Value‐Added Formate Production from Selective Methanol Oxidation as Anodic Reaction to Enhance Electrochemical Hydrogen Cogeneration
Bibliographic record
Abstract
Abstract Electrolytic overall water splitting is a promising approach to produce H 2 , but its efficiency is severely limited by the sluggish kinetics of the oxygen evolution reaction (OER) and the low activity of current electrocatalysts. To solve these problems, in addition to the development of efficient precious‐metal catalysts, an effective strategy is proposed to replace the OER by the selective methanol oxidation reaction. Ni–Co hydroxide [Ni x Co 1− x (OH) 2 ] nanoarrays were obtained through a facile hydrothermal treatment as the bifunctional electrocatalysts for the co‐electrolysis of methanol/water to produce H 2 and value‐added formate simultaneously. The electrocatalyst could catalyze selective methanol oxidation (≈1.32 V) with a significantly lower energy consumption (≈0.2 V less) than OER. Importantly, methanol was transformed exclusively to value‐added formate with a high Faradaic efficiency (selectivity) close to 100 %. Specifically, a cell voltage of only approximately 1.5 V was required to generate a current density of 10 mA cm −2 . Furthermore, the Ni 0.33 Co 0.67 (OH) 2 /Ni foam nanoneedle arrays presented an outstanding stability for overall co‐electrolysis.
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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".