Nucleophilic Covalent Ligands Enable Simultaneous Surface Reconstruction and Passivation of Colloidal InSb Quantum Dots for Stable Short‐Wave Infrared Photodetectors
Bibliographic record
Abstract
Indium antimonide (InSb) colloidal quantum dots (CQDs) are promising candidates for short-wave infrared (SWIR) photodetectors due to their large Bohr exciton radius and tunable bandgap in the 0.6-1.3 eV range. However, the formation of metal oxides on InSb surfaces during synthesis impedes charge transport, necessitating CQD resurfacing strategies for integration into photodetectors. Previous reports achieved progress in device efficiency by resurfacing these CQDs with acid-halide sequential treatments, but the device operating stability remains unsatisfactory. Herein, we report a solution-phase strategy for surface reconstruction and passivation of InSb CQDs using sulfur-based nucleophilic covalent ligands. We find that short-chain thiol molecules remove surface metal oxides through nucleophilic attack and enable robust passivation of In and Sb via strong covalent bonds, whereas metal sulfides are less effective at oxide removal and passivation. Consequently, the thiolate-passivated CQDs exhibit a tenfold decrease in trap state density compared to controls and remain structurally and optically stable for 5 months. We demonstrate InSb CQD SWIR photodetectors that realize a high external quantum efficiency (EQE) of 28% at 1450 nm, with the highest operating stability among reported CQD SWIR photodetectors, retaining 95% of performance following 300 h of biased and illuminated operation.
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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".