Resurfacing of InAs Colloidal Quantum Dots Equalizes Photodetector Performance across Synthetic Routes
Bibliographic record
Abstract
The synthesis of highly monodispersed InAs colloidal quantum dots (CQDs) is needed in InAs CQD-based optoelectronic devices. Because of the complexities of working with arsenic precursors such as tris-trimethylsilyl arsine ((TMSi) 3 As) and tris-trimethylgermyl arsine ((TMGe) 3 As), several attempts have been made to identify new candidates for synthesis; yet, to date, only the aforementioned two highly reactive precursors have led to excellent photodetector device performance. We begin the present study by investigating the mechanism, finding that the use of the cosurfactant dioctylamine plays a crucial role in producing monodispersed InAs populations. Through quantitative analysis of ligands on the surface of InAs CQDs, we find that (TMGe) 3 As leads to In-rich characteristics, and we document the presence of an amorphous In-oleate shell on the surface. This we find causes surface defects, and thus, we develop materials processing strategies to remove the surface shell with a view to achieving efficient charge transfer in CQD solids. As a result, we develop resurfacing protocols, tailored to each dot synthesis, that produce balanced In-to-As stoichiometry regardless of synthetic input, enabling us to fabricate NIR photodetectors that achieve best-in-class EQEs at 940 nm excitons (25–28%, biased), independent of the synthetic pathway.
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 | 0.001 | 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".