Dicarboxylic Acid‐Assisted Surface Oxide Removal and Passivation of Indium Antimonide Colloidal Quantum Dots for Short‐Wave Infrared Photodetectors
Bibliographic record
Abstract
Abstract Heavy‐metal‐free III–V colloidal quantum dots (CQDs) are promising materials for solution‐processed short‐wave infrared (SWIR) photodetectors. Recent progress in the synthesis of indium antimonide (InSb) CQDs with sizes smaller than the Bohr exciton radius enables quantum‐size effect tuning of the band gap. However, it has been challenging to achieve uniform InSb CQDs with band gaps below 0.9 eV, as well as to control the surface chemistry of these large‐diameter CQDs. This has, to date, limited the development of InSb CQD photodetectors that are sensitive to 1400 nm light. Here we adopt solvent engineering to facilitate a diffusion‐limited growth regime, leading to uniform CQDs with a band gap of 0.89 eV. We then develop a CQD surface reconstruction strategy that employs a dicarboxylic acid to selectively remove the native In/Sb oxides, and enables a carboxylate‐halide co‐passivation with the subsequent halide ligand exchange. We find that this strategy reduces trap density by half compared to controls, and enables electronic coupling among CQDs. Photodetectors made using the tailored CQDs achieve an external quantum efficiency of 25 % at 1400 nm, the highest among III–V CQD photodetectors in this spectral region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".