The utility of Raman spectra in aiding the interpretation of surface structure at aqueous interfaces
Bibliographic record
Abstract
Abstract In this perspective, we review how Raman spectroscopy of bulk aqueous phases can assist in the interpretation of surface‐selective vibrational spectra obtained from visible‐infrared sum‐frequency generation experiments. This overcomes the limitations associated with a reliance on spectral fitting to study characteristic vibrational modes of all species and thereby provides an all‐experimental route for analysis of spectral features, including cases where spectra are available in only a single set of beam polarizations. The basic principle is based on two‐dimensional correlation analysis, a generalized method with broad applicability, but most well‐known in its application to vibrational spectra. We provide an example of the type of information that can be obtained when using heterospectral correlation that includes both sum‐frequency and Raman data. The combination of these methods can help to unravel characteristic features of aqueous interfaces such as the surface preference of adsorbed species relative to their bulk concentration.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".