Harvesting Hot Holes in Plasmon-Coupled Ultrathin Photoanodes for High-Performance Photoelectrochemical Water Splitting
Bibliographic record
Abstract
The harvesting of hot carriers produced by plasmon decay to generate electricity or drive a chemical reaction enables the reduction of the thermalization losses associated with supra-band gap photons in semiconductor photoelectrochemical (PEC) cells. Through the broadband harvesting of light, hot-carrier PEC devices also produce a sensitizing effect in heterojunctions with wide-band gap metal oxide semiconductors possessing good photostability and catalytic activity but poor absorption of visible wavelength photons. There are several reports of hot electrons in Au injected over the Schottky barrier into crystalline TiO 2 and subsequently utilized to drive a chemical reaction but very few reports of hot hole harvesting. In this work, we demonstrate the efficient harvesting of hot holes in Au nanoparticles (Au NPs) covered with a thin layer of amorphous TiO 2 (a-TiO 2 ). Under AM1.5G 1 sun illumination, photoanodes consisting of a single layer of ∼50 nm diameter Au NPs coated with a 10 nm shell of a-TiO 2 (Au@a-TiO 2 ) generated 2.5 mA cm –2 of photocurrent in 1 M KOH under 0.6 V external bias, rising to 3.7 mA cm –2 in the presence of a hole scavenger (methanol). The quantum yield for hot-carrier-mediated photocurrent generation was estimated to be close to unity for high-energy photons (λ < 420 nm). Au@a-TiO 2 photoelectrodes produced a small positive photocurrent of 0.1 mA cm –2 even at a bias of −0.6 V indicating extraction of hot holes even at a strong negative bias. These results together with density functional theory modeling and scanning Kelvin probe force microscope data indicate fast injection of hot holes from Au NPs into a-TiO 2 and light harvesting performed near-exclusively by Au NPs. For comparison, Au NPs coated with a 10 nm shell of Al 2 O 3 (Au@Al 2 O 3 ) generated 0.02 mA cm –2 of photocurrent in 1 M KOH under 0.6 V external bias. These results underscore the critical role played by a-TiO 2 in the extraction of holes in Au@a-TiO 2 photoanodes, which is not replicated by an ordinary dielectric shell. It is also demonstrated here that an ultrathin photoanode (<100 nm in maximum thickness) can efficiently drive sunlight-driven water splitting.
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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".