Inhibiting the Hydrogen Evolution Reaction (HER) with Proximal Cations: A Strategy for Promoting Selective Electrocatalytic Reduction
Bibliographic record
Abstract
Most electrochemical reduction reactions require protons; however, direct reduction of protons to hydrogen is a common competitive side reaction that lowers the overall yield (Faradaic efficiency) for the desired product. Inhibition of the hydrogen evolution reaction (HER) by fixed, proximal mono- or dication is reported. An SNS tridentate ligand with a pendant aza-crown functionality and the corresponding Pd(II) and Rh(I) complexes were synthesized. For the Pd complexes, the presence of a Na + or Ba 2+ ion in the aza-crown increases the overpotential for hydrogen evolution by up to 260 mV compared to a congener that lacks an aza-crown. The increase in the overpotential for HER was observed with cationic and neutral acids in both acetonitrile and dimethylformamide. Additionally, for the Pd complexes, the inclusion of a Ba 2+ ion into the aza-crown modestly improved the selectivity for electrocatalytic CO 2 reduction to CO, whereas H 2 was the only product for the congener lacking the aza-crown. The Ba 2+ -containing complex also had improved catalytic stability, although all complexes were ultimately unstable under prolonged electrolytic conditions. The increase in the overpotential for HER through the installation of a local charge exemplifies an effective catalyst design strategy for inhibiting the hydrogen evolution reaction.
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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 | 0.001 |
| 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".