In-situ surface engineering of ternary eco-friendly QDs for enhanced photoelectrochemical hydrogen production
Bibliographic record
Abstract
Ternary I-III-VI quantum dots (QDs) have recently received wide attention in solar energy conversion technologies because of their non-toxicity, tunable band gap, and composition-dependant properties. However, their complex non-stoichiometry induces high density of surface traps/defects which significantly affects solar energy conversion efficiencies and long-term stability. This work presents an in-situ growth passivation approach to encapsulate ternary Cu:ZnInSe with ZnSeS alloyed shell (CZISe/ZSeS QDs) as light harvesters for solar-driven photoelectrochemical (PEC) hydrogen (H 2 ) production. The engineered CZISe/ZSeS QDs coupled with TiO 2 -MWCNTs hybrid photoanode exhibit a high photocurrent density of 13.15 mA/cm 2 at 0.8 V vs RHE under 1 sun illumination, which is 20.5 % higher than bare CZISe QDs/TiO 2 photoanode based device. In addition, we observed a 48 % enhancement in the long-term stability with ~88 % current retained after 6000 s. These results indicate that the effective shell passivation has mitigated the surface traps/defects, leading to suppressed charge recombination and improved charge transfer efficiency, as confirmed by optoelectronic, carrier dynamics measurements and theoretical simulations. The findings hold great promise on improving the performance of ternary/multinary eco-friendly colloidal QDs by surface engineering for effective utilization in solar energy conversion technologies.
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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".