Halide‐Driven Synthetic Control of InSb Colloidal Quantum Dots Enables Short‐Wave Infrared Photodetectors
Bibliographic record
Abstract
In the III-V family of colloidal quantum dot (CQD) semiconductors, InSb promises access to a wider range of infrared wavelengths compared to many light-sensing material candidates. However, achieving the necessary size, size-dispersity, and optical properties has been challenging. Here the synthetic challenges associated with InSb CQDs are investigated and it is found that uncontrolled reduction of the antimony precursor hampers the controlled growth of CQDs. To overcome this, a synthetic strategy that combines nonpyrophoric precursors with zinc halide additives is developed. The experimental and computational studies show that zinc halide additives decelerate the reduction of the antimony precursor, facilitating the growth of more uniformly sized CQDs. It is also found that the halide choice provides additional control over the strength of this effect. The resultant CQDs exhibit well-defined excitonic transitions in spectral range of 1.26-0.98 eV, along with strong photoluminescence. By implementing a postsynthesis ligand exchange, colloidally stable inks enabling the fabrication of high-quality CQD films are achieved. The first demonstration of InSb CQD photodetectors is presented reaching 75% external quantum efficiency (QE) at 1200 nm, to the knowledge the highest short-wave infrared (SWIR) QE reported among heavy-metal-free infrared CQD-based devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".