Raman maps reveal heterogeneous hydrogenation on carbon materials
Bibliographic record
Abstract
Abstract This work presents an application of Raman spectroscopy as a tool to investigate heterogeneity in the composition of carbon materials obtained electrochemically. A combination of Raman maps and histograms has been used to describe samples synthesized via wet chemistry. The results showed that a simple evaluation of an average spectrum or one single spectrum per sample would have hidden important compositional variations present in the film. The Raman maps revealed heterogeneous hydrogenation in different areas of the carbon films, whereas histograms described the statistical relevance of the classification of the different types of carbon materials. The effect of electrosynthesis parameters on the quality of the films was also investigated. As the deposition time increased, the carbon films showed higher homogeneity in their spatial composition. The nature of the electrolyte led to differences in film functionalization and on the degree of hydrogenation.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".