratio of their average values. Typically three or more replicate measurements are done for each normal load. Tack and friction are not typically measured for industrial coatings because these are often not important properties of the final coating. However, the time-dependence of these properties can be used for cure profiling or drying profiling. Timedependent probe tack and friction tests using the TFS offer better quantification compared to various conventional coatings tests which use some measure of coating tackiness as a qualitative indicator of degree of cure or drying (e.g., cotton ball test). For typical thermoset coatings, friction and tack are initially low (corresponding to a low molecular weight and low viscosity liquid state) then go through a maximum (roughly corresponding to gelation or vitrification, although with some offset) and then decrease and level off (corresponding to a fully cured and/or vitrified state). Cure profiling via time-dependent friction measurements has some similarity to a dry time recorder but has advantages such as quantitative values (e.g., elapsed time and property value at the maximum and at other defined points such as half-maximum and final plateau) and less subjectivity. Furthermore, cure profiling in this manner provides some insight into viscoelastic property changes as well as gelation and vitrification phenomena during cure, although these property linkages are complex and not universal. Lastly, it is often the case that cure rate is slow enough relative to the time per measurement that cure profiling can be performed concurrently on a set of coatings by repeatedly cycling through a set of coatings at programmed time intervals. Further detail on cure profiling using the TFS is found below (Cure Speed section) and elsewhere.32 Impact and Crosshatch. Impact resistance (ASTM D2794, 2010) and crosshatch adhesion (ASTM D-3359, 2009) both relate to critical performance aspects of protective coatings. However, the conventional tests themselves are relatively crude due to variations in coating thickness, substrate selection, and operator experience and judgment. These two tests were automated to remove operator subjectivity by automating image analysis to provide consistent and quantitative outputs. A Gardner dart falling weight impact tester (BYK Gardner, Columbia, Maryland) was integrated with pneumatic controls, a BenchCel, and a six-axis robot to perform automated tests (Figure 7). There is also a custom imaging booth to capture images of the impact area before and after each impact for automated image analysis to determine % area damaged. The BenchCel served as an autosampler to send, receive and store coated substrates. In a typical run, multiple impact distances and directions (direct or indirect, i.e., front or back of a coating facing the dart) are programmed at the start, and the system carries out the tasks serially. The 6-axis robot picks up a coating substrate and moves it between the image station and impact anvil. The weight of the impact tester is moved up to the desired distance and dropped by pneumatic controls. The system goes through all preprogrammed distances, directions, and their imaging before moving to the next coating. The cross-hatch/tape peel adhesion test is a standard test commonly used to assess coating adhesion. Failure, when observed, can be adhesive (e.g., to the substrate or another coating layer) or, more rarely, cohesive failure of the coating. The method involves first scribing a cross-hatch pattern on a

Figure 7. Photograph of the automated impact tester: (a) BenchCel autosampler; (b) barcode scanner; (c) rotary stage; (d) coated substrate; (e) 6-axis robot; (f) impact tester assembly; (g) imaging booth. The bottom left inset is the photograph of a substrate held by the robot arm set for a dart drop impact.

coating with a hand tool, and then applying an adhesive tape on the scribed area, and finally pulling the tape away. An adhesive or cohesive “strength” is determined as the fraction (0−100%) of intact coating remaining in the cross-hatched area; however, the observation of any adhesive or cohesive failure is generally considered to be inadequate adhesion. It is a very popular test due to its simplicity and low cost, but tends to have poor consistency and quantification, especially as a manual test. An automated cross-hatcher was developed at Dow by mounting a Gardco 2 mm blade (PA-2056) on a crossbar above a Prior x-y stage (Figure 8). The coated substrate is placed on a rotary stage on the x-y stage. When the program is executed, the coated substrate is moved under the blade, and the blade comes down to cut into the coating under a fixed pneumatically applied pressure and the coating moves along the blade cutting

Figure 8. Photograph of the automated crosshatch station: (a) cutter head assembly; (b) cutting blade; (c) coated substrate; (d) rotary stage; (e) x−y stage; (f) cleaning stage with metal wire brush; (g) vacuum hoses. The inset on the top left shows the close up view of the cutting blade. G


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Coating Imaging. Coating image acquisition and analysis is often handled manually, which leads to a tremendous variability of conditions and hence unreliable results. Standardizing imaging conditions and imaging protocol enables reproducible image acquisition and more easily automated image analyses. These factors allow researchers to compare results from different days, operators, and laboratories with minimum variability in image quality. A coating image station with multiple lighting sources, cameras, and a calibration standard was built and integrated with a BenchCel. Computer protocols are used, so that each project acquires images of stacks of coatings using a fixed imaging condition in one automated run. The calibration standard resides on the sample holder, and it becomes part of the coating image in every camera shot, which plays a critical role in achieving the highest reproducibility and comparability in automated image analysis. The calibration standard is a photographic print of six saturated colors: red, green, blue, cyan, magenta and yellow and five neutral gray values: saturated black, saturated white and three intermediate grays. This system is frequently used to provide images for assessment of compatibility, crosshatch adhesion, impact resistance, chemical resistance, corrosion resistance, and sag and leveling. At a minimum, images serve as a record of visual coating quality for which questions can arise long after the actual samples have been discarded. White Light Interferometry. Characterization of surface microstructure and topography is an important aid to understanding various surface phenomena on coatings. For instance, surface topography can be used to study incipient photochemical degradation of epoxy networks.39,40 One way of analyzing the surface topography of a coating is by optical techniques such as white light interferometry (WLI). A white light interferometer is an optical microscope equipped with an interferometer near the objective lens.41 The interferometer is positioned on a stage capable of z-directional motion. Illumination from white light is reflected from the reference mirror of the interferometer and is combined with the light reflected from the surface of the coating to form fringes. These fringes represent the topography of the surface. The intensities of these fringes are converted back to a Z-scale dimension to map the surface of the coating Z(X,Y). For moderately rough surfaces (Ra = 500 nm), WLI and stylus techniques (e.g., atomic force microscopy height images) are in close agreement.42 However, WLI offers much faster scan speeds relative to stylus techniques, such as AFM. WLI is therefore a rapid and reliable technique to characterize scratch, mar, and wear profiles as metrics of coatings durability and to examine many other phenomena. Measurement of Color, Gloss, and Thickness of Coatings. Gloss is itself an important performance metric for many if not most coating applications and can also be used as a proxy for other properties such as wear and compatibility of resins and curing agents in multicomponent coating systems. For example, coatings made from a poorly compatible epoxy resin-hardener pair exhibit low gloss due to surface inhomogeneity. Similarly, epoxy coating surfaces that show high degree of amine blushing also display suppressed gloss.43 Color measurements can be used to evaluate initial color (e.g., tint strength, color developed during cure) or changes in color after some sort of environmental exposure (e.g., staining, “yellowing” of the coating after UV exposure or an accelerated weathering test, etc.) Thickness is generally important to measure, whether to verify the performance of the coating

direction for a desired distance, and then the blade moves up to release the coating. This action is repeated until the programmed cutting pattern is done. Coatings are then rotated by 90° to make a series of cross-cuts. The typical throughput is about 1 min per substrate. Finally, a pressure-sensitive tape is manually applied over the cut region and then removed. Adhesion of the coating to the substrate is determined according to ASTM D3359.33 The advantages of this system are (1) the cutting pressure/depth, and also the locations are fixed and reproducible, totally eliminating operator-dependent variations, and (2) the reproducible locations allow using automated image analyses to determine the “adhesion strength.” Microindentation. Microindentation refers to penetration of a measurement head (or indenter) with a known shape and physical properties into the surface of a sample whose properties are unknown. A microindenter is capable of applying a controlled load on the order of milli-Newtons and measuring the indentation depth on the order of few micrometers. Fundamental properties of a coating, such as Martens hardness, elastic modulus, compliance, and others can be measured using this technique. These fundamental properties can be correlated to application specific properties. For instance, scratch and mar resistance of a polymeric coating can be correlated to its indentation hardness.34−36 Similarly, wear resistance can be modeled using indentation parameters.37 Microindentation also allows quantification of coating properties that are precluded by conventional techniques. For instance, solvent resistance of a coating can be assessed by comparing the hardness prior to and after solvent treatment. A Fischerscope HM2000 (Helmut Fischer GmbH) microindenter equipped with a Berkovich type indenter is integrated with a Benchcel by means of a 6-axis robot (shown in Figure 9),

