Perovskite luminescent solar concentrators for photovoltaics
Bibliographic record
Abstract
As large-area photon collection devices designed for photovoltaics, luminescent solar concentrators (LSCs) have been proposed for more than 40 years. In recent years, the perovskite-based LSCs have received much interest for the convenient preparation process and low cost along with high quantum yields of perovskite luminophores. However, optical losses, such as non-radiative recombination loss and reabsorption loss, seriously impair the performance of LSCs and further impede the commercialization of such promising photovoltaic devices. Various strategies, such as increasing the Stokes shift and defect passivation, have been implemented to enhance the optical performance in perovskite-based LSCs. Here, we appraise the applications of perovskite luminophores in LSCs and review the typical preparation method of perovskite-based LSCs. The state-of-the-art solutions are presented to address the optical losses, leading to the demonstration of enabling high-performance perovskite-based 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".