Quantum Dot Based Heterostructures for Unassisted Photoelectrochemical Hydrogen Generation
Bibliographic record
Abstract
Abstract TiO2 hollow nanowires (HNWs) and nanoparticles (NPs) constitute promising architectures for QDs sensitized photoanodes for H2 generation. We sensitize these structures with CdS/CdSe quantum dots by two different methods (chemical bath deposition, CBD and succesive ionic layer adsorption and reaction, SILAR) and evaluate the performance of these photoelectrodes. Remarkable photocurrents of 4 mA·cm and 8 mA·cm−2 and hydrogen generation rates of 40 ml·cm−2·day−1 and 80 ml·cm−2·day−1 have been obtained in a three electrode configuration with sacrificial hole scavengers (Na2S and Na2SO3), for HNWs and NPs respectively, which is confirmed through gas analysis. More importantly, autonomous generation of H2 (20 ml·cm−2·day−1 corresponding to 2 mA·cm−2 photocurrent) is obtained in a two electrode configuration at short circuit under 100 mW·cm−2 illumination, clearly showing that these photoanodes can produce hydrogen without the assistance of any external bias. To the best of the authors' knowledge, this is the highest unbiased solar H2 generation rate reported for these of QDs based heterostructures. Impedance spectroscopy measurements show similar electron density of trap states below the TiO2 conduction band while the recombination resistance was higher for HNWs, consistently with the much lower surface area compared to NPs. However, the conductivity of both structures is similar, in spite of the one dimensional character of HNWs, which leaves some room for improvement of these nanowired structures. The effect of the QDs deposition method is also evaluated. Both structures show remarkable stability without any appreciable photocurrent loss after 0.5 hour of operation. The findings of this study constitute a relevant step towards the feasibility of hydrogen generation with wide bandgap semiconductors/quantum dots based heterostructures.
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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".