Sequential Co‐Passivation in InAs Colloidal Quantum Dot Solids Enables Efficient Near‐Infrared Photodetectors
Bibliographic record
Abstract
Abstract III‐V colloidal quantum dots (CQDs) are promising materials for optoelectronic applications, for they avoid heavy metals while achieving absorption spanning the visible to the infrared (IR). However, the covalent nature of III‐V CQDs requires the development of new passivation strategies to fabricate conductive CQD solids for optoelectronics: this work shows herein that ligand exchanges, previously developed in II‐VI and IV‐VI quantum dots and employing a single ligand, do not fully passivate CQDs, and that this curtails device efficiency. Guided by density functional theory (DFT) simulations, this work develops a co‐passivation strategy to fabricate indium arsenide CQD photodetectors, an approach that employs the combination of X‐type methyl ammonium acetate (MaAc) and Z‐type ligands InBr 3 . This approach maintains charge carrier mobility and improves passivation, seen in a 25% decrease in Stokes shift, a fourfold reduction in the rate of first‐exciton absorption linewidth broadening over time‐under‐stress, and leads to a doubling in photoluminescence (PL) lifetime. The resulting devices show 37% external quantum efficiency (EQE) at 950 nm, the highest value reported for InAs CQD 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".