Recent Advances of Bimetallic Sulfides‐Based Nanomaterials for Photocatalytic Hydrogen Production
Bibliographic record
Abstract
Photocatalytic hydrogen production from water splitting is an environmentally friendly and cost‐effective technology to achieve green hydrogen. In recent years, bimetallic sulfides (BMS) have been considered as promising candidates compared to monometallic sulfides due to the tunable energy band structure, higher number of active sites, and good chemical stability, resulting in high performance of photocatalytic hydrogen production. Herein, recent progress of BMS in photocatalytic hydrogen production has been addressed comprehensively. First, two commonly employed methods for synthesizing BMS with tailored morphological characteristics are discussed and compared. Then, the main functions of BMS are unraveled to aid in promoting photocatalytic hydrogen evolution performance intrinsically. Detailed applications of BMS both as a single photocatalyst and in heterojunction composite systems of three typical categories with unique properties and catalytic performance are summarized, focusing on their charge transfer behaviors and hydrogen production performance. In the end, research trends and prospects of BMS‐based photocatalysts are also proposed. This review is believed to unveil new advances and features of BMS‐based nanomaterials toward practical benefits and future research for highly efficient and robust photocatalytic H2 generation catalysts.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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".