Figure 9. Photograph of a high-throughput microindenter: (a) microindenter head and its housing; (b) x−y sample stage; (c) coated substrate; (d) 6-axis robot.

which enables automated transfer of coatings between the microindenter and the Benchcel. The fully automated system can perform indentation measurement on multiple coating plates without user intervention. Moreover, automation allows in situ monitoring of coating properties. For example, the rate of cross-link density increase of thermoset coatings can be evaluated by measuring hardness of a coating over time.38 However, this technique is only useful when the coating becomes tack-free so that no residue is left on the indenter tip. H


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ACS Combinatorial Science process vs target thickness and thickness uniformity or because certain properties can be sensitive to thickness (e.g., impact resistance.) An automated color gloss thickness (CGT) instrument (Freeslate, Inc.) consisting of a BenchCel, barcode reader, a gloss meter (micro-TRI-gloss μ, BYK-Gardner USA, Columbia, MD) and a color meter (USB4000 with integrating sphere, Ocean Optics, Dunedin, FL) is used to characterize gloss, thickness and color. The micro-Trigloss Meter is capable of simultaneous gloss (ASTM D523) and thickness measurement (ASTM B499). Automation allows measurement of color, gloss or thickness on multiple plates without user intervention. Plates are transferred from the BenchCel to the measurement stage which is equipped with a vacuum chuck to hold the coating flat and prevent motion of the coating during measurements. An aluminum backing plate with an array of holes is sometimes included underneath coatings on flexible substrates (e.g., paper, flexible metal sheet) for uniform distribution of vacuum pressure. Measurements are taken at specific locations on the coating as programmed prior to the run, which allows changes to the color, gloss or thickness in a particular locations to be monitored (e.g., at different aging times, before and after chemical exposure, etc.) Thickness measurements are made by an eddy current probe. A high frequency electric field is passed through a metal coil which induces a magnetic field in a nonferrous conductive substrate (e.g., aluminum). The eddy currents generated due to this field have an associated magnetic field. These magnetic fields change the electrical impedance of the coil to an extent which depends on coating thickness. Changes in thickness can be used to measure phenomena such as scrubbing-induced wear, physical or chemical degradation, and ablation.

and dispense actions. The robotic actions are repeated eight samples at a time until all samples are measured. A typical automated viscosity measurement of 96 samples at a single aspiration rate takes 10−40 min depending on the viscosity and material flow rate. The pressure versus time data is extracted and converted to viscosity. Methods based on multiple aspiration rates have also been developed for non-Newtonian coatings. Measurements can be done at thermostated ambient temperature or at elevated temperature. For pot-life assessment, the viscosity measurements are repeated eight (channels) at a time for the same set of samples (typically 8−24) at a preset time interval and total time. For the following epoxy pot-life example, nine sample sets of eight per set were measured over 2 h with time interval of 108 s, providing 72 points per curve for good resolution given the reaction kinetics of these coatings. Figure 10 shows the pot life

APPLICATION EXAMPLES IN INDUSTRIAL COATINGS Viscosity and Pot-Life. Dow developed an automated rheology measurement system jointly with Anton Paar, which consists of two 6-axis robots serving a rheometer (MCR 301, Anton Paar GmbH, Austria) with cone and plate fixtures. One robot works on the sample handling, and the other works on the handling and cleaning of the fixtures. This system is wellsuited for water-borne industrial coatings but not for solventborne and thermoset coatings due to issues with gelation or vitrification within the measurement parts. Therefore, automated viscosity measurement of solvent-borne and reactive systems requires the use of disposable parts or a mechanism to clean the parts before gelation occurs. An automated viscosity and pot-life measurement system for solvent-borne reactive coatings has been reported by Majumdar et al, which uses an automated liquid formulator mounted on one robotic arm and a viscometer mounted on the second robot arm.44 At Dow, we have developed an automated liquid-handler-based viscosity method, referred to here as “TADM viscosity”.45 For this approach, the Hamilton liquid handler (mentioned in the formulation section) with disposable tips is used. TADM stands for Hamilton’s total aspiration and dispense monitoring, which involves a differential pressure sensor on each pipetting channel to monitor the pressure change inside the pipet tip during aspiration and dispense processes. The robot liquid channel manifold picks up eight disposable plastic tips and moves to the plate of sample vials, performs aspiration and dispense actions on the samples, and then discards the tips at the waste station. Pressure is recorded as a function of time during the aspiration

Figure 10. Pot-life of various epoxy formulations based on automated viscosity measurements. Each cell contains one curve (black trace) of viscosity (right y axis) as a function of time (x axis) and the shaded gray bar is the pot-life (left y axis), defined as time to reach viscosity = 104 cP. The labels of the axes (expanded) show the fixed range applicable to all cells.

■

result of 72 blends of epoxy resins mixed with an ethylene amine hardener at 1 to 1 epoxy to amine stoichiometric ratio. This figure contains more than 5000 automated measurements. The curves are the viscosity in cP (right y axis) as a function of time (x axis), and the gray bars are the calculated pot-life in minutes (left y axis) from the curve. In this example, pot-life is defined as the time to reach viscosity of 104 cP, based on curves fit to the data. The four empty cells in Figure 10 are for samples where initial viscosity was >104 cP. Unlike this example, common practice is to define pot-life as the time to double the initial viscosity. However, in this parallel pot-life measurement the samples are typically not mixed simultaneously which can introduce error in the “time zero” viscosity and thus in the pot-life. Furthermore, a formulation at twice its initial viscosity can in some cases still be useful for the end application. The choice of pot-life definition is based on the application requirements. There are also several practical factors that can impact the reported pot-life. First, with small sample size there is very limited acceleration due to self-heating, which can result in significant differences compared to pot-life measured by conventional tests on much larger samples. On the plus side, this lack of self-heating due to small sample side results in I


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Figure 11. Pot-life measurement of water-borne epoxy formulations. (a and b) Merged images of coatings as a function of pot-time for pigmented and clear coats. (c) Gloss as a function of pot-time, stoichiometric ratio (E:A = epoxy:amine), PVC, and volume % solids (VS) for two different hardeners 1 (red) and 2 (blue). Curve shade indicates coalescent level, lower (lighter) vs higher (darker).

epoxy formulations. The photographs at the left are images of coatings prepared from a series of pot-life samples for (a) pigmented and (b) clear coats. The formulation study is based on two water-soluble polyamine hardeners (hardener 1 in red and 2 in blue) and one water-borne epoxy (a dispersion of a modified bisphenol A based “1”-type epoxy) at three epoxy:amine (E:A) stoichiometric ratios (E:A = 1:1.8, 1:1, and 1.8:1), two solids levels (35 and 45 vol %), two pigment volume concentrations (PVC = 0, i.e., clear coat, and 18%), and two volatile coalescent levels (high and low). The light color curves represent the low coalescent level. Results show that pigment increases the pot-life significantly while volume solids has little effect on pot-life. It was also found that hardener 1 generally yields longer pot-life than hardener 2, but this effect of hardener-type is reduced as the epoxy:amine ratio is reduced. Pot-life dependence on hardener type, coalescent level, and E:A ratio is consistent with expected effects of these variables on the extent to which the hardener partitions from the aqueous phase into the epoxy particles: higher hardener concentration in the epoxy leads to shorter pot-life due to greater intraparticle reaction interfering with film coalescence. For shorter pot-life formulations, the RCS is used for similar coating quality versus time experiments. The robot prepares one formulation, and then coatings are made continuously from the same formulation until the predetermined end time is reached, before mixing the next formulation and starting the repetitive coating process. Cure Speed. Cure of reactive coatings is a complex timedependent process, involving both chemical reactions and changing physical properties as the material structure evolves. Fortunately, there are many techniques which are well-suited to automated repetitive measurements. Chemical reaction can be conveniently followed via spectroscopy or thermal analysis; and physical properties can be followed via rheology, dynamic mechanical spectroscopy, or, perhaps most conveniently for CHT workflows, probe techniques such as microindentation or probe tack. For coatings applications, the chemical and physical degrees of cure are both important: full reaction of the functional groups generally confers better properties (e.g.,

