Advanced Interface Engineering in Gradient Core/Shell Quantum Dots Enables Efficient Photoelectrochemical Hydrogen Evolution
Bibliographic record
Abstract
Abstract Semiconductor core/shell quantum dots (QDs) are considered promising building blocks to fabricate photoelectrochemical (PEC) cells for the direct conversion of solar energy into hydrogen (H2). However, the lattice mismatch between core and shell in such QDs results in undesirable defects and severe carrier recombination, limiting photo‐induced carrier separation/transfer and solar‐to‐fuel conversion efficiency. Here, an interface engineering approach is explored to minimize the core‐shell lattice mismatch in CdS/CdSexS1‐x (x = 0.09–1) core/shell QDs (g‐CSG). As a proof‐of‐concept, PEC cells based on g‐CSG QDs yield a remarkable photocurrent density of 13.1 mA cm−2 under AM 1.5 G one‐sun illumination (100 mW cm−2), which is ≈54.1% and ≈33.7% higher compared to that in CdS/CdSe0.5S0.5 (g‐CSA) and CdS/CdSe QDs (g‐CS), respectively. Theoretical calculations and carrier dynamics confirm more efficient carrier separation and charge transfer rate in g‐CSG QDs with respect to g‐CSA and g‐CS QDs. These results are attributed to the minimization of the core‐shell lattice mismatch by the cascade gradient shell in g‐CSG QDs, which modifies carrier confinement potential and reduces interfacial defects. This work provides fundamental insights into the interface engineering of core/shell QDs and may open up new avenues to boost the performance of PEC cells for H2 evolution and other QDs‐based optoelectronic devices.
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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".