Water Structure in the Electrical Double Layer and the Contributions to the Total Interfacial Potential at Different Surface Charge Densities
Bibliographic record
Abstract
The electric double layer governs the processes of all charged surfaces in aqueous solutions, however elucidating the structure of the water molecules is challenging for even the most advanced spectroscopic techniques. Here, we present the individual Stern layer and diffuse layer OH stretching spectra at the silica/water interface in the presence of NaCl over a wide pH range using a combination of vibrational sum frequency generation and heterodyned second harmonic generation techniques and streaming potential measurements. We find that the Stern layer water molecules and diffuse layer water molecules respond differently to pH changes: unlike the diffuse layer, whose water molecules remain net-oriented in one direction, water molecules in the Stern layer flip their net orientation as the solution pH is reduced from basic to acidic. We obtain an experimental estimate of the non-Gouy-Chapman (Stern) potential contribution to the total potential drop across the insulator/electrolyte interface and discuss it in the context of dipolar, quadrupolar, and cross-term potential contributions. We quantify how these contributions result in a considerable influence on the vibrational lineshapes. Our findings show that a purely Gouy-Chapman (Stern) view is insufficient to accurately describe the electrical double layer of aqueous interfaces.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".