A Synergic Effect of Bi-metallic Layered Hydro-Oxide Cocatalyst on 1-D TiO2 Driven Photoelectrochemical Water Splitting
Bibliographic record
Abstract
Photoelectrochemical (PEC) water splitting is one of the most sustainable approaches for converting solar energy into hydrogen fuel. Affordable and robust photoelectrodes are crucial for the commercialization of PEC technologies. Recently, transition metal-based co-catalysts, especially Ni- and Fe-based catalysts, have attracted much interest owing to their exceptional OER characteristics. Given this, we here proposed the decoration of a Fe-Ni-based cocatalyst on the surface of the TiO2 photoanode for PEC water splitting. The TiO2 photoanode was hydrothermally synthesized and then decorated by Fe-Ni hydroxide catalyst using photo-assisted electrodeposition. The optimized TiO2/FeNiOOH photoanode exhibited the maximum photocurrent density value of 1.36 mA cm−2, which is almost twice the value obtained for bare TiO2, at 1.23 V vs RHE under the AM 1.5 G illumination. Due to the enhanced light absorption in the UV region, the optimized photoanode exhibited remarkable IPCE and photoconversion efficiency of 87.8% and 0.93%, respectively. Furthermore, excellent faradaic efficiencies of ∼90% for H2 and ∼70% for O2 generations were obtained. Predominantly, the enhancement in the photocurrent potentials was explained in detail. Our study shows the roles and benefits of using bimetallic catalysts with TiO2 photoanodes for sustainable water-splitting applications.
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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".