Tuning Binding Strength of Multiple Intermediates towards Efficient pH‐universal Electrocatalytic Hydrogen Evolution by Mo <sub>8</sub> O <sub>26</sub> ‐NbN <sub>x</sub> O <sub>y</sub> Heterocatalysts
Bibliographic record
Abstract
Abstract Developing efficient and robust hydrogen evolution reaction (HER) catalysts for scalable and sustainable hydrogen production through electrochemical water splitting is strategic and challenging. Herein, heterogeneous Mo 8 O 26 ‐NbN x O y supported on N‐doped graphene (defined as Mo 8 O 26 ‐NbN x O y /NG) is synthesized by controllable hydrothermal reaction and nitridation process. The O‐exposed Mo 8 O 26 clusters covalently confined on NbN x O y nanodomains provide a distinctive interface configuration and appropriate electronic structure, where fully exposed multiple active sites give excellent HER performance beyond commercial Pt/C catalyst in pH‐universal electrolytes. Theoretical studies reveal that the Mo 8 O 26 ‐NbN x O y interface with electronic reconstruction affords near‐optimal hydrogen adsorption energy and enhanced initial H 2 O adsorption. Furthermore, the terminal O atoms in Mo 8 O 26 clusters cooperate with Nb atoms to promote the initial H 2 O adsorption, and subsequently reduce the H 2 O dissociation energy, accelerating the entire HER kinetics.
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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".