Multiphoton Absorption Stimulated Metal Chalcogenide Quantum Dot Solar Cells under Ambient and Concentrated Irradiance
Bibliographic record
Abstract
Abstract Colloidal metal chalcogenide quantum dots (QDs) have excellent quantum efficiency in light–matter interactions and good device stability. However, QDs have been brought to the forefront as viable building blocks in bottom‐up assembling semiconductor devices, the development of QD solar cell (QDSC) is still confronting considerable challenges compared to other QD technologies due to their low performance under natural sunlight, as a consequence of untapped potential from their quantized density‐of‐state and inorganic natures. This report is designed to address this long‐standing challenge by accessing the feasibility of using QDSC for indoor and concentration PV (CPV) applications. This work finds that above bandgap photon energy irradiation of QD solids can generate high densities of excitons via multi‐photon absorption (MPA), and these excitons are not limited to diffuse by Auger recombination up to 1.5 × 1019 cm−3 densities. Based on these findings, a 19.5% (2000 lux indoor light) and an 11.6% efficiency (1.5 Suns) have been facilely realized from ordinary QDSCs (9.55% under 1 Sun). To further illustrate the potential of the MPA in QDSCs, 21.29% efficiency polymer lens CPVs (4.08 Suns) and viable sensor networks powered by indoor QDSCs matrix have been demonstrated.
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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) | 0.000 | 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) | 0.002 | 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".