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Driving innovations in manufacturing: Open Access research from DMEM

Strathprints makes available Open Access scholarly outputs by Strathclyde's Department of Design, Manufacture & Engineering Management (DMEM).

Centred on the vision of 'Delivering Total Engineering', DMEM is a centre for excellence in the processes, systems and technologies needed to support and enable engineering from concept to remanufacture. From user-centred design to sustainable design, from manufacturing operations to remanufacturing, from advanced materials research to systems engineering.

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TOF-SIMS analysis of a 576 micropatterned copolymer array to reveal surface moieties that control wettability

Urquhart, Andrew J. and Taylor, Michael and Anderson, Daniel G. and Langer, Robert S. and Davies, Martyn C. and Alexander, Morgan R. (2008) TOF-SIMS analysis of a 576 micropatterned copolymer array to reveal surface moieties that control wettability. Analytical Chemistry, 80 (1). pp. 135-142. ISSN 0003-2700

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Abstract

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) was used in a high-throughput fashion to obtain mass spectra from the surfaces of 576 novel acrylate-based polymers, synthesized using a combinatorial approach and in a micropatterned format. To identify variations in surface chemistry within the library, principal component analysis (PCA) was used. PCA clearly identified surface chemical commonality and differences within the library. The TOF-SIMS spectra were also used to determine the relationship between water contact angle (WCA) and the surface chemistry of the polymer library using partial least-squares regression (PLS). A good correlation between the TOF-SIMS data from the novel polymers and water contact angle was obtained. Examination of the PLS regression vector allowed surface moieties that correlate with high and low WCA to be identified. This in turn provided an insight into molecular structures that significantly influence wettability. This study demonstrates that multivariate analysis can be successfully applied to TOF-SIMS data from a large library of samples and highlights the potential of these techniques for building complex surface property/chemistry models.