ToF‐SIMS multivariate characterization of surface modification of polymers by N <sub>2</sub> H <sub>2</sub> atmospheric pressure dielectric barrier discharge
Bibliographic record
Abstract
Abstract Time of flight secondary ion mass spectrometry (ToF‐SIMS) is a powerful tool for the surface characterization of plasma‐modified surface. However, the SIMS fragmentation patterns of the resulting surface are quite complex and a full interpretation may be prohibitive. As a result, many studies are turning to multivariate statistical methods to simplify the interpretation. In this study, a principal component analysis (PCA) was used to obtain a more detailed interpretation of the surface modification of polymers by an atmospheric pressure plasma. The dataset was obtained from three polymers with different chemical compositions [namely, polyethylene, polyvinylidene fluoride, and poly(tetrafluoroethylene)], where each material was treated with an atmospheric pressure dielectric barrier discharge (DBD) in an atmosphere composed of different N 2 /H 2 ratios. The results are discussed in terms of the suitability of ToF‐SIMS analysis combined with PCA for the discrimination between the three polymers and the possibility to create a predictive model that would describe the plasma surface modification, independent of the polymer substrate chemical composition. Copyright © 2010 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".