Open Circuit Potential Decay Transients Quantify Non-Equilibrium Local pH During Electrocatalysis
Bibliographic record
Abstract
Many key energy conversion reactions are proton-coupled electron transfer (PCET) reactions that consume or generate protons at electrode surfaces. Thus, catalytic turnover can generate non-equilibrium local pH environments at the surface that differ substantially from that of the bulk. Quantitative insight into the magnitude of this interfacial pH swing is a prerequisite for understanding and designing efficient systems for energy conversion, but is difficult to measure, particularly under high current density operation; with complex gas diffusion electrodes (GDEs); and with membrane-decorated surfaces employed in functional devices. Herein, we develop and validate a general methodology for experimentally quantifying interfacial pH swings using open circuit potential (OCP) decay transients. Using this method, we quantify the impact of buffer strength, supporting electrolyte composition, and the presence of cation exchange polymer overlayers on the polarization-induced pH swing on Pt GDEs. We find that modest current densities of −30 mA cm−2 are sufficient to sustain pH swings of > 2 pH units, even for strongly buffered solutions. Meanwhile, the addition of alkali supporting electrolyte to unbuffered, acidic electrolyte can induce pH swings so large that the polarized electrode environment becomes strongly alkaline. The presence of a Nafion polymer overlayer containing fixed anionic charges serves to further augment the interfacial pH swing, resulting in a similar pH swing at half the applied current density. The transport characteristics of these systems were analytically modelled, enabling direct calculation of boundary layer thickness and quantitative prediction of the OCP decay transient. These studies establish methods for quantifying local pH swings and highlight the dramatic variation in local pH relative to the bulk under many electrolyte conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".