Control Over Metal‐Halide Reactivity Enables Uniform Growth of InSb Colloidal Quantum Dots for Enhanced SWIR Light Detection
Bibliographic record
Abstract
Abstract InSb colloidal quantum dots (CQDs) hold promise in short‐wave infrared sensing; however, their synthesis presents ongoing challenges, particularly in achieving precise size control – this is the result of poorly controlled reactivity among the precursors. Herein, the use of alkyl‐phosphine and amine‐based organic additives to control the reactivity of In and Sb precursors during the nucleation and growth of CQDs is developed. This interplay between organic additive and precursors enables the synthesis of InSb CQDs having narrowed size distributions; and bandgaps tunable across the 1.2–1.5 µm spectral range; all this leading to peak‐to‐valley ratios >1.4 in absorption spectra. The CQDs are surface‐terminated with a mixture of oleylamine, halides, and oxide‐like species, and this hinders ligand exchange reactions and subsequent integration into photodiodes. We therefore resurface the CQDs with alkanethiols, displacing the native ligands via an acid‐base mechanism, an approach that removes oxide species. Using a layer‐by‐layer fabrication process, the ligands of the resurfaced InSb CQDs are exchanged with short organic and halide ligands and incorporated films into n‐i‐p photodiode structures. The resultant devices exhibit a detectivity of 10¹2 Jones, an external quantum efficiency (EQE) of 33% at 1380 nm, and T90 operating stability of >19 h under continuous 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".