Improving power conversion efficiency in luminescent solar concentrators using nanoparticle fluorescence and scattering
Bibliographic record
Abstract
Abstract Large-size luminescent solar concentrators (LSCs), which act as a complement to silicon-based photovoltaic (Si-PV) systems, still suffer from low power conversion efficiency (PCE). How to improve the performance of LSCs, especially large ones, is currently a hot research topic. Traditional LSCs have only a single transmission mode of fluorescence from the luminescent materials to the Si-PV, but here we introduce a new idea to improve the absorption of Si-PV by employing dual transmission modes of both fluorescence and scattering light. To prepare LSCs with dual mode transmission, Si-PV systems are coupled around the edges of a light-harvesting slice, which is prepared by ultraviolet light-induced polymerization of methyl methacrylate (MMA) solution containing both luminescent CsPbBr 3 and TiO 2 nanocrystals (NCs). When the sun light or incident light is coupled into the light-harvesting slice, CsPbBr 3 NCs can convert the incident light into fluorescence, and then partly transmit to Si-PV at the edges, where the light is finally converted into electrical energy. Besides the traditional fluorescence transmission mode, the addition of TiO 2 brings another transmission mode, namely the scattering of incident light to Si-PV, leading to an increase in PCE. In comparison to that of pure CsPbBr 3 -based LSCs without the addition of TiO 2 (0.97%), the PCE of TiO 2 -doped LSCs with a large size of 20 cm × 20 cm is improved to 1.82%. The maximal PCE appears for LSCs with a size of 5 cm × 5 cm, reaching 2.62%. The reported method of dual transmission modes is a new alternative way to improve the performance of LSC devices, which does not need to change the optical properties of luminescent materials. Moreover, the production process is simple, low-cost and suitable for preparing large area LSCs, further promoting the application of LSCs.
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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) | 0.000 | 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".