Efficient quantum dot infrared solar cells with enhanced low-energy photon conversion via optical engineering
Bibliographic record
Abstract
Infrared (IR) solar cells are promising devices for improving the power conversion efficiency (PCE) of conventional solar cells by expanding the utilization region of the sunlight spectrum to near-infrared range. IR solar cells based on colloidal quantum dots (QDs) have attracted extensive attention due to the widely tunable absorption spectrum controlled by dot size and the unique solution processibility. However, the trade-off in QD solar cells between light absorption and photo-generated carrier collection has limited the further improvement of PCE. Here, we present high-performance PbS QD IR solar cells resulting from the combination of boosted light absorption and optimized carrier extraction. By constructing an optical resonance cavity, the light absorption is significantly enhanced in the range of 1,150–1,300 nm at a relatively thin photoactive layer. Meanwhile, the thin photoactive layer facilitates efficient carrier extraction. Consequently, the PbS QD IR solar cells exhibit a highly efficient photoelectric conversion in the IR region, resulting in a high IR PCE of 1.3% which is comparable to the highest value of solution-processed IR solar cells based on PbSe QDs. These results demonstrate that constructing an optical resonance cavity is a reasonable strategy for effective conversion of photons in the devices aiming at light in a relatively narrow wavelength range, such as IR solar cells and narrow band photodetectors.
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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) | 0.001 | 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".