Separating the pH-Dependent Behavior of Water in the Stern and Diffuse Layers with Varying Salt Concentration
Bibliographic record
Abstract
Vibrational sum frequency generation (SFG) spectroscopy was utilized to distinguish different populations of water molecules within the electric double layer (EDL) at the silica/water interface. By systematically varying the electrolyte concentration, surface deprotonation, and SFG polarization combinations, we provide evidence of two regions of water molecules that have distinct pH-dependent behavior when the Stern layer is present (with onset between 10 and 100 mM NaCl). For example, water molecules near the surface in the Stern layer can be probed by the pss polarization combination, while other polarization combinations (ssp and ppp) predominantly probe water molecules further from the surface in the diffuse part of the electrical double layer. For the water molecules adjacent to the surface within the Stern layer, upon increasing the pH from the point-of-zero charge of silica (pH ∼2) to higher values (pH ∼12), we observe an increase in alignment consistent with a more negative surface with increasing pH. In contrast, water molecules further from the surface appear to exhibit a net flip in orientation upon increasing the pH over the same range, which we attribute to the presence of the Stern layer and possible overcharging of the EDL at lower pH. The opposing pH-dependent behavior of water in these two regions sheds new light on our understanding of the water structure within the EDL at high salt concentrations when the Stern layer is present.
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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 | 0.001 |
| 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 | 0.001 |
| 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".