Harnessing the Synergetic Effects of Ag, Mn Dopants in Eco‐Friendly Ultraviolet Selective Quantum Dots for Luminescent Solar Concentrators
Bibliographic record
Abstract
Abstract Quantum dots (QDs) are promising building blocks for luminescent solar concentrators (LSCs), yet most QD‐based LSCs suffer from toxic metal composition and color tinting. UV‐selective harvesting QDs can enable visible transparency, but their development is restricted by large reabsorption losses and low photoluminescence quantum yield (PLQY). The developed here Ag, Mn: ZnInS2/ZnS QDs show a high PLQY of 53% due to the passivating effect of ZnS shell. These QDs selectively absorb UV light and emit orange‐red light with a large Stokes shift of 180 nm. A LSC of 5 × 5 × 0.2 cm3, fabricated using a poly(lauryl methacrylate) (PLMA) as a matrix, maintains 87% of integrated PL after 7 h of UV exposure. The QD‐PLMA achieved 90.7% average visible transparency (AVT) and a color rendering index (CRI) of 95.8, which is close to plain PLMA (AVT = 90.8%; CRI = 99.5), yielding excellent visible light transparency. Incorporating Si‐PVs at LSC edges, the Ag, Mn: ZIS/ZnS QD‐LSC achieved an optical efficiency of 1.42%, ranking competitively among high‐performing UV‐harvesting LSCs.
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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) | 0.001 | 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".