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 MoO 3 through Ni doping (sample named Ni x Mo 1− x O 3 ) is reported. Ni is incorporated to replace Mo atoms in MoO 3 . Specifically, Ni x Mo 1− x O 3 is combined with optimal adsorption energy, along with MoO 2 /Mo 2 N hybrid with high conductivity. The resulting Ni x Mo 1− x O 3 supported on MoO 2 /Mo 2 N hybrid (sample named as Ni x Mo 1− x O 3 ‐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 Ni x Mo 1− x O 3 ‐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 MoO 3 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".