Transition Metal Disulfides as Noble‐Metal‐Alternative Co‐Catalysts for Solar Hydrogen Production
Bibliographic record
Abstract
The production of hydrogen fuels by using sunlight is an attractive and sustainable solution to the global energy and environmental problems. Platinum (Pt) is known as the most efficient co‐catalyst in hydrogen evolution reaction (HER). However, due to its high‐cost and limited‐reserves, it is highly demanded to explore alternative non‐precious metal co‐catalysts with low‐cost and high efficiency. Transition metal disulfides (TMDs) including molybdenum disulfide and tungsten disulfide have been regarded as promising candidates to replace Pt for HER in recent years. Their unique structural and electronic properties allow them to have many opportunities to be designed as highly efficient co‐catalysts over various photo harvesting semiconductors. Recent progress in TMDs as photo‐cocatalysts in solar hydrogen production field is summarized, focusing on the effect of structural matchability with photoharvesters, band edges tunability, and phase transformation on the improvement of hydrogen production activities. Moreover, recent research efforts toward the TMDs as more energy‐efficient and economical co‐catalysts for HER are highlighted. Finally, this review concludes by critically summarizing both findings and current perspectives, and highlighting crucial issues that should be addressed in future research activities.
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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 | 0.001 |
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".