| People | Locations | Statistics |
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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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Muir, Benjamin Ward
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (14/14 displayed)
- 2023New insight into degradation mechanisms of conductive and thermally resistant polyaniline filmscitations
- 2023Solvent-free Surface Modification of Milled Carbon Fiber using Resonant Acoustic Mixing
- 2023Comparison of Tiling Artifact Removal Methods in Secondary Ion Mass Spectrometry Imagescitations
- 2023Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencodercitations
- 2023Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF‐SIMS and Self‐Organizing mapscitations
- 2022Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems
- 2020ToF-SIMS and machine learning for single-pixel molecular discrimination of an acrylate polymer microarray
- 2017Determining the limit of detection of surface bound antibodycitations
- 2015Fundamentals and functional applications of plasma polymer films
- 2012A ToF-SIMS and XPS study of protein adsorption and cell attachment across PEG-like plasma polymer films with lateral compositional gradients
- 2012One step multifunctional micropatterning of surfaces using asymmetric glow discharge plasma polymerisation
- 2012UV grafting of a vinyl monomer onto a methanol plasma polymercitations
- 2010High-Throughput synthesis and screening of self assembled nanoparticles for use as MRI contrast agents (conference poster)
- 2006X-ray and neutron reflectometry study of glow-discharge plasma polymer filmscitations
Places of action
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article
ToF-SIMS and machine learning for single-pixel molecular discrimination of an acrylate polymer microarray
Abstract
Combinatorial approaches to materials discovery offer promising potential for the rapid development of novel polymer systems. Polymer microarrays enable the high-throughput comparison of material physical and chemical properties—such as surface chemistry and properties like cell attachment or protein adsorption—in order to identify correlations that can progress materials development. A challenge for this approach is to accurately discriminate between highly similar polymer chemistries or identify heterogeneities within individual polymer spots. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) offers unique potential in this regard, capable of describing the chemistry associated with the outermost layer of a sample with high spatial resolution and chemical sensitivity. However, this comes at the cost of generating large scale, complex hyperspectral imaging data sets. We have demonstrated previously that machine learning is a powerful tool for interpreting ToF-SIMS images, describing a method for color-tagging the output of a self-organizing map (SOM). This reduces the entire hyperspectral data set to a single reconstructed color similarity map, in which the spectral similarity between pixels is represented by color similarity in the map. Here, we apply the same methodology to a ToF-SIMS image of a printed polymer microarray for the first time. We report complete, single-pixel molecular discrimination of the 70 unique homopolymer spots on the array while also identifying intraspot heterogeneities thought to be related to intermixing of the polymer and the pHEMA coating. In this way, we show that the SOM can identify layers of similarity and clusters in the data, both with respect to polymer backbone structures and their individual side groups. Finally, we relate the output of the SOM analysis with fluorescence data from polymer–protein adsorption studies, highlighting how polymer performance can be visualized within the context of the global topology of the data set