Coupling of Engineered High Entropy Alloys with Semiconducting TiO <sub>2</sub> Nanofilms for Scalable and Ultrafast Alkaline Hydrogen Evolution Reaction
Bibliographic record
Abstract
Abstract High entropy alloys (HEAs) are a promising class of electrocatalysts because of their high reactivity. However, the development of scalable synthesis strategies and fundamental understanding of their interfacial synergy with metal oxides remains underexplored. Herein, a new approach is reported for the fabrication of hybrid photoelectrocatalysts combining PtFeCoNiCu HEA structures with titanium dioxide (TiO 2 ) nanofilms via sequential anodization and electrodeposition. The TiO 2 nanofilms function as both a photoactive semiconducting framework and nanostructured substrate, enabling controlled nucleation and growth of HEA nanoparticles through a Volmer–Weber mechanism. The resulting hybrid photoelectrocatalysts exhibit outstanding hydrogen evolution reaction (HER) performance, achieving an ultralow overpotential of –11 mV at 10 mA cm −2 under simultaneous illumination and elevated electrolyte temperature. Mechanistic studies combining in situ Raman spectroscopy and density functional theory simulations reveal that HER occurs through three distinct stages, during which the TiO 2 support undergoes dynamic structural and electronic evolution – from a passive scaffold to an electron‐buffering layer. This process involves Ti⁴⁺ reduction, hydrogen intercalation, and accelerated turnover of OH * intermediates, which collectively enhance interfacial charge transfer and broaden active‐site availability. These findings provide new insights into the dynamic interplay between HEAs–semiconducting metal oxide substrates, enabling a generalizable design strategy for scalable, high‐performance photoelectrocatalysts.
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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) | 0.000 | 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".