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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Taylor, Michael
in Cooperation with on an Cooperation-Score of 37%
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Publications (5/5 displayed)
- 2024Prior Austenite Grain Measurement: A Direct Comparison of EBSD Reconstruction, Thermal Etching and Chemical Etchingcitations
- 2023A SARS-CoV-2 outbreak associated with five air force bases and a nightclub following the lifting of COVID-19-related social restrictions, United Kingdom, July-to-September 2021
- 2022Quantifiable correlation of ToF‐SIMS and XPS data from polymer surfaces with controlled amino acid and peptide contentcitations
- 2009Partial least squares regression as a powerful tool for investigating large combinatorial polymer librariescitations
- 2008TOF-SIMS analysis of a 576 micropatterned copolymer array to reveal surface moieties that control wettabilitycitations
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article
Partial least squares regression as a powerful tool for investigating large combinatorial polymer libraries
Abstract
Partial Least Squares (PLS) regression is an established analytical tool in surface science, particularly for relating multivariate ToF-SIMS data to a univariate surface property. Herein we construct a PLS model using ToF-SIMS and surface energy data from a 496 copolymer micro-patterned library. Using this 496 copolymer library we investigate how changing the number of samples used to construct the PLS model affects the identity of the most influential ions identified in the regression vector. The regression coefficients vary in magnitude, but the general relationship between ion structure and surface energy is maintained. As expected, if copolymers containing monomers with unique chemistries are removed from the training set, secondary ions specific to these copolymers are not present in the regression vector. The use of PLS to obtain quantitative predictions has not been actively explored in the surface analytical field. We investigate whether the PLS model obtained can be used to predict the surface energies of polymers within and outside of the training set. The model systematically underestimated the surface energy of a group of acrylate copolymers synthesised using monomers common to the training set, but in different compositions. The predictions for a group of acrylate copolymers that were synthesised from monomers not used in the training set were very poor. When the model was used to obtain predictions for six commercially available polymers the values obtained were all close to the mean surface energy of the training set. This exercise suggests that PLS may be able to predict the surface energy of polymers synthesised from monomers common to the training set, confirming the importance that the training set reflects the chemistry of the samples to be predicted. Copyright © 2008 John Wiley & Sons, Ltd.