| 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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Pigram, Paul
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2023New insight into degradation mechanisms of conductive and thermally resistant polyaniline filmscitations
- 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
- 2020Analyzing 3D Hyperspectral ToF-SIMS Depth Profile Data Using Self-Organizing Map-Relational Perspective Mappingcitations
- 2018Distinguishing chemically similar polyamide materials with ToF-SIMS using self-organizing maps and a universal data matrixcitations
- 2017Determining the limit of detection of surface bound antibodycitations
- 2016Chromium functionalized diglyme plasma polymer coating enhances enzyme-linked immunosorbent assay performancecitations
Places of action
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article
Distinguishing chemically similar polyamide materials with ToF-SIMS using self-organizing maps and a universal data matrix
Abstract
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is advancing rapidly, providing instruments with growing capabilities and resolution. The data sets generated by these instruments are likewise increasing dramatically in size and complexity. Paradoxically, methods for efficient analysis of these large, rich data sets have not improved at the same rate. Clearly, more effective computational methods for analysis of ToF-SIMS data are becoming essential. Several research groups are customizing standard multivariate analytical tools to decrease computational demands, provide user-friendly interfaces, and simplify identification of trends and features in large ToF-SIMS data sets. We previously applied mass segmented peak lists to data from PMMA, PTFE, PET, and LDPE. Self-organizing maps (SOMs), a type of artificial neural network (ANN), classified the polymers based on their molecular composition and primary ion probe type more effectively than simple PCA. The effectiveness of this approach led us to question whether it would be useful in distinguishing polymers that were very similar. How sensitive is the technique to changes in polymer chemical structure and composition? To address this question, we generated ToF-SIMS ion peak signatures for seven nylon polymers with similar chemistries and used our up-binning and SOM approach to classify and cluster the polymers. The widely used linear PCA method failed to separate the samples. Supervised and unsupervised training of SOMs using positive or negative ion mass spectra resulted in effective classification and separation of the seven nylon polymers. Our SOM classification method has proven to be tolerant of minor sample irregularities, sample-to-sample variations, and inherent data limitations including spectral resolution and noise. We have demonstrated the potential of machine learning methods to analyze ToF-SIMS data more effectively than traditional methods. Such methods are critically important for future complex data analysis and provide a pipeline for rapid classification and identification of features and similarities in large data sets.