Fe Cluster Modified Co<sub>9</sub>S<sub>8</sub> Heterojunction: Highly Efficient Photoelectrocatalyst for Overall Water Splitting and Flexible Zinc‐Air Batteries
Bibliographic record
Abstract
Abstract Designing bifunctional low‐cost photo‐assisted electrocatalysts for converting solar and electric energy into hydrogen energy remains a huge challenge. Herein, a heterojunction (Fe cluster modified Co 9 S 8 loaded on carbon nanotubes, Co 9 S 8 ‐Fe@CNT) for both hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is demonstrated. Benefiting from the good electronic conductivity and spatial confinement of the carbon skeleton, as well as the electronic structure regulation of the Fe cluster, Co 9 S 8 ‐Fe@CNT exhibits excellent catalytic performance with a low overpotential of 150 mV for OER and 135 mV for HER at 10 mA cm −2 . Upon light irradiation, holes and electrons are generated in the valence band and conduction band of the Co 9 S 8 , respectively. Part of the charges are transferred to the interface to facilitate the catalytic reaction, while the remaining are transferred by the electrode. When working as a bifunctional catalyst for overall water splitting, the performance can reach 1.33 V at under light conditions, which is significantly better than 1.52 V in a dark environment. Theoretical calculations revealed lowered Gibbs free energy (∆ G H *) of the heterojunction with the effect of Fe modification of Co 9 S 8 . This work sheds a new light in designing novel photoelectrochemical materials to convert solar and electric energy into chemical energy.
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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".