Ru-P pair sites boost charge transport in hematite photoanodes for exceeding 1% efficient solar water splitting
Bibliographic record
Abstract
Fast transport of charge carriers in semiconductor photoelectrodes are a major determinant of the solar-to-hydrogen efficiency for photoelectrochemical (PEC) water slitting. While doping metal ions as single atoms/clusters in photoelectrodes has been popularly used to regulate their charge transport, PEC performances are often low due to the limited charge mobility and severe charge recombination. Here, we disperse Ru and P diatomic sites onto hematite (DASs Ru-P:Fe 2 O 3 ) to construct an efficient photoelectrode inspired by the concept of correlated single-atom engineering. The resultant photoanode shows superior photocurrent densities of 4.55 and 6.5 mA cm −2 at 1.23 and 1.50 V RHE , a low-onset potential of 0.58 V RHE , and a high applied bias photon-to-current conversion efficiency of 1.00% under one sun illumination, which are much better than the pristine Fe 2 O 3 . A detailed dynamic analysis reveals that a remarkable synergetic ineraction of the reduced recombination by a low Ru doping concentration with substitution of Fe site as well as the construction of Ru-P bonds in the material increases the carrier separation and fast charge transportation dynamics. A systematic simulation study further proves the superiority of the Ru-P bonds compared to the Ru-O bonds, which allows more long-lived carriers to participate in the water oxidation reaction. This work offers an effective strategy for enhancing charge carrier transportation dynamics by constructing pair sites into semiconductors, which may be extended to other photoelectrodes for solar water splitting.
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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 | 0.000 |
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".