Boosting H<sub>2</sub> Generation Coupled with Selective Oxidation of Methanol into Value‐Added Chemical over Cobalt Hydroxide@Hydroxysulfide Nanosheets Electrocatalysts
Bibliographic record
Abstract
Abstract The sluggish kinetics of oxygen evolution reaction (OER) is the main bottleneck for the electrocatalytic water splitting to produce hydrogen (H 2 ), and the by‐product is worthless O 2 . Therefore, designing a thermodynamically favorable oxidation reaction to replace OER and coupling with value‐added product generation on the anode is of significance for boosting H 2 generation under low electrolysis voltage. Herein, cobalt hydroxide@hydroxysulfide nanosheets on carbon paper (Co(OH) 2 @HOS/CP) are synthesized as bifunctional electrocatalysts to facilitate H 2 production and convert methanol to valuable formate simultaneously. Benefiting from the influences/changes on the composition, surface properties, electronic structure, and chemistry of Co(OH) 2 , the as‐obtained electrodes exhibit very high selectivity for methanol to value‐added formate oxidation (MFO) and boost electrocatalytic performance with low overpotential of 155 mV for MFO and 148 mV for hydrogen evolution reaction at a current density of 10 mA cm −2 . Furthermore, the integrated two‐electrode electrolyzer drives 10 mA cm −2 at a cell voltage of 1.497 V with united 100% Faradaic efficiency for anodic and cathodic reaction and continuous 20 h of operation without obvious decay. The electrocatalytic hydrogen production with the assistance of alternative oxidation by the robust electrocatalyst can be further used to realize the upgrading of other organic molecules with less energy consumption.
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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".