viscosity changes which can be more directly related to isothermal cure kinetics. Second, there is negligible additional mixing once the measurements start, which is generally only an issue if inhomogeneity develops during the measurement. Third, there is some material loss in every aspirate/dispense action cycle since some portion of the viscous material remains with the pipet tips, but again this is only an issue if the sample is inhomogeneous (which is its own issue). Another approach sometimes used to determine pot-life, especially for reactive emulsion and colloidal coatings, is based on coating quality as a function of pot-time, where a poorer coating results if reaction in the colloidal particles leads to poorer coalescence over time. In such colloidal systems, the reaction is often not manifested in significant or readily interpreted changes in viscosity. Typically, an operator mixes a formulation and makes coatings at fixed time intervals. The potlife is then determined by the degradation in appearance or gloss of the coatings. When gloss starts to drop steeply, the potlife is reached. As a manual method, this approach is laborintensive. We developed a robotic method utilizing the coating station to improve the overall throughput and data quality. If the pot-life is several hours, the coating station can make coatings of a set of formulations (e.g., 6, 9, or 12) in serial, and repeat at a fixed interval. Typically, one coating per formulation per every 0.5 or 1 h is sufficient, and four coatings from four different formulations can be applied onto one substrate. In this case, the mixing of each formulation is timed so it is incorporated into the first running cycle without causing delays. When the cycle time is up, the coating station starts to make the next coating for each formulation, and the cycling continues until the predefined cycle number is completed. Gloss of the coating is measured with the CGT station after 1 day (or 7 days depending on the cure schedule), and the results are plotted for pot-life determination. Images of the resulting coatings are also obtained and processed using an automated imaging station and a custom program to combine the sequence of coating images from the same formulation into one image for record keeping. Figure 11 shows an example of using this methodology to determine pot-life for water-borne J


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has no upper limit on the total cure time and also can be used for more rapidly curing systems. For a typical epoxy-amine coating, a characteristic curve is seen when COF values are plotted against the cure time (e.g., the curve labeled “cycloaliphatic amine” in Figure 12). The

chemical resistance and environmental durability); whereas the time needed to reach certain physical states, for example, tackfree or through-hard times, is of great practical importance to the applicator. Cure speed is a general term which can refer to either chemical reaction rate or rate at which some property develops. Unfortunately, there is no general correlation of the viscoelastic state of the coating (i.e., physical state) to the chemical degree of conversion; and even within each of these two broad property classes, the correlation of one property to another (e.g., hardness vs degree of cross-linking) is not universal. Therefore, the choice of techniques needs to made carefully in line with each particular application and chemistry; and having multiple options available for cure speed and cure extent measurements is important to handle diverse chemistries and applications. Although this section is primarily focused on an automated probe-based coefficient of friction (COF) method for following changes in the physical state of the coating, it is important to note that “physical cure” measurements such as this generally need to be supplemented with measurements of the chemical degree of cure by spectroscopy or DSC to ensure that, for a particular coating formulation, “full cure” as defined by having reached a specified viscoelastic state also corresponds to a fully reacted state. Therefore, this section also includes a brief discussion of approaches used for measuring rate and extent of chemical reactions in the coating. Traditionally, a BYK Dry Time Recorder (ASTM D5895, “DTR”) is used for measuring cure speed by tracking changes in the physical consistency of the coating. In this technique, the coating is applied on one of the six glass substrates using a film applicator. A needle is dragged slowly through the coating from one end at a constant speed for 24 h until it reaches the other end. As the needle traverses through the coating, it creates a scratch, the morphology of which depends on the stage of cure. Although, the DTR is a simple technique that is widely used in the coatings industry, there are several practical challenges associated with this technique. First, detecting transitions in the scratch morphology is not trivial because many of these transitions in scratch morphology are ambiguous. As a result, the various stages of cure cannot be determined unequivocally. Also, certain transitions may not occur in all cases, which makes interpretation of the cure stage difficult. Second, the variability of this technique can be very high. Third, the instrument is limited to measuring cure speed for up to 24 h. Recently, a new technique was developed at Dow to evaluate cure speed of reactive coatings by measuring the coefficient of friction (COF) of the coated substrate as a function of cure time.32 The COF is measured using the tack and friction station with the probe being a 9.525 mm diameter stainless steel ball (shown in Figure 6). This technique is therefore roughly analogous to the DTR but brings a number of advantages. This technique is quantitative and provides more accurate and precise comparative assessment of cure speed compared to the observation-based DTR. Because the tack and friction station is integrated with the reactive coating station and the thermal curing ovens (shown in Figure 3), drawdown of the coatings, controlled cure, and COF measurements can all be performed without any user intervention. The thermal curing ovens, which include both standard bake and controlled temperature and humidity conditions (e.g., subambient plus high humidity), provide flexibility in cure conditions relevant to field application of epoxy coatings. Moreover, unlike the DTR, this technique

Figure 12. (a) Effect of hardener chemistry on coefficient of friction. COF values are normalized by the maximum COF value for each coating. (b) Correlation between dry-hard time on the recorder and cure time to reach peak COF value.

basic phenomenology is a low COF at the start of cure, a rise in COF through a maximum, followed by decreasing COF which tends to plateau at sufficiently long times at a COF value intermediate between the starting and maximum COF values. Formulations of epoxy-amine and other reactive coatings can vary widely (e.g., the functionalities and equivalent weights of components and the fully cured glass transition temperature Tg), therefore the link of the basic features of a typical curve to the underlying changes in molecular structure and viscoelasticity is complex and nonuniversal. In particular, changes in viscoelastic properties associated with gelation/cross-linking and vitrification can have similar effects on the COF. Other factors confounded in this measurement are changes in probecoating adhesive interaction and changing depth of penetration and probe-coating contact area, especially past gelation and/or vitrification. At short times, the low COF is clearly a consequence of the low viscosity of the largely unreacted still-liquid coating. As reaction proceeds, molecular weight grows and viscosity increases, resulting in an increasing COF. For well-designed ambient cure epoxy-amine coatings (fully cured Tg ≈ 50 °C), it is unlikely that this increase in COF is due to a viscosity increase associated with incipient vitrification because components are generally chosen such that vitrification does not occur until high extents of reaction. Results of dynamic mechanical K


