During the last two decades the pharmaceutical industry has constantly made use of automated and parallel workflows to increase their productivity in the R&D process. The use of automated, high-throughput (HT) screening methodologies to develop new structures using fast identification systems has resulted in an important reduction of time-to-market and an increase in cost savings1.
Recently, researchers in the field of polymeric (EU) coatings and polymeric formulations started using these tools. It is well known that a rainbow of formulation and process parameters affect the performance of coatings formulations. From these parameters one can mention the formulation composition (structure and composition of the polymeric binder(s), leveling agents (EU), cross-linkers (EU), thickeners (EU), defoamers (EU), pigments (EU), photoinitiators (EU), etc…), the application of the coating (blading, brush, spraying, etc…), and the processing conditions (drying, aging, curing, etc…). All these parameters need to be varied in order to develop a correlation between them and product performance, therefore combinatorial methods seem to be a powerful tool for the optimization of these systems2.
To overcome this problem, the design of experiments (DoE) is a decisive way to extend the coverage and understanding of variable correlations in parameter space. Moreover, the DoE software employed must act as a statistical station to process the extensive amount of data created by the designed (and quite necessary) HT workflows. The following work presents the DoE proposed for the testing of a new binder with dispersing properties and the experimentation that was carried out throughout 2 different HTWs: the first one designed at Nuplex (EU) R&D laboratories and the second one designed at Van Loon Chemical Innovations (VLCI)5.
The DoE in this work was carried out with the aid of the software Design Expert® version 9.0. The type of design used was Surface Response since it offers the possibility of mixing numerical and categorical factors with high accuracy3,4. This resulted in 36 samples, which were produced via 2 different HT workflows as shown in figure 1 and 2. The difference between the workflows is that the Nuplex HT can’t add raw materials whilst processing (mixing) and can’t add solids. Mixing is done via the well-known high speed mixer, the Dual Asymmetric Centrifugal Laboratory Mixer, to make the pigment paste, followed by automatized letdown preparation and mixing to obtain the full paint in the Paint Robot. The VLCI paint making HT workflow (FORMAX, Chemspeed) is completely automated. The time needed to prepare all the 36 prototypes for the case of Nuplex was 24 h and for VLCI was 11 h. The paints were manually tested on appearance, gloss, haze, whiteness, opacity, coffee and hand cream resistance.
The use of HT solutions in combination with a solid statistical analysis resulted in an excellent tool.
- It affords understanding in the behavior and the interaction of a new complex binder with dispersing properties from Nuplex in a pigmented paint.
- If offers the potential to fine tune the performance of the binder in terms of coffee resistance, whiteness and opacity.
- Basically, the workflows performed at Nuplex and VLCI led to the same results: described robustness as the statistical analysis of the DoE resulted in response (paint tests) models that were similar in trends and results.
- The models generated by DoE can be used to predict certain performances when using paint formulations predicted by the software. The positive validation to real, “live,” larger scale preparation shows the real strength of performing HTE and DoE. These features were obtained without an excessive amount of experiments and in a much shorter experimentation period as compared to conventional bench work. HT paint preparation was 3-5 times faster, while the whole process (DoE, preparation, testing and analysis) could be 2-3 times faster compared to the complete manual way of working
1) S.P. Rohrer, E. T. Birzin, R. T. Mosley, S. C. Berg, S. M. Hutchins, D.-M. Shen, Y. Xiong, E. C Hayes, R. M. Parmar, F. Floor, S. W. Mitra, S. J. Degrado, M. Shu, J. M. Klopp, S. -J. Cai, A. Blake, W. W. S. Chan, A. Pasternak, L. Yang, A. A. Patchett, R. G. Smith, K. T. Chapman, J. M. Schaeffer, Science 1998, 282, 737.
2) B. J. Chisholm, R. A. Potyrailo, J. N. Cawse, R. E. Shaffer, M. Brennan, C. Molaison, D. Whisenhunt, B. Flanagan, D. Olson, J. Akhave, D. Saunders, A. Mehrabi, M. Licon, Prog. Org, Coat. 2002, 45, 313.
3) J. Fireman, SAE Off-Highway Engng. 2009, http://articles.sae.org/6633/
4) M. J. Anderson, P. J. Whitcomb, “RSM Simplified – Powerful Tools for Optimizing Processes via Response Surface Methods” New York: Productivity, Inc., 2004.
5) Extensive paper presented at the 9th Wood Coatings Congress, Sept 2014 in Amsterdam by J. Akkerman
About the Author:
After his study at the Vrije Universiteit (organic chemistry) in 2001, Sander van Loon worked for 7 years on the Marine and Protective Coatings laboratory of PPG. In November 2008 he founded the company VLCI, which provides R&D services to the formulation industry. He is currently the CEO of VLCI, a company with 7 persons, using HT screening and serving small to multinational customers over the world. For more information on Van Loon Chemical Innovations’ services, click here.
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