Efficient Photoelectrochemical Hydrogen Generation Using Eco-Friendly “Giant” InP/ZnSe Core/Shell Quantum Dots
Bibliographic record
Abstract
InP quantum dots (QDs) are promising building blocks for use in solar technologies because of their low intrinsic toxicity, narrow bandgap, large absorption coefficient, and low-cost solution synthesis. However, the high surface trap density of InP QDs reduces their energy conversion efficiency and degrades their long-term stability. Encapsulating InP QDs into a wider bandgap shell is desirable to eliminate surface traps and improve optoelectronic properties. Here, we report the synthesis of “giant” InP/ZnSe core/shell QDs with tunable ZnSe shell thickness to investigate the effect of the shell thickness on the optoelectronic properties and the photoelectrochemical (PEC) performance for hydrogen generation. The optical results demonstrate that ZnSe shell growth (0.9–2.8 nm) facilitates the delocalization of electrons and holes into the shell region. The ZnSe shell simultaneously acts as a passivation layer to protect the surface of InP QDs and as a spatial tunneling barrier to extract photoexcited electrons and holes. Thus, engineering the ZnSe shell thickness is crucial for the photoexcited electrons and hole transfer dynamics to tune the optoelectronic properties of “giant” InP/ZnSe core/shell QDs. We obtained an outstanding photocurrent density of 6.2 mA cm –1 for an optimal ZnSe shell thickness of 1.6 nm, which is 288% higher than the values achieved from bare InP QD-based PEC cells. Understanding the effect of shell thickness on surface passivation and carrier dynamics offers fundamental insights into the suitable design and realization of eco-friendly InP-based “giant” core/shell QDs toward improving device performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".