Mixed Lead Halide Passivation of Quantum Dots
Bibliographic record
Abstract
Abstract Infrared‐absorbing colloidal quantum dots (IR CQDs) are materials of interest in tandem solar cells to augment perovskite and cSi photovoltaics (PV). Today's best IR CQD solar cells rely on the use of passivation strategies based on lead iodide; however, these fail to passivate the entire surface of IR CQDs. Lead chloride passivated CQDs show improved passivation, but worse charge transport. Lead bromide passivated CQDs have higher charge mobilities, but worse passivation. Here a mixed lead‐halide (MPbX) ligand exchange is introduced that enables thorough surface passivation without compromising transport. MPbX–PbS CQDs exhibit properties that exceed the best features of single lead‐halide PbS CQDs: they show improved passivation (43 ± 5 meV vs 44 ± 4 meV in Stokes shift) together with higher charge transport (4 × 10 ‐2 ± 3 × 10 ‐3 cm 2 V ‐1 s ‐1 vs 3 × 10 ‐2 ± 3 × 10 ‐3 cm 2 V ‐1 s ‐1 in mobility). This translates into PV devices having a record IR open‐circuit voltage (IR V oc ) of 0.46 ± 0.01 V while simultaneously having an external quantum efficiency of 81 ± 1%. They provide a 1.7× improvement in the power conversion efficiency of IR photons (>1.1 µm) relative to the single lead‐halide controls reported herein.
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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".