Harnessing the properties of colloidal quantum dots in luminescent solar concentrators
Bibliographic record
Abstract
Luminescent solar concentrators (LSCs) can serve as large-area sunlight collectors, are suitable for applications in high-efficiency and cost-effective photovoltaics (PVs), and provide adaptability to the needs of architects for building-integrated PVs, which makes them an attractive option for transforming buildings into transparent or non-transparent electricity generators. Compared with traditional organic dyes, colloidal semiconducting quantum dots (QDs) are excellent candidates as emitters for LSCs because they exhibit wide size/shape/composition-tunable absorption spectra ranging from ultraviolet to near infrared, significantly overlapping with the solar spectrum. They also feature narrow emission spectra, high photoluminescence quantum yields, high absorption coefficients, solution processability and good photostability. Most importantly, QDs can be engineered to provide a minimal overlap between absorption and emission spectra, which is key to the realization of large-area LSCs with largely suppressed reabsorption energy losses. In this review article, we will first present and discuss the working principle of LSCs, the synthesis of colloidal QDs using wet-chemistry approaches, the optical properties of QDs, their band alignment and the intrinsic relationship between the band energy structure and optical properties of QDs. We focus on emerging architectures, such as core/shell QDs. We then highlight recent progress in QD-based LSCs and their anticipated applications. We conclude this review article with the major challenges and perspectives of LSCs in future commercial technologies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".