Recent advances in hydrogen production using MXenes-based metal sulfide photocatalysts
Bibliographic record
Abstract
At present, the composition and crystalline structure of transition metal nitrides or carbides (MXenes) and their derivatives are continuously expanding due to their unique physicochemical properties, especially in the photocatalytic field. Advances over the past four years have led to improved preparation of new MAX phases, resulting in new MXenes with excellent photo-thermal effect, considerable specific surface area, long-term stability and optimum activity. Since MXenes have good electrical conductivity and their bandgap is adjustable under the visible light range, this group is one of the best promising candidates for hydrogen production from photo-splitting of water as an environment-friendly method of converting sunlight to chemical energy. Progress in noble metal-free photocatalyst associated with more understanding of the fundamental mechanism of photocatalysis has enabled a proper choice of cocatalyst with better efficiency. In this study, the photocatalytic production of hydrogen through MXens as a support and co-catalyst on metal sulfide is summarized and discussed. Recent advances in the design and synthesis of MXenes-based metal sulfide nanocomposites to increase the efficiency of photocatalytic hydrogen production are then highlighted. Finally, the challenges and future prospects for the development of MXenes-based metal sulfide composites are outlined.
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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 | 0.001 |
| 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".