Influence of the Hydrogen-Bonding Environment on Vibrational Coupling in the Electrical Double Layer at the Silica/Aqueous Interface
Bibliographic record
Abstract
Vibrational spectroscopy is a powerful tool for determining the local hydrogen-bonding environment. However, vibrational coupling present in H 2 O makes it difficult to relate vibrational spectra to a molecular description of the system. While numerous bulk studies have shed light on this phenomenon, the influence of both intra- and intermolecular vibrational coupling on the resulting electrical double layer spectra at buried interfaces remains largely unexplored. By utilizing vibrational sum frequency generation (vSFG), electrokinetic measurements, and the maximum entropy method on isotopically diluted water (HOD) at the silica/aqueous interface, we reveal the influence of vibrational coupling on the Stern and diffuse layer spectra as the pH is varied. For the Stern layer spectra, we observe differences in the frequency centers at pH 2 that are less significant at higher pH, signifying the presence of intermolecular coupling that can be related to the double-donor hydrogen-bonded structure of water. Furthermore, the differences in the evolution of the Stern layer of H 2 O and HOD suggest that the presence of intramolecular coupling in the former may distort the spectral response. Moreover, we observe that the evolution of HOD closely matches the p K a of the out-of-plane silanols predicted by previous molecular dynamic simulations.
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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".