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consumption or formation of specific functional groups. Lastly, it is often important to determine whether a plateau in either chemical or physical extent of reaction corresponds to essentially complete chemical reaction or is instead due to the marked slowing of reaction and physical property changes due to vitrification. Common methods to answer this question are the levels of functional groups remaining (FTIR or Raman) or the Tg and residual exotherm (DSC). Scratch Resistance. The tack and friction station (shown in Figure 6) can be modified to use a 100 μm radius tungstencarbide tip in place of the stainless steel ball bearing probe, which can then be used to apply scratches on the coating surface at a specified normal load (shown in Figure 13). Here

cure experiments on three typical epoxy-amine coatings are consistent with these arguments and confirm the approximate correspondence of the peak COF time to the gel time. Further into the cure progression, the COF value peaks, presumably associated with the diverging viscosity just short of gelation. For ambient cure epoxy-amine coatings, the decrease in COF past this maximum is presumably associated primarily with a relatively rapid reduction in penetration and adhesion beyond the gel point. Hardening due to vitrification would have similar effects although a slower time dependence. The COF continues to fall due to further increase in cross-linking, typically with some contribution from vitrification as well. Ultimately the COF reaches a plateau value when the extent of cure reaches a limiting value, whether truly full cure or a vitrification-limited apparent full cure. Three coating formulations were made consisting of an experimental liquid epoxy resin combined with three hardeners: a Mannich base (Polypox H 015, SCG Paper), cycloaliphatic amine (Polypox H 488L, SCG Paper), and a polyamide (Versamid 140, BASF). The coating with polyamide hardener has the slowest cure speed, as evidenced by the longest time (∼10 h) to reach the COF peak; the broadest peak in the COF curve; and failure to reach a plateau in 24 h. The coating with Mannich base hardener has the fastest cure speed. For this coating, the peak in COF appears to have been reached even before the first COF measurement at 2 h, and the plateau COF is reached in 5 h. The coating with cycloaliphatic amine hardener has intermediate cure speed compared to the other two, and best shows the typical phenomenology of this measurement. For this coating, the COF peak is at ∼8 h and plateau COF is reached at ∼15 h. In order to correlate the COF-based HT method to the dry time recorder method, we plotted the dry-hard time measured on the DTR against the time to peak COF. Data was collected from six coatings made from the same epoxy resin but different hardener systems. A line with slope of 0.973 can be fit to the results (Figure 12), which shows that the time to reach the peak COF is more or less equivalent to the dry-hard time on the dry time recorder. This is an important bridge between the HT test and the historically used conventional test. Based on the results of dynamic mechanical experiments mentioned above, both peak COF time and dry-hard time also correspond approximately to the gel time. More recently, we have used this COF-based cure speed technique to screen various accelerators for low temperature cure of epoxy-amine coatings46 and we continue efforts to better understand the relationship between changes in the COF and the viscoelastic property changes as coatings cross-link and vitrify during cure. As mentioned above, it is generally dangerous to rely on physical cure measurements alone; therefore, measuring the extent and rate of chemical reactions is also important. Analytical techniques utilizing vibrational spectroscopy (FTIR, Raman)47,48 and differential scanning calorimetry (DSC) are commonly used. Because these techniques generally operate in a sequential measurement mode (single measuring head), the temporal resolution has to be balanced with the number of coatings per substrate. In order to utilize the high throughput spectroscopic tools, a patented 4 × 6 coating application apparatus was developed to make 24 coatings simultaneously on one substrate,49 and the substrate with coatings library array can be placed on automated x−y stages for FTIR or Raman analyses as a function of time. The degree of cure is typically monitored through changes of vibrational peaks due to the

Figure 13. (a) Tack and friction station with a blunt tungsten-carbide tip used for application of scratches at specified normal load. (b) Percentage gloss loss of clear coatings after application of scratches at various normal loads.

we give an example of a procedure for evaluating scratch resistance, which starts by making nine patches of parallel scratches at various normal loads between 50 and 200 g (1.0 cm × 1.3 cm area, distance between adjacent scratches = 0.33 cm). Measuring the change in gloss provides an easy, rapid, automated, quantitative, and application-relevant way to assess the extent of visible damage. For the example, 60° gloss was measured on these patches using the color gloss and thickness (CGT) station and the percentage drop from the average gloss of the original coating was used as a quantitative metric of the relative scratch resistance of different coatings. The four coatings of this example are a 2K polyurethane clear coat (coating A) and three different acrylic latex coatings (coatings B, C, D). Figure 13 shows the gloss loss of the coatings, expressed as the percentage drop with respect to the original coating gloss, as a function of the normal load. As the normal load increases, the tungsten-carbide tip indents deeper into the coating (i.e., deeper scratches) resulting in higher loss in gloss. Coating A L


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ACS Combinatorial Science (2K polyurethane clear coat) shows the lowest percentage reduction in the gloss and therefore best scratch resistance; and coating C shows the highest reduction in gloss and therefore poorest scratch resistance. Comparing coatings B and D, an interesting load-dependent behavior is observed: coating B shows better scratch resistance at lower loads (below ∼100 g) whereas coating D shows better scratch resistance at higher loads. This methodology can easily be tailored by choice of the probe and load to mimic the specifics of scratch and mar insults that are experienced by coatings in different end-use applications, and is readily adapted to aid in developing selfhealing coatings and other wear-resistant technologies. Automated microindentation can readily be coupled with HT scratch resistance to provide insight into how scratch resistance relates to some fundamental coatings properties such as modulus, hardness, and the ratio of elastic to plastic work for different coatings chemistries. Scratch resistance, as measured by the gloss retention of the coating, can often be correlated to the elastic properties of the coating as measured by microindentation. In a typical microindentation experiment, the indentation load is applied at a certain rate to a specified maximum load or until the indenter reaches a certain depth within the coating. The maximum force is held for a specified time to allow the indenter to creep further into the coating. Finally, the indentation force is released at a specified rate until zero force is reached. The resulting force vs displacement curve (Figure 14) can be used to deduce elastic properties of the coating, such as elastic modulus and stiffness using models proposed by Doerner and Nix,50 Oliver and Pharr,51 and Field and Swain.52 The following example explores the relationship between the elastic portion of work (EPW) from microindentation and scratch resistance for a library of polyurethane and acrylic coatings. EPW is the fraction of the work of indentation which is elastically recovered upon unloading; and scratch resistance for this study is the percentage reduction in gloss after application of parallel scratches at 100 g normal load. One set of coatings was prepared from various polyurethane dispersions (PUDs) which were comprised of a polyester polyol backbone with varying amounts of ionic salts added to disrupt the hydrogen bonding. The PUD coatings had significant variation in their elasticity and hardness. A second set of coatings was prepared from acrylic latexes containing butyl acrylate (BA), methyl methacrylate (MMA), methacrylic acid (MAA), plus varying levels of a ureido functional monomer. The ureido group can be cross-linked by reaction with dialdehydes, which allowed for significant variation in cross-link density in the set of coatings. The EPW obtained from microindentation was plotted against the percentage gloss reduction (Figure 14). The majority of the coatings with better scratch resistance (gloss loss 0.3). However, the results also suggest that factors other than EPW are important and perhaps dominant in determining scratch resistance, based on the observation that the trend line consists of two portions, each associated with a different polymer type, which show virtually opposite relationships between EPW and scratch damage: (a) low scratch damage despite large variation in EPW (the vertical portion, PUDs) and (b) large variation in scratch damage despite little variation in EPW (the horizontal portion, acrylic latexes).

Figure 14. (Top) Typical load−displacement curve obtained for loadunload microindentation of a polymeric coating. (Bottom) Elastic portion of work plotted against percentage gloss reduction after application of parallel scratches at 100 g normal load for coatings made from polyurethane dispersions (blue) and acrylic latexes (red).

elastic portion of work =

work recovered during elastic relaxation (B) total indentation work (A + B)

Coating Surface Defects. Coating appearance is highly interdependent on the coating formulation, the surface onto which the coating is applied, the application process itself, and the environmental conditions during cure. While some defects such as orange peel simply affect the appearance of the coating, other defects such as cratering can also impact a coating’s protective properties. Coating defects can have various potential causes and it may not always be easy to determine the cause or causes through visual analysis alone. Therefore, smart use of quantifiable imaging techniques can help in identifying the cause of the coating defect and to monitor changes in performance during efforts to understand and resolve the problem.53 White light interferometry (WLI)41,54 is one such technique which allows rapid imaging and quantification of coating defects. In this section, we summarize a study carried out to reduce amine blushing in an epoxy coating formulation through modification of the solvent package. Amine blush occurs because of the reaction of primary amines with atmospheric carbon dioxide to form organic ammonium carbamate salts at the coating surface.39 Amine M


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ACS Combinatorial Science blushing is a major concern for epoxy coatings applied as primer or base coats because it affects overcoatability and disrupts interlayer adhesion. In addition, the loss of amines to carbamate formation impacts stoichiometry leaving the coating undercured which in turn affects coating performance. The amount of amine blush can be monitored by measuring change in the surface roughness as the coating cures. The influence of solvent and the induction period (time between mixing of the reactive components and coating application) on the amine blushing behavior was studied. Six solvents (listed in Table 2) Table 2. List of Solvents Evaluated in Epoxy Coating System to Mitigate Amine Blush solvent A B C D E F a

type aromatic alcohol glycol ether acetate aliphatic acetate aliphatic alcohol glycol ether glycol ether acetate

boiling point (°C)

relative evaporation ratea

viscosity (mPa·s at 25 °C)

