An Effective Approach to Enhance Hydrogen Evolution Reaction and Hydrogen Oxidation Reaction by Ni Doping to MoO<sub>3</sub>
Bibliographic record
Abstract
Abstract The development of bifunctional catalysts that facilitate both the hydrogen evolution reaction (HER) and hydrogen oxidation reaction (HOR) in alkaline environment is crucial for realizing unitized regenerative anion‐exchange membrane fuel cells. In this study, a novel strategy to modulate the electron density of MoO3 through Ni doping (sample named NixMo1−xO3) is reported. Ni is incorporated to replace Mo atoms in MoO3. Specifically, NixMo1−xO3 is combined with optimal adsorption energy, along with MoO2/Mo2N hybrid with high conductivity. The resulting NixMo1−xO3 supported on MoO2/Mo2N hybrid (sample named as NixMo1−xO3‐H) exhibits excellent alkaline HER activity, with an overpotential of only 16 mV at 10 mA cm−2 and a Tafel slope of 54 mV dec−1. In addition, the NixMo1−xO3‐H demonstrates an ultrahigh HOR performance with a high exchange current density (3.852 mA cm−2). The catalyst's breakdown potential of 0.23 V indicates its ability to withstand higher voltages without breaking down. As evidenced by the results, this characteristic leads to improved stability. These results are higher than those of the other catalysts reported, which indicates that the electron density of MoO3 can be effectively modulated through Ni doping, leading to excellent HER and HOR performance.
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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".