Separating Hofmeister Trends in Stern and Diffuse Layers at a Charged Interface
Bibliographic record
Abstract
Understanding the role of pH and ions on the electrical double layer (EDL) at charged mineral oxide/aqueous interfaces remains crucial in modeling environmental and industrial processes. Yet the simultaneous contribution of pH and specific ion effects (SIEs) on the different layers of the EDL remains unknown. Here, we utilize zeta potential measurements, vibrational sum frequency generation, and the maximum entropy method to ascertain the detailed structure of the Stern and diffuse regions of the EDL at the silica/water interface with varying pH values for different alkali chlorides. Both at pH 2, when the surface is nearly neutral, and at pH 12, when the surface is highly charged, we observe that Li + and Na + disrupt while Cs + enhances existing water structures within the Stern layer. Moreover, the SIE trends for the diffuse and Stern layers are opposite to one another at pH 2 (in the amount of ordered water) and at pH 12 (in the amount of net oriented water). Finally, we observe an inversion in Hofmeister (SIE) trends at low and high pH in the zeta that impacts the diffuse layer structure. These results indicate that SIEs play critical yet separable roles in governing both the electrostatic and water-structuring capabilities of the EDL.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".