PbSe Quantum Dot Passivated Via Mixed Halide Perovskite Nanocrystals for Solar Cells With Over 9% Efficiency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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%, V oc of 0.56 V, J sc 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 ), V oc , and J sc 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it