In Situ Observed Local Structural Distortions Boost Solar Water Splitting in Hematite
Bibliographic record
Abstract
Although hematite (α-Fe 2 O 3 ) is considered a promising photoanode material for photoelectrochemical (PEC) water splitting, its practical application is limited by inherently low charge transport efficiency and sluggish oxygen evolution reaction (OER) kinetics. In this study, in situ synchrotron X-ray absorption spectroscopy (XAS) was employed under illumination to distinctly observe the transient electronic structure changes at Fe sites and to elucidate their structure–activity relationship with the stretching of Fe–O bond. It is revealed that local distortions induced by Fe–O bond stretching enhance the hybridization of O 2p-Fe 3d orbitals, promote electron transfer, and facilitate the formation of Fe 4+ active sites. Such structural modulation significantly suppresses surface charge recombination, thus accelerating the OER kinetics. Leveraging this mechanism, an In-Fe 2 O 3 (LV) photoanode was constructed, delivering a photocurrent density of 3.66 mA cm –2 at 1.23 V RHE, with an onset potential negatively shifted to 0.86 V RHE . Upon coupling with a FeNiOOH cocatalyst, the photocurrent density is further enhanced to 4.32 mA cm –2, and the onset potential is reduced to 0.77 V RHE . This study employs atomic-scale in situ characterization to systematically elucidate the structure–activity relationship between local lattice distortions and enhanced OER performance, providing actionable insights and strategies for the rational design of high-efficiency photoanodes.
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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".