In Situ‐Prepared Attachable Transparent Luminescent Solar Concentrators for Photovoltaic with Polymer Antireflection/Barrier Layer
Bibliographic record
Abstract
As large‐area photon collection devices designed to convert sunlight into electricity, luminescent solar concentrators (LSCs) have been proposed for more than 40 years. In practical sunlight‐harvesting applications, existing glass windows or curtain walls have to be torn down and then replaced by traditional LSCs with planar optical waveguides, leading to high manufacture and installation costs. One alternative and attractive approach is to design and manufacture LSCs that are compatible with and can be attached directly onto the original building glass windows, which substantially reduces the overall costs. Herein, a feasible strategy of attachable transparent LSCs is proposed, converting ordinary glass to LSCs by simply attaching novel luminescent films. By integrating a phenylethylammonium (PEA)‐assisted perovskite−PVDF composite film with a polymer antireflection/barrier layer, as‐prepared composite films show dramatical improvement in photoluminescence quantum yield from 4.1% to 45.8% (11‐fold enhancement), substantially increased optical transmittance from 30.9% to 71.1% (at 700 nm), as well as strongly suppressed photoluminescence (PL) quenching during the attaching process. The fabricated attachable LSCs demonstrate a maximum optical efficiency of 2.8% at the geometric factor of 5 and retain 87% of initial optical efficiency after 2 months of storage in ambient conditions.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".