Unveiling the Nanocluster Conversion Pathway for Highly Monodisperse InAs Colloidal Quantum Dots
Bibliographic record
Abstract
Colloidal quantum dots (CQDs) have garnered significant attention in nanoscience and technology, with a particular emphasis on achieving high monodispersity in their synthesis. Recent advances in understanding the chemistry of reaction intermediates such as magic-sized nanoclusters (MSC) have paved the way for innovative synthetic strategies. Notably, monodisperse CQDs of various compositions, including indium phosphide, indