| 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
Comparison of Tiling Artifact Removal Methods in Secondary Ion Mass Spectrometry Images
Abstract
Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is used across many fields for the atomic and molecular characterisation of surfaces, with both high sensitivity and high spatial resolution. When large analysis areas are required, standard ToF-SIMS instruments allow for the acquisition of adjoining tiles, each of which is acquired by rastering the primary ion beam. For such large area scans, tiling artefacts are a ubiquitous challenge, manifesting as intensity gradients across each tile and/or sudden changes in intensity between tiles. Such artefacts are thought to be related to a combination of sample charging, local detector sensitivity issues and misalignment of the primary ion gun, among other instrumental factors. In this work, we investigated 6 different computational tiling artefact removal methods: tensor decomposition, multiplicative linear correction, linear discriminant analysis, seamless stitching, simple averaging and simple interpolating. To ensure robustness in the study, we applied these methods to 3 hyperspectral ToF-SIMS datasets and one OrbiTrap™ SIMS dataset. Our study includes a carefully designed statistical analysis and a quantitiative survey that subjectively assessed the quality of the various methods employed. Our results demonstrate that while certain methods are useful and preferred more often, no one particular approach can be considered universally acceptable and that the effectiveness of the artefact removal method is strongly dependent on the particulars of the dataset analysed. As examples, the multiplicative linear correction and seamless stitching methods tended to score more highly on the subjective survey, however for some data sets led to the introduction of new artifacts. In contrast, simple averaging and interpolation methods scored subjectively poorly on the biological data set, but more highly on a the microarray data sets. We discuss and explore these findings in depth, and present general recommendations given our findings to conclude the work.