Transition metal phosphides as noble-metal-alternative co-catalysts for solar hydrogen production
Bibliographic record
Abstract
Hydrogen (H2) emerges as a highly promising contender for replacing conventional fossil fuels due to its high combustion heat value and net-zero greenhouse gas emission. Photocatalytic H2 generation through semiconductor-based water splitting represents a clean and sustainable technology in the field. Developing highly efficient and abundant source semiconductor materials, along with co-catalysts, is paramount in achieving the industrial-level H2 evolution by photocatalysis technology. In recent years, transition-metal phosphides (TMPs) have emerged as powerful co-catalysts for photocatalytic reactions due to their cost-effectiveness, abundant reserves in the earth’s crust, and favorable physicochemical properties, thus offering a viable alternative to conventional precious metal materials. In this review, we first provide a concise historical overview and outline the structure of TMPs. The synthetic strategies of TMPs are subsequently systematically analyzed based on diverse phosphorus sources. Additionally, this review provides a comprehensive summary of the recent research endeavors conducted on TMPs as potential photocatalytic co-catalysts for efficient hydrogen generation through photocatalysis. Eventually, this review briefly addresses the prevailing key concerns, proposed countermeasures, and forthcoming challenges associated with enhancing the efficiency of photocatalytic H2 evolution in TMPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".