The variability in hydrocarbon ions (CnH−) of polymers detected by ToF-SIMS: principal component analysis on carbon density and cross-linking degree
Why this work is in the frame
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Bibliographic record
Abstract
Negative hydrocarbon ions, C n H − (n = 1–10), are ubiquitous in time-of-flight secondary ion mass spectrometry, but their utility may have been overlooked. Recently, however, it has been demonstrated that the ion intensity ratio between C 6 H − and C 4 H − , denoted as ρ, can differentiate the chemical structures of polymers such as polyethylene, polypropylene, polyisoprene and polystyrene, as well as depth profile the cross-linking degree of poly (methyl methacrylate). It was found that ρ increases with the carbon density of polymers. Principal component analysis (PCA), a dimensionality reduction technique, can reveal hidden data structures through exploring the relationships among the C n H − intensities for the four polymers. Assisted by the biplot approach, PCA is key to uncovering hidden data structures, from which characteristic ions may be identifiable and their relationships classifiable. The four polymers were classified by their carbon densities, which dictate the variability of C n H − intensities and are captured by the first principal component (PC1). It also became clear that PC1 is correlated with ρ. This data-driven analytical approach is imperative when differentiating chemicals with similar structures, especially when diagnostic ions are lacking. We demonstrate the usefulness of this approach by examining poly (methyl methacrylate) with different degrees of cross-linking.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it