Polarization-Induced Local pH Swing Promotes Pd-Catalyzed CO<sub>2</sub> Hydrogenation
Bibliographic record
Abstract
Electrochemical polarization can dramatically promote the rate of concurrent nonfaradaic catalytic reactions, but the mechanistic basis for these promotion effects at solid–liquid interfaces remains poorly understood. Herein, we establish a mechanistic framework for nonfaradaic promotion in aqueous media that operates via a local pH swing induced by a concurrent faradaic reaction. As a model system, we examined the kinetics of nonfaradaic Pd-catalyzed CO 2 hydrogenation to formate and find that the reaction can be promoted by a combination of high alkalinity and high CO 2 concentration. In bulk electrolyte, alkalinity and CO 2 concentration are inversely correlated to each other as set by the CO 2 /bicarbonate equilibrium. We show that this impasse can be overcome by using electrical polarization to generate a nonequilibrium local environment that has both high alkalinity and high CO 2 concentration. We find that this local pH swing promotes the rate of nonfaradaic CO 2 hydrogenation to formate by nearly 3 orders of magnitude at modest potential bias. The work establishes a rigorous mechanistic model of nonfaradaic promotion in aqueous media and provides a basis for enhancing hydrogenation catalysis under mild conditions via electrical polarization.
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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 | 0.000 | 0.000 |
| 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".