Synthetic Control over Quantum Well Width Distribution and Carrier Migration in Low-Dimensional Perovskite Photovoltaics
Bibliographic record
Abstract
Metal halide perovskites have achieved photovoltaic efficiencies exceeding 22%, but their widespread use is hindered by their instability in the presence of water and oxygen. To bolster stability, researchers have developed low-dimensional perovskites wherein bulky organic ligands terminate the perovskite lattice, forming quantum wells (QWs) that are protected by the organic layers. In thin films, the width of these QWs exhibits a distribution that results in a spread of bandgaps in the material arising due to varying degrees of quantum confinement across the population. Means to achieve refined control over this QW width distribution, and to examine and understand its influence on photovoltaic performance, are therefore of intense interest. Here we show that moving to the ligand allylammonium enables a narrower distribution of QW widths, creating a flattened energy landscape that leads to ×1.4 and ×1.9 longer diffusion lengths for electrons and holes, respectively. We attribute this to reduced ultrafast shallow hole trapping that originates from the most strongly confined QWs. We observe an increased PCE of 14.4% for allylammonium-based perovskite QW photovoltaics, compared to 11-12% PCEs obtained for analogous devices using phenethylammonium and butylammonium ligands. We then optimize the devices using mixed-cation strategies, achieving 16.5% PCE for allylammonium devices. The devices retain 90% of their initial PCEs after >650 h when stored under ambient atmospheric 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) | 0.000 | 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 | 0.000 | 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".