One-Dimensional CdS/Carbon/Au Plasmonic Nanoarray Photoanodes via In Situ Reduction–Graphitization Approach toward Efficient Solar Hydrogen Evolution
Bibliographic record
Abstract
Photoelectrochemical (PEC) hydrogen evolution has been acknowledged as a promising “green” technique to convert solar energy into clean chemical fuel. Photoanodes play a key role in determining the performance of PEC systems, spurring numerous efforts to develop advanced materials as well as structures to improve the photoconversion efficiency. In this work, we report the rational design of a plasmonic hierarchical nanorod array, composed of oriented one-dimensional (1D) CdS nanorods decorated with a uniformly wrapped graphite-like carbon (C PDA ) layer and Au nanoparticles (Au NPs), as highly efficient photoanode materials. An interfacial in situ reduction–graphitization method has been conducted to prepare the CdS/C PDA /Au nanoarchitecture, where polydopamine (PDA) coating was used as a C source and a reductant. The CdS/C PDA /Au nanoarray photoanode demonstrates superior photoconversion efficiency with a photocurrent density of 8.74 mA/cm 2 and an IPCE value (480 nm) of 30.2% (at 1.23 V vs RHE), under simulated sunlight irradiation, which are 12.7 and 13.5 times higher than pristine CdS. The significant enhancement of PEC performance is mainly benefited from the increase of the entire quantum yield and efficiency due to the formation of a Schottky rectifier, localized surface plasmon resonance (LSPR)-enhanced light absorption, and promoted hot-electron injection from interlayered graphene-like carbon. More importantly, thanks to the inhibited charge carrier recombination process and transferred oxidation reaction sites, the fabricated CdS/C PDA /Au photoelectrode exhibits lengthened electron lifetimes and better photostability, illustrating its wonderful potential for future PEC application.
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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".