Interfacial Acid–Base Equilibria and Electric Fields Concurrently Probed by <i>In Situ</i> Surface-Enhanced Infrared Spectroscopy
Bibliographic record
Abstract
Understanding how applied potentials and electrolyte solution conditions affect interfacial proton (charge) transfers at electrode surfaces is critical for electrochemical technologies. Herein, we examine mixed self-assembled monolayers (SAMs) of 4-mercaptobenzoic acid (4-MBA) and 4-mercaptobenzonitrile (4-MBN) on gold using in situ surface-enhanced infrared absorption spectroscopy (SEIRAS). Measurements as a function of the applied potential, the electrolyte pD, and the electrolyte concentration determined both the relative surface populations of acidic and basic forms of 4-MBA, as well as the local electric fields at the SAM–solution interface by following the Stark shifts of 4-MBN. The effective acidity of the SAM varied with the applied potential, requiring a 600 mV change to move the p K a by one unit. Since this is ca. 10× the Nernstian value of 59 mV/p K a, ∼90% of the applied potential dropped across the SAM layer. This emphasizes the importance of distinguishing applied potentials from the potential experienced at the interface. We use the measured interfacial electric fields to estimate the experienced potential at the SAM edge. The SAM p K a showed a roughly Nernstian dependence on this estimated experienced potential. An analysis of the combined acid–base equilibria and Stark shifts reveals that the interfacial charge density has significant contributions from both SAM carboxylate headgroups and electrolyte components. Ion pairing and ion penetration into the SAM also influence the observed surface acidity. To our knowledge, this study is the first concurrent examination of both effective acidity and electric fields, and highlights the relevance of experienced potentials and specific ion effects at functionalized electrode surfaces.
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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 | 0.001 | 0.001 |
| 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".