Engineering the Optical Properties of Eco‐Friendly CuGaS<sub>2</sub>/ZnS and CuGaInS<sub>2</sub>/ZnS Core/Shell Quantum Dots for High‐Performance Tandem Luminescent Solar Concentrators
Bibliographic record
Abstract
Herein, highly luminescent eco‐friendly CuGaS2/ZnS (CGS/ZnS) and CuGaInS2/ZnS (CGIS/ZnS) core/shell quantum dots (QDs) are rationally prepared for luminescent solar concentrator (LSC) application. It is demonstrated that the optical properties of these core/shell QDs can be tailored by engineering the ZnS shell thickness, leading to large Stokes shifts and high‐photoluminescence quantum yields up to 94.6%. As‐synthesized core/shell QDs with optimized optical properties are employed to fabricate LSCs (5 × 5 × 0.5 cm3) using glasses as waveguides, wherein the individual CGS/ZnS and CGIS/ZnS QD‐based LSCs, respectively, exhibit an optical efficiency (η opt) of ≈3.26% and 6.53% under AM1.5G illumination (100 mW cm−2). Remarkably, a tandem QDs‐LSC integrated via vertical stacking of the top yellow‐emitting CGS/ZnS QDs‐LSC and bottom red‐emitting CGIS/ZnS QDs‐LSC delivers an optical efficiency (η opt) as high as 9.94%, which is, respectively, ≈3 and 1.5 times higher than the individual QDs‐LSCs and is comparable to various best‐reported QDs‐LSCs. The results indicate that environment‐benign I–III–VI2 core/shell QDs with engineered optical properties and LSC architectural design are promising to develop future cost‐effective and high‐performing building‐integrated photovoltaics.
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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".