Water in Electrocatalysis
Bibliographic record
Abstract
Abstract Renewable electricity‐powered electrocatalysis technologies occupy a central position in clean energy conversion and the pursuit of a net‐zero carbon emission future. Water can serve multiple roles in electrocatalytic reactions, for instance, as a reaction medium, reactant, modifier, promoter, etc. This significantly influences the mass transport, active site, intermediate adsorption and reaction kinetics, ultimately determining the electrocatalytic performance (e.g., activity, selectivity, and stability) as well as device efficiency. As the heart location where electrocatalytic reactions occur, the typical electrical‐double layer is established at a water‐electrode interface. Therefore, the comprehension and regulation of water are crucial topics in electrocatalysis, which encourages us to organize this review. We begin with the fundamental understanding on structure of water and its behavior under electrochemical conditions. Subsequently, we delve into the “water effect” by elucidating specific functions of water in electrocatalysis. Recent advances in manipulating water to enhance electrocatalytic efficiency of representative reactions such as hydrogen evolution/oxidation, oxygen evolution/reduction, CO 2 reduction, N 2 reduction and organic electrosynthesis, are also highlighted. We finally discuss the remaining challenges and future opportunities in this field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".