Review on Colloidal Quantum Dots Luminescent Solar Concentrators
Bibliographic record
Abstract
Abstract Luminescent solar concentrators (LSCs) have recently gained popularity as an effective solution to increase solar energy conversion. Utilizing LSCs together with solar cells can generate more energy at a lower cost than using only solar cells. LSCs operate by utilizing luminophores, molecules that absorb incident solar irradiation and re‐emit photons, and waveguides that redirect emitted photons to the edges of a glass or polymer slab at high concentrations. Many quantum dots (QDs) have been the focus of much research as luminophores for LSCs, owing to their high quantum yields (QYs), controllable absorption/emission spectra, good stability, and ease of synthesis. Various QDs, such as CdSe, PbS, CdS, AgInS 2 , Si, and C, have been modified to enhance their optical performances in LSCs, often measured by their optical efficiencies, internal/external quantum efficiencies, and power conversion efficiencies. This review appraises the latest developments in colloidal QDs — basic QDs, doped QDs, core/shell QDs, hybrid QDs, and Si‐based QD — for their applications in LSCs. Other factors that enhance an LSC's efficiency, such as altering the polymer matrix and using distributed Bragg reflectors, are discussed. The development of highly efficient, QD‐based LSCs will be essential for increasing solar energy production worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".