Controllable Thermochemical Generation of Active Defects in the Horizontal/Vertical MoS<sub>2</sub> for Enhanced Hydrogen Evolution
Bibliographic record
Abstract
Abstract As for 2D transition metal dichalcogenides, the creation of proper active defects concentrations is considered as the efficient strategy for improving hydrogen evolution performance. However, the synthesis methods of large‐area MoS 2 catalysts with controllable active defects are limited, also for its working mechanism. Herein, thermochemical generation of active defects for MoS 2 catalysts has established by annealing sodium hypophosphite, in which the phosphine is spontaneously generated and chemically tailors the MoS 2 lattice. The defects formation is confirmed by the investigation of slightly‐changed surface structure and unpaired electrons for the annealed samples. The hydrogen evolution reaction performances of horizontally/vertically grown MoS 2 films are improved by controlling reaction conditions, indicating the active defects could form in the basal plane and edges with retained crystal structure. The overpotential of MoS 2 samples converted from 10 nm Mo reduces from −520 to −265 mV with largely decreased Tafel slope. The electrochemical microreactor studies reveal the protons adsorption of active sites shows much more significant contribution, than interfacial charge transfer with the enhanced remarkable performance (−100 mV at 10 mA cm −2 ). This study presents the large‐area synthesized strategy for MoS 2 based catalysts with controllable defects concentration and helps establish rational design principles for future MoS 2 family electrocatalysts.
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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".