203.2

0.009

5.5

145.8

0.32

0.8

126

0.99

0.69

108.1

0.63

4

170.6

0.079

3.3

191.6

0.04

1.6

n-butyl acetate = 1.

with a range of structures, boiling points, and evaporation rates were selected and added into the coating formulation in the part A side at 10 wt % (based on total formulation weight). For every composition, two coatings were prepared. One coating was made immediately after the reactive components were mixed, and a second coating was made after a 30 min induction period. Coatings surfaces were analyzed using a Veeco NT9080 White Light Interferometer operated under the vertical scanning interferometry (VSI) mode. The VSI mode uses broad spectrum illumination and calculates the point of best fringe contrast to give the relative surface height. The white light interferometer is equipped with 5× and 50× objective lenses. Images were taken using a Z-scan length of 35 μm to scan the surface. “Stitching” mode was applied to get images of desired sizes. The induction period and the choice of solvent in the coating formulation dramatically influence amine blushing behavior, as illustrated in Figure 15. Induction time reduces the amount of amine blush by allowing time for the amine to react with the epoxy resin before exposing a large surface area to atmospheric carbon dioxide. This effect is reflected in the surface roughness values obtained from the WLI images as seen in Figure 16. Certain solvents help compatibilize the amine and the epoxy phases thereby reducing the overall amine blushing. For instance, solvent A, an aromatic alcohol with relatively higher boiling point, lower evaporation rate and higher viscosity, showed the best improvement in amine blush reduction. However, a few solvents promote amine blush by reducing the viscosity of the system and/or preferentially solubilizing the amine (or carbon dioxide) thereby aiding the carbamateforming reaction and/or diffusion of the reactants and undesired products to the surface. This is evident in the case

Figure 15. White light interferometry images of clear epoxy/amine coatings made from formulations containing 10% solvent as indicated on the left. For each formulation two induction times: 0 and 30 min were used to make the coatings. All images are 3 mm × 4 mm. Z-scale (0 ± 3 μm) is indicated by the color chart on the bottom.

Figure 16. Surface roughness of clear coatings by white light interferometry in nm.

of solvents B and C, which have relatively higher rate of evaporation, lower boiling point, and lower viscosity. It is also interesting to note that solvents that are acetates do not reduce amine blush as effectively as glycol ethers or alcohols even at longer induction times. This is true even if the acetate solvent N


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Figure 17. Yellowness (orange color) and gloss at 60° (blue color) as a function of QUV hours of various epoxy/hardener coatings.

Gloss is indicative of the surface quality of a coating. In this experiment, once the gloss started to drop for a coating, it was found to drop quickly over the next 250 to 500 h. The yellowness of a coating is indicative of thermal and/or UV degradation. However, the yellowness over time tends to increase first and then decrease. The steady decrease after initial increase is due mainly to chalking of the material, which is typically accompanied by a decrease in gloss. Therefore, to truly judge the weatherability performance, both gloss and yellowness are needed. For a good weatherable coating, both gloss and yellowness should stay constant for many thousands of hours of QUV exposure. The initial gloss values shown in this clear-coat study are larger than 100, which is due an additional contribution from reflection at the metallic substrate-coating interface. Aliphatic epoxy resins showed generally superior weatherability compared to aromatic epoxy resins. The liquid epoxy resin (LER, bisphenol A aromatic epoxy resin) showed fast decrease in gloss as a function of QUV exposure across different hardeners. The aliphatic epoxy resins show much better weatherability with low decrease in gloss and low increase in yellowness, with the linear difunctional 1,6 hexanediol diglycidyl ether having the worst performance in this class.

has high boiling point and low evaporation rate (e.g., solvent F). Therefore, both structure of the solvent and the physical properties play a role in reducing amine blush in epoxy coatings. Accelerated Weathering. Tests such as weathering are usually not considered high throughput because the elapsed time during coating insult can be lengthy. However, analysis can be considerably accelerated and improved using high throughput tools. Cured coatings on chromated aluminum panels (Type AL, Q-PANEL, Q-Lab Corp., Westlake, OH) were placed into a QUV unit (model QUV/SE, Q-Lab Corp., Westlake, OH). The samples were exposed to weathering cycles with the following repeated steps: (a) UVA bulb UV irradiance at 0.68 W/m2, 60 °C for 4 h, and (b) condensation at 50 °C for 4 h. Cycles are repeated for 2000 h total sample exposure time or longer. This procedure follows ASTM G-53. The coatings were measured for color and gloss on CGT prior to the weathering test, and then they were taken out for color and gloss measurements approximately every 250 h. The yellowness and gloss values are plotted as a function of QUV exposure hours to determine their weatherability performance. It is not uncommon to find that accelerated weathering tests do not correlate well to real world performance, but the controlled environment at least provides consistent conditions. This allows researchers to compare the coating weatherability across conditions and formulation variations; and certain tests such as the ASTM test used here have been found to have at least reasonably good correlation to real world exposure. In this test, the positions of the coatings inside the test chamber are moved every 250 h so the position factor in the result is randomized. The integration of an automated measurement system with a database is a powerful addition to these tests. All data and time associated with each sample are tracked in a database. Newly collected data can be extracted directly and performance charts updated automatically. Figure 17 shows examples of gloss and yellowness of epoxy coatings as a function of hours of QUV exposure. When a coating fails (delamination with significant loss of coating material or degradation to very low gloss values) it is removed from the test, and a “−1” value is recorded for its gloss and yellowness. Therefore, “−1” in the curves in the figures signals the coating had failed at that exposure time.

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EXAMPLES OF INTEGRATED HIGH-THROUGHPUT WORKFLOWS This section provides illustration of one of the major organizing principles behind Dow’s CHT capabilities for industrial coatings, namely the flexibility to combine tools into custom workflow configurations. Product development typically involves synthesis and characterization of a variety of different compositions. In addition, characterization typically entails measurement of a range of properties to assess the balance of performance rather than analysis of a single property. It is therefore necessary to have an integrated high throughput workflow for rapid development of prototype products and speedy commercialization. Customized integrated HT workflows are enabled by standardization of various aspects of the high throughput tools such as coating size, bar-coding to identify and track samples, and the Benchcel plate handling system with associated coating substrate carriers. Such standardization allows characterization of a single coating on O


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Figure 18. Example of an integrated high throughput workflow for development of epoxy-amine coatings.

the workflows described in previous sections. Finally, the characterization data plus all of the input variables such as composition and process parameters is modeled using statistical analysis software or used as input for other models. This overall process is typically repeated with new experimental designs based on findings from earlier iterations until the objectives of the study are met.

multiple HT instruments. The integrated workflow is a process consisting of several subworkflows. While steps within a subworkflow need to be run in a sequential manner, every subworkflow is an independent process. Therefore, these subworkflows are routinely operated in parallel to further accelerate product development. An example of an integrated workflow for development of epoxy-amine coatings is shown in Figure 18. In this case, there are three subworkflows: (1) hardener synthesis and formulation; (2) coating and curing; and (3) application performance testing. The hardener synthesis and formulation workflow starts with the experimental design directed at composition, typically setup using Library Studio software (Freeslate, Inc.). Hardeners for epoxy systems are themselves formulated products, typically involving chemical reactions as well. The hardeners are first formulated using a liquid handling robotic system, such as the high viscosity formulator, which is capable of making 48 formulations per run from up to 10 separate components. The reactive chemistry is carried out by transferring the array of formulated hardeners to a rotary oven, which simultaneously mixes and heats the components. These hardeners are then used to formulate coatings using epoxy resins and other additives. Similar to the hardener synthesis workflow, the coatings workflow is also initiated by an experimental design using Library Studio, which contains the coating compositions. Important process parameters such as the induction time, mixing time, and mixing speed are also fed into the experimental design through the Automation Studio software, which serves as a set of instructions to the Reactive Coating Station. The RCS sequentially processes the coating compositions with associated process parameters. After the epoxy-amine coatings for a given composition are drawn down, the plates are transferred to an adjacent rack (for ambient cure) or to one of the thermal, IR or UV curing stations. Characterization of cured coatings is then performed using

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DATA VISUALIZATION AND MODELING Using statistical software to aid in design of experiments and subsequent data modeling is an essential part of industrial coatings CHT research. This section presents two examples which represent two of the most common types of problems we encounter. One example is a large combinatorial study assessing novel reactive diluents vs existing commercial reactive diluents for the influence of their composition and use level on coating property balance across a wide swath of formulation space. The other example is a study aimed at developing a formulation for a specific application. The second type of study is readily handled by a standard statistical experimental design and analysis approach. However, for studies as in the first example, a general challenge is how best to utilize the array of experimental design, modeling, and visualization approaches that are available to answer the types of bigger questions which typically arise relative to assessment of new materials for coatings: Is this new material advantaged? If so, how and where? Which variant of the new material is best? What are the formulation rules to guide customers? Experimentally addressing such questions in a comprehensive way requires large CHT studies which generate tremendous amounts of data. Even with careful statistical analyses in hand, it is often a struggle to translate the results into answers to the questions listed above, where detail matters and where the answers must be in a format readily understood by colleagues and customers. We have found that the ability to visualize and interact with the data P


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ACS Combinatorial Science itself is an important adjunct to statistical analyses and modeling. Hopefully the examples which follow spur new ideas in this gray area of how best to reduce and translate rich CHT data sets into useful, practical knowledge. Using CHT approaches to tackle big-scope problems in industrial coatings naturally leads to large complex data sets. Due to the combinatorial nature of coatings formulations plus the large number of performance dimensions of industrial coatings and their often antagonistic requirements, modeling can be challenging. For instance, a typical coating project requires multiple property measurements such as pot-life, viscosity, hardness, impact resistance, adhesion, gloss, color, and chemical resistance. The chemical resistance itself can include many different chemicals, so the overall number of responses can easily range from 10 to 20. Balancing these performance requirements such that acceptable properties are achieved in each dimension is a challenge, and it can be difficult to properly weight properties to yield a meaningful overall index of performance Furthermore, some measurements yield X−Y data (e.g., pot-life) or time-dependent data (e.g., weathering), which cannot always be readily reduced to single -value metrics for modeling. An epoxy diluent study provides a good example. The objective was to map the performance of amine-cured epoxy coatings across a broad range of formulations. Twenty-five different reactive diluents were paired with liquid epoxy resin (LER) at three levels of diluent (10%, 17.5%, and 25% of the total epoxy weight). In addition two stoichiometric epoxy-toamine ratios with five different hardeners were screened. Therefore, this study involved 760 unique formulations without counting replicates. A typical approach for comparing performance is to use spider/radar charts where each axis represents one performance attribute (often scaled from 0 to 100), but it becomes impractical when there are very many samples each with many attributes. Figure 19A shows such an example for one diluent with five hardeners at a single epoxy:amine stoichiometric ratio at four dilution ratios (including zero). One can draw conclusions about performance from the figure, but it represents only one set of data from the study which has 49 more sets. Dow has developed custom software to assist researchers in data analysis and visualization. It allows the user to filter and select samples (inputs), responses (outputs) and their respective scalings; and then it calculates a composite performance rating (CPR, from 0 to 100) for each sample and displays the results. However, too many property responses still makes analysis difficult; for example, choosing relative weights in calculating a CPR. A simpler approach involves narrowing to the critical responses for a specific application, and performing modeling on the selected properties. An example is shown in Figure 19B for a LER/polyamide system, which is commonly used for anticorrosion coatings. The spider charts shown have four corners for four different performance properties: initial viscosity, impact resistance, cross hatch adhesion, and chemical resistance. Each coordinate was scaled from 0 to 100 representing the worst to the best performance in the study. With LER, the viscosity of the formulation was too high and its CPR was 59.2. Diluent addition to reduce viscosity was desired but maintenance of mechanical performance and chemical resistance was necessary. In this system, many diluents led to poorer chemical resistance, but one common diluent resulted in comparable mechanical properties while improving the system viscosity (CPR = 80.0, Figure 19B right). Even better, a new

Figure 19. (A) Performance comparison of one diluent at various weight percentages with 5 hardeners at 1:1 epoxy:amine ratio using spider charts. There are 12 axes for each chart for 12 different properties. (B) LER cured with polyamide (left) and impact of different diluents on performance (right). The number on each spider chart is its respective CPR score.

diluent was found to improve the mechanical properties and reduce the viscosity when it was added at the 17.5% level in the epoxy part, resulting in a significantly improved CPR (97.8). Although the spider/radar chart above provides a way for visual comparison and picking out the best candidates, it lacks the power of modeling and generating knowledge from a large data set. To fully utilize large data sets, modeling should be conducted with CHT studies to generate composition− structure−property relationships and also to allow exploration of untested formulations. Using the diluent study as an example again, statistical modeling was carried out with the data from the 760 formulations, and the model was then used to predict results for nearly 3000 formulations with additional diluent % and epoxy:amine ratios. The model output for overall desirability is summarized in Figure 20. The Z-axis is performance desirability, and color also represents desirability

Figure 20. Overall performance of various epoxy diluents as a function of hardener and stoichiometric ratio. Q


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ACS Combinatorial Science for better visualization. The color scale from green to red represents good to poor performance. The individual cubes represent individual formulations, and the contour surface plot shows the performance trend in the design space. Using the color contour plot, a user can draw the conclusion that hardeners H2 and H3 perform much better in general than the other hardeners and one diluent (Q) is better than the other diluents. This is an example of visual analysis possible with powerful visualization software in conjunction with modeling. No single plot can show all information in this type of complex study. The coating predicted to have the best performance for a specific set of properties can be extracted from the whole study by appropriately querying the models. CHT is also frequently used for smaller more compositionally constrained studies than the previous example. Such studies are readily handled by conventional statistical experimental design and analysis. Researchers rely on the models which are generated to make predictions of properties for unknown compositions and formula optimization. Mixture designs are commonly used in formulating industrial coatings systems. An example of the mixture design approach is depicted in the ternary plots shown in Figure 21. A glass bonding moisturecured primer required formulation optimization to improve glass transition temperature (Tg) and the glass bonding strength. The primer consists of three isocyanate prepolymers (total weight of prepolymers = 60 wt %) and other components including catalyst, solvents, and stabilizers. Tg was measured on cured primer films using differential scanning calorimetry (DSC). Glass bonding strength was measured using an automated high throughput protocol on the Tack and Friction station. A scratch probe with a 1 mm diameter stainless steel tip was installed on the Tack and Friction station arm. The arm was programmed to apply parallel scratches at incremental normal loads from 200 to 900 g. The minimum load at which the primer failed at the glass interface was recorded as the glass bonding strength (Fmin) for the primer. A mixture design, consisting of the three isocyanate prepolymers, was developed. On the basis of prior knowledge of the effect of isocyanate composition, the prepolymers A, B, and C were varied between 10−50%, 0−30%, and 0−30%, respectively (depicted by the nonshaded region of the ternary plots of Figure 21A and B). Tg and Fmin data were collected for the various points of the mixture design. The resulting models for Fmin and Tg are shown using contours on the ternary plots of Figure 21A and B, respectively. Formulations containing high levels of prepolymer A result in higher Fmin values, whereas prepolymer B-rich formulations have lower Fmin values. On the other hand, prepolymer A and B have opposite effect on the Tg: prepolymer A raises Tg, while prepolymer B reduces Tg. To obtain good primer performance, the Tg and the Fmin need to be greater than 65 °C and 600 g, respectively. By superimposing the two models in the ternary plot and applying the required constraints, we obtained an optimal formulation window that satisfies both criteria for Tg and bond strength (Figure 21C).

Figure 21. Ternary plot showing contours of (A) glass bonding strength, (B) Tg, and (C) an optimal formulation window that satisfy the required criteria of glass bonding strength more than 600 g and glass transition of 65 °C.

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examples from industrial coatings CHT research at Dow.29 Industrial coatings research requires handling of diverse chemistries directed at diverse problems with well-defined timelines. Consequently, our approach places a premium on flexibility in CHT workflows. To facilitate success, we frequently augment CHT workflows with conventional coatings tests in order to rapidly bridge gaps between the CHT world and our colleagues’ and customers’ worlds. Our approach has

CONCLUDING REMARKS As combinatorial and high throughput (CHT) methodologies for coatings and other materials have developed over the last 15 years, it has been interesting to follow the approaches and associated philosophies adopted by different research groups in industrial, academic, and government laboratories. In this account we present the approach, philosophy, and some R


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(6) Potyrailo, R. A.; Chisholm, B. J.; Morris, W. G.; Cawse, J. N.; Flanagan, W. P.; Hassib, L.; Molaison, C. A.; Ezbiansky, K.; Medford, G.; Reitz, H. Development of combinatorial chemistry methods for coatings: High-throughput adhesion evaluation and scale-up of combinatorial leads. J. Comb. Chem. 2003, 5, 472−478. (7) Arslan, H. K.; Shekhah, O.; Wohlgemuth, J.; Franzreb, M.; Fischer, R. A.; Wöll, C. High-Throughput Fabrication of Uniform and Homogenous MOF Coatings. Adv. Funct. Mater. 2011, 21, 4228− 4231. (8) Potyrailo, R. A.; Ezbiansky, K.; Chisholm, B. J.; Morris, W. G.; Cawse, J. N.; Hassib, L.; Medford, G.; Reitz, H. Development of combinatorial chemistry methods for coatings: high-throughput weathering evaluation and scale-up of combinatorial leads. J. Comb. Chem. 2005, 7, 190−196. (9) Ekin, A.; Webster, D. C. Combinatorial and high-throughput screening of the effect of siloxane composition on the surface properties of crosslinked siloxane-polyurethane coatings. J. Comb. Chem. 2007, 9, 178−188. (10) Chisholm, B. J.; Christianson, D. A.; Webster, D. C. Combinatorial materials research applied to the development of new surface coatings. Prog. Org. Coat. 2006, 57, 115−122. (11) Chisholm, B.; Potyrailo, R.; Cawse, J.; Shaffer, R.; Brennan, M.; Molaison, C.; Whisenhunt, D.; Flanagan, B.; Olson, D.; Akhave, J.; et al. The development of combinatorial chemistry methods for coating development: I. Overview of the experimental factory. Prog. Org. Coat. 2002, 45, 313−321. (12) Oh, J. K.; Anderson, J.; Erdem, B.; Drumright, R.; Meyers, G. Selection of coalescing solvents for coatings derived from polyurethane dispersions utilizing high throughput research methods. Prog. Org. Coat. 2011, 72, 253−259. (13) Potyrailo, R.; Rajan, K.; Stoewe, K.; Takeuchi, I.; Chisholm, B.; Lam, H. Combinatorial and High-Throughput Screening of Materials Libraries: Review of State of the Art. ACS Comb. Sci. 2011, 13, 579− 633. (14) Peil, K. P.; Neithamer, D. R.; Patrick, D. W.; Wilson, B. E.; Tucker, C. J. Applications of High Throughput Research at The Dow Chemical Company. Macromol. Rapid Commun. 2004, 25, 119−126. (15) Cawse, J. N. Experimental strategies for combinatorial and highthroughput materials development. Acc. Chem. Res. 2001, 34, 213− 221. (16) Chisholm, B. J.; Webster, D. C. The development of coatings using combinatorial/high throughput methods: a review of the current status. J. Coat. Technol. Res. 2007, 4, 1−12. (17) Yan, J.; Ariyasivam, S.; Weerasinghe, D.; He, J.; Chisholm, B.; Chen, Z.; Webster, D. Thiourethane thermoset coatings from biobased thiols. Polym. Int. 2012, 61, 602−608. (18) Kalita, H.; Selvakumar, S.; Jayasooriyamu, A.; Fernando, S.; Samanta, S.; Bahr, J.; Alam, S.; Sibi, M.; Vold, J.; Ulven, C.; et al. Biobased poly (vinyl ether) s and their application as alkyd-type surface coatings. Green Chem. 2014, 16, 1974−1986. (19) Chisholm, B. J.; Stafslien, S. J.; Christianson, D. A.; GallagherLein, C.; Daniels, J. W.; Rafferty, C.; Wal, L. V.; Webster, D. C. Combinatorial materials research applied to the development of new surface coatings. Appl. Surf. Sci. 2007, 254, 692−698. (20) Chisholm, B. J.; Berry, M.; Bahr, J.; He, J.; Li, J.; Balbyshev, S.; Bierwagen, G. P. Combinatorial materials research applied to the development of new surface coatings XI: a workflow for the development of hybrid organic−inorganic coatings. J. Coat. Technol. Res. 2010, 7, 23−37. (21) Potyrailo, R. A.; Chisholm, B. J.; Olson, D. R.; Brennan, M. J.; Molaison, C. A. Development of combinatorial chemistry methods for coatings: High-throughput screening of abrasion resistance of coatings libraries. Anal. Chem. 2002, 74, 5105−5111. (22) Potyrailo, R. A.; Morris, W. G.; Wroczynski, R. J.; McCloskey, P. J. Resonant Multisensor System for High-Throughput Determinations of Solvent/Polymer Interactions. J. Comb. Chem. 2004, 6, 869−873. (23) Jimenez, M.; Duquesne, S.; Bourbigot, S. High-throughput fire testing for intumescent coatings. Ind. Eng. Chem. Res. 2006, 45, 7475− 7481.

been strongly shaped by Dow’s position in the industrial coatings value chain as a developer and supplier of novel component materials to coating formulators. To successfully position these new coating components in the market, we must clearly demonstrate their performance across a wide variety of formulations and application conditions which is a problem ideally suited for CHT workflows.

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AUTHOR INFORMATION

Corresponding Author

*Tel: 989-636-8900. E-mail: [email protected]. Funding

Funding was provided by The Dow Chemical Company, Midland, Michigan. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We stand on the shoulders of many talented co-workers, especially our colleagues in the Formulation Science, Information Research, and Dow Coating Materials laboratories, who have made important contributions to both the research projects and hardware and software capabilities described in this Account. Colleagues whose contributions we would like to especially acknowledge are Andrew Banks, Sarah Eckersley, Irina Graf, Adam Grzesiak, John Klier, Lyle McCarty, Jodi Mecca, Paul Morabito, Robert Mussell, Matt Ninke, Rebecca Ortiz, Brian Orvosh, Amy Reder, John Roper Jr., Shannon Timpe, Chris Tucker, Jamie Weishuhn, Heather Wiles, Rick Winterton, Huiqing Zhang, and Jonathan Zieman. We also acknowledge key members of the Reactive Coatings System development team from Freeslate, Justin Fisher, Harry Luo, Cherry Yuan, and Jay Zhu. We also would like to acknowledge the leadership vision behind The Dow Chemical Company’s strong commitment to funding the development and use of CHT coatings capabilites. As broader acknowledgment, we are grateful for all that we have learned over the years from others in the materials CHT community, here we would call out the pioneers at BASF, General Electric, Avery Dennison, Bayer Materials Science, the National Institute of Science and Technology, North Dakota State University, the Dutch Polymer Institute, Freeslate, Chemspeed, BOSCH Lab Systems, FLAMAC, and others for their innovative contributions to the state of the art.

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REFERENCES

(1) Rajaram, S. Global Markets and Advanced Technologies for Paints and Coatings; BCC Research, 2014. (2) Stafford, C. M.; Guo, S.; Harrison, C.; Chiang, M. Y. M. Combinatorial and high-throughput measurements of the modulus of thin polymer films. Rev. Sci. Instrum. 2005, 76, 062207. (3) Cawse, J. N.; Olson, D.; Chisholm, B. J.; Brennan, M.; Sun, T.; Flanagan, W.; Akhave, J.; Mehrabi, A.; Saunders, D. Combinatorial chemistry methods for coating development V: generating a combinatorial array of uniform coatings samples. Prog. Org. Coat. 2003, 47, 128−135. (4) Goto, M.; Kasahara, A.; Tosa, M. Low frictional property of copper oxide thin films optimized using a combinatorial sputter coating system. Appl. Surf. Sci. 2006, 252, 2482−2487. (5) Potyrailo, R. A.; Olson, D. R.; Medford, G.; Brennan, M. J. Development of combinatorial chemistry methods for coatings: Highthroughput optimization of curing parameters of coatings libraries. Anal. Chem. 2002, 74, 5676−5680. S


Account

ACS Combinatorial Science (24) Stafslien, S. J.; Bahr, J. A.; Daniels, J. W.; Vander Wal, L.; Nevins, J.; Smith, J.; Schiele, K.; Chisholm, B. Combinatorial materials research applied to the development of new surface coatings VI: An automated spinning water jet apparatus for the high-throughput characterization of fouling-release marine coatings. Rev. Sci. Instrum. 2007, 78, 072204. (25) Webster, D. C.; Bennett, J.; Kuebler, S.; Kossuth, M. B.; Jonasdottir, S. High throughput workflow for the development of coatings. JCT CoatingsTech 2004, 1, 34−39. (26) Chisholm, B.; Potyrailo, R.; Shaffer, R.; Cawse, J.; Brennan, M.; Molaison, C. Combinatorial chemistry methods for coating development. Prog. Org. Coat. 2003, 48, 219−226. (27) Chisholm, B.; Potyrailo, R.; Shaffer, R.; Cawse, J.; Brennan, M.; Molaison, C. Combinatorial chemistry methods for coating development: VI. Correlation of high throughput screening methods with conventional measurement techniques. Prog. Org. Coat. 2003, 48, 219−226. (28) Kalihari, V.; Timpe, S. J.; McCarty, L.; Ninke, M.; Whitehead, J. An automated high throughput tribometer for adhesion, wear, and friction measurements. Rev. Sci. Instrum. 2013, 84, 035104. (29) Hansen, C. M. Hansen Solubility Parameters: A User’s Handbook, 2nd ed.; CRC Press, 2007. (30) Bicerano, J. Prediction of Polymer Properties, 3rd ed.; CRC Press, 2002. (31) Tucker, C.; Mohler, C.; Harris, K. High throughput surfactant synthesis, characterization and formulation. SOFW J. 2008, 134, 80− 90. (32) Timpe, S. J.; Kuo, T.-C. Surface Property Development in Polymeric Coating Systems. Tribol. Lett. 2013, 52, 105−112. (33) ASTM Standard Test Methods for Measuring Adhesion by Tape Test, ASTM D3359-09e2; ASTM International: West Conshohocken, PA, 2009. (34) Jardret, V.; Morel, P. Viscoelastic effects on the scratch resistance of polymers: relationship between mechanical properties and scratch properties at various temperatures. Prog. Org. Coat. 2003, 48, 322−331. (35) Bertrand-Lambotte, P.; Loubet, J.; Verpy, C.; Pavan, S. Nanoindentation, scratching and atomic force microscopy for evaluating the mar resistance of automotive clearcoats: study of the ductile scratches. Thin Solid Films 2001, 398, 306−312. (36) Ryntz, R. A.; Abell, B.; Pollano, G.; Nguyen, L.; Shen, W. Scratch resistance behavior of model coating systems. J. Coat. Technol. 2000, 72, 47−53. (37) Leyland, A.; Matthews, A. On the significance of the H/E ratio in wear control: a nanocomposite coating approach to optimized tribological behaviour. Wear 2000, 246, 1−11. (38) Wang, T.; de las Heras Alarcón, C.; Goikoetxea, M.; Beristain, I.; Paulis, M.; Barandiaran, M. J.; Asua, J. M.; Keddie, J. L. Cross-linked network development in compatibilized alkyd/acrylic hybrid latex films for the creation of hard coatings. Langmuir 2010, 26, 14323− 14333. (39) Gu, X.; Nguyen, T.; Oudina, M.; Martin, D.; Kidah, B.; Jasmin, J.; Rezig, A.; Sung, L.; Byrd, E.; Martin, J. W.; et al. Microstructure and morphology of amine-cured epoxy coatings before and after outdoor exposuresAn AFM study. J. Coat. Technol. Res. 2005, 2, 547−556. (40) Rezig, A.; Nguyen, T.; Martin, D.; Sung, L.; Gu, X.; Jasmin, J.; Martin, J. W. Relationship between chemical degradation and thickness loss of an amine-cured epoxy coating exposed to different UV environments. J. Coat. Technol. Res. 2006, 3, 173−184. (41) Deck, L.; De Groot, P. High-speed noncontact profiler based on scanning white-light interferometry. Appl. Opt. 1994, 33, 7334−7338. (42) Vorburger, T. V.; Rhee, H.-G.; Renegar, T. B.; Song, J.-F.; Zheng, A. Comparison of optical and stylus methods for measurement of surface texture. Int. J. Adv. Manuf. Technol. 2007, 33, 110−118. (43) Dinnissen, T. Amine blushing and blooming of epoxy binder systems in protective coatings. J. Prot. Coat. Linings 2005, 22, 40−45. (44) Majumdar, P.; Christianson, D. A.; Webster, D. C. Automated determination of pot life of two-component reactive coatings. Prog. Org. Coat. 2006, 57, 210−214.

(45) Deshmukh, S.; Bishop, M.; Dermody, D.; Dietsche, L.; Kuo, T.C.; Mushrush, M.; Harris, K.; Zieman, J.; Morabito, P.; Orvosh, B.; Patrick, D. A Novel High-Throughput Viscometer. ACS Comb. Sci. 2016, 18, 405−414. (46) Malvadkar, N.; Weishuhn, J.; Drumright, R. Methodology for Developing Low Temperature Cure Accelerators for Epoxy-Amine Coatings. Paints and Coatings Industry Magazine 2015, 31, 52−54. (47) Leugers, A.; Neithamer, D. R.; Sun, L. S.; Hetzner, J. E.; Hilty, S.; Hong, S.; Krause, M.; Beyerlein, K. High-throughput analysis in catalysis research using novel approaches to transmission infrared spectroscopy. J. Comb. Chem. 2003, 5, 238−244. (48) Chen, X.; Stoneburner, K.; Ladika, M.; Kuo, T.-C.; Kalantar, T. H. High-Throughput Raman Spectroscopy Screening of Excipients for the Stabilization of Amorphous Drugs. Appl. Spectrosc. 2015, 69, 1271−1280. (49) Mecca, J. M.; Morabito, P. L.; Lyons-Bell, C. E.; Williams, J. G. Coating apparatus and method. U.S. Patent 8,434,422, May 7, 2013. (50) Doerner, M. F.; Nix, W. D. A method for interpreting the data from depth-sensing indentation instruments. J. Mater. Res. 1986, 1, 601−609. (51) Oliver, W. C.; Pharr, G. M. An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiments. J. Mater. Res. 1992, 7, 1564−1583. (52) Field, J.; Swain, M. A simple predictive model for spherical indentation. J. Mater. Res. 1993, 8, 297−306. (53) Lee, S.; Chang, L.-M.; Skibniewski, M. Automated recognition of surface defects using digital color image processing. Automat. Constr. 2006, 15, 540−549. (54) Larkin, K. G. Efficient nonlinear algorithm for envelope detection in white light interferometry. J. Opt. Soc. Am. A 1996, 13, 832−843.

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