A Comprehensive Review of MoS<sub><i>x</i></sub> for Improved Photo(electro)catalytic Performance
Bibliographic record
Abstract
As an efficient hydrogen precipitation site, MoSx shows great promise for energy production and environmental remediation applications due to its low cost, easy modulation, multiple complex valence states, and excellent catalytic performance. Therefore, the development of MoSx synthesis strategies, catalytic enhancement mechanisms, and catalytic applications is reviewed. First, six synthesis strategies regarding MoSx are mainly outlined: electrochemical deposition, photodeposition, hydrothermal/solvent thermal, pulsed laser deposition, precipitation, and calcination, and their advantages and disadvantages are described in detail. Second, catalytic enhancement mechanisms of the composite strategies are elucidated based on the trends of MoSx electronic properties, valence species, light‐absorption range, and interfacial charge transfer. Third, applications of MoSx in photo(electro)catalysis in recent years are systematically reviewed. Finally, the current shortcomings and future research directions of MoSx are discussed from the perspectives of synthesis strategies and practical applications, respectively.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".