PbSe Quantum Dot Passivated Via Mixed Halide Perovskite Nanocrystals for Solar Cells With Over 9% Efficiency
Bibliographic record
Abstract
PbSe quantum dots (QDs) have stronger electronic coupling resulting from a large Bohr exciton radius, suggesting PbSe QDs may be able to achieve superior charge separation and transport in optoelectronic devices compared with PbS QDs. However, PbS QDs solar cell have achieved a certified 12.01% power conversion efficiency (PCE), whereas PbSe QD photovoltaics lag behind at 8.2% PCE. One reason for this difference is that there has been significantly less work done on surface passivation of PbSe QDs. Here, the surface passivation of chlorinated PbSe QDs is optimized via a halide ion exchange treatment using mixed halide CsPb(Br/I)3 perovskite nanocrystals. Champion devices made from treated QDs achieved a PCE of 9.2%, Voc of 0.56 V, Jsc of 25.7 mA cm−2, and fill factor of 64%. Average PCEs for optimized cells are 8.9%. Detailed physical characterizations including capacitance‐voltage (C‐V), Voc, and Jsc as a function of light intensity, transient photovoltage, and photocurrent measurements are all carried out to investigate the mechanism of the improvement in the PCE and to understand the role of the mixed halide perovskites in providing superior surface passivation for PbSe solar cells. At this time, 9.2% is the highest PCE yet reported for PbSe QDs solar cells.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".