Ternary Earth-Abundant Catalyst Enabling Stable Silicon Photocathodes for Solar Hydrogen Generation
Bibliographic record
Abstract
Silicon-based photocathodes offer great promise for scalable photoelectrochemical (PEC) hydrogen production due to their earth abundance and optimal bandgap, yet their practical deployment remains hindered by interfacial instability and sluggish catalytic kinetics. Herein, we report a CoMoS ternary bimetallic catalyst, deposited via a photoelectrodeposition method onto a TiO 2 -passivated Si substrate, enabling efficient and durable PEC hydrogen evolution. The research found that Co–Mo has a strong electronic interaction and charge distribution. The incorporation of Mo into CoS optimizes its electronic structure and regulates the hydrogen adsorption free energy, thereby enhancing HER activity. Mo incorporation lowers the reaction energy barriers for hydrogen adsorption and desorption, providing highly active sites that facilitate intermediate transfer, reduce activation energy, and boost intrinsic catalytic activity. These improvements originate from the robust electronic coupling between Mo and Co, as well as the synergistic effects at the heterogeneous interface, which effectively modulate the local electronic structure and band alignment, thereby enhancing charge transfer kinetics. Comprehensive carrier dynamics analyses based on IMVS, IMPS, EIS, OCP, and transient photocurrent measurements demonstrate substantially reduced interfacial resistance, extended carrier lifetime, and faster charge transport. The optimized CoMoS/TiO 2 /Si photocathode delivers an onset potential of 0.69 V RHE, a high photocurrent density of 31.2 mA cm –2 at 0 V RHE, and an applied bias photon-to-current efficiency (ABPE) of 7.04%, while maintaining exceptional operational stability. These results highlight the viability of nonprecious metal catalysts and structural tuning strategies for advancing silicon-based PEC devices toward real-world 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".