High‐performance Photoelectrochemical Hydrogen Production Using Asymmetric Quantum Dots
Bibliographic record
Abstract
Abstract Solar‐driven photoelectrochemical (PEC) reactions using colloidal quantum dots (QDs) as photoabsorbers have shown great potential for the production of clean fuels. However, the low H2 evolution rate, consistent with low values of photocurrent density, and their limited operational stability are still the main obstacles. To address these challenges, the heterostructure engineering of asymmetric capsule‐shaped CdSe/CdxZn1‐xSe QDs with broad absorption and efficient charge extraction compared to pure‐shell QDs is reported. By engineering the shell composition from pure ZnSe shells into CdxZn1‐xSe gradient shells, the electron transfer rate increased from 4.0 × 107 s −1 to 32.7 × 107 s −1 . Moreover, the capsule‐shaped architecture enables more efficient spatial carrier separation, yielding a saturated current density of average of 25.4 mA cm −2 under AM 1.5 G one sun illumination. This value is the highest ever observed for QDs‐based devices and comparable to the best‐known Si‐based devices, perovskite‐based devices, and metal oxide‐based devices. Furthermore, PEC devices based on heterostructured QDs maintained 96% of the initial current density after 2 h and 82% after 10 h under continuous illumination, respectively. The results represent a breakthrough in hydrogen production using heterostructured asymmetric QDs.
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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".