Stacked High‐Entropy Hydroxides Promote Charge Transfer Kinetics for Photoelectrochemical Water Splitting
Bibliographic record
Abstract
Abstract Although various kinds of cocatalyst are developed and decorated on the bismuth vanadate (BiVO4) photoanode, its photoelectrochemical (PEC) water splitting performance is limited owing to severe charge recombination and sluggish oxygen evolution reaction (OER). Herein, a high‐entropy hydroxide electrocatalyst (FeCoNiMoCrOOH) is constructed as a co‐catalyst deposited on BiVO4 with a good PEC activity and stability in potassium borate buffer, addressing substantial charge recombination and poor surface oxygen evolution reaction of the material. FeCoNiMoCrOOH synthesized by a simple electrodeposition stacking strategy, delivers an overpotential of 172 mV at 10 mA cm−2 with a stability of 600 h under alkaline conditions, representing one of the best performances on high‐entropy‐based catalysts. The FeCoNiMoCrOOH/BiVO4 photoanode shows a photocurrent density of 5.23 mA cm−2 at 1.23 VRHE with 100 h durability in potassium borate buffer. Experimental investigations and theoretical calculations demonstrate that the synergistic effect of Mo and Cr in FeCoNi catalyst effectively decreases the dissolution Fe, Co, and Ni after long‐term operation, increases the charge transfer kinetics, and promotes OER and PEC performances, therefore enhancing the photocorrosion resistance of BiVO4. This work provides a new avenue to design high entropy‐based electrocatalysts boosting solar water splitting activity and stability.
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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".