Perovskite Nanocrystals: Opportunities in Luminescent Solar Concentrators
Bibliographic record
Abstract
Abstract Luminescent solar concentrators (LSCs) are complementary sunlight collectors for photovoltaics (PVs). Emissive fluorophores embedded in a transparent waveguide collect solar radiation over a large area and convert it into luminescence directed to the PV cells that frame the waveguide's edges. Among various fluorophores, perovskite nanocrystals (PNCs) show considerable potential for LSCs thanks to their wide size/composition/shape tunable broad absorption spectrum ranging from UV to near‐infrared, which significantly overlaps with the solar spectrum. They also feature high brightness with a photoluminescence quantum yield of up to 100% and ease of fabrication through wet chemistry approaches. In addition, PNCs can be engineered to minimize the absorption/emission overlap, which is the key to suppressing energy losses caused by reabsorption. Here, the structure and properties of PNCs and then correlate them with LSC performance is presented. The synthesis of PNCs using wet‐chemistry approaches and summarize the latest developments of PNCs‐based LSCs, categorized by the engineering strategies of PNCs and the design of the LSC configurations is critically reviewed. Finally, it is described major challenges and perspectives for future work, outlining the rational design, synthesis, PNC loading, surface engineering, and machine‐learning‐based tuning of PNC‐LSC.
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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 | 0.000 | 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 teacher head, 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".