Rational Control of Near‐Infrared Colloidal Thick‐Shell Eco‐Friendly Quantum Dots for Solar Energy Conversion
Bibliographic record
Abstract
Abstract Thick‐shell colloidal quantum dots (QDs) are promising building blocks for solar technologies due to their size/composition/shape‐tunable properties. However, most well‐performed thick‐shell QDs suffer from frequent use of toxic metal elements including Pb and Cd, and inadequate light absorption in the visible and near‐infrared (NIR) region due to the wide bandgap of the shell. In this work, eco‐friendly AgInSe2/AgInS2 core/shell QDs, which are optically active in the NIR region and are suitable candidates to fabricate devices for solar energy conversion, are developed. Direct synthesis suffers from simultaneously controlling the reactivity of multiple precursors, instead, a template‐assisted cation exchange method is used. By modulating the monolayer growth of template QDs, gradient AgInSeS shell layers are incorporated into AgInSe2/AgInS2 QDs. The resulting AgInSe2/AgInSeS/AgInS2 exhibits better charge transfer than AgInSe2/AgInS2 due to their favorable electronic band alignment, as predicted by first‐principle calculations and confirmed by transient fluorescence spectroscopy. The photoelectrochemical cells fabricated with AgInSe2/AgInSeS/AgInS2 QDs present ≈1.5‐fold higher current density and better stability compared to AgInSe2/AgInS2. The findings define a promising approach toward multinary QDs and pave the way for engineering the QDs’ electronic band structures for solar‐energy conversion.
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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".