Reabsorption Losses in Luminescent Solar Concentrators: Effect of the Band Gap of Semiconductor Quantum Dots, their Size and Dispersion
Bibliographic record
Abstract
We investigate the effect of semiconductor quantum dots (QDs) radius r and its dispersion r on the re absorption during a luminescence process. QDs are promising as chromophores in luminescence solar concentrators (LSCs). To minimize detrimental reabsorption losses, six semiconduc tors, typically used to fabricate QDs, with a wide range of the bulk bandgaps Eg0 have been considered: CdS (Eg0 = 2.42 eV), CdSe (Eg0 = 1.67 eV), CdTe (Eg0 = 1.5 eV), InP (Eg0 = 1.27 eV), InAs (Eg0 = 0.355 eV), and PbSe (Eg0 = 0.27 eV). We prove that by adjusting the QD radius r and dispersion r, it is possible to optimize nanocrystal dimensions to minimize the reabsorption. It was shown that for the semiconductor bulk band gap range between 2.42 eV to 1.27 eV there is always the optimum QD size and its dispersion, at which the reabsorption is below the total experimental error of the measured normal ized both absorption coefficient and luminescence intensity. Further reduction of Eg0 increases the reabsorption at any val ues of r and r: for instance, for PbSe based QD with Eg0 = 0.27 eV, 1 nm mean radius and its 1% dispersion, the reabsorp tion reaches 54%. We estimate the width of the part of the solar spectrum, from which the photons contribute to the lumines cence processes. This is important for several LSCs, stalked on top of each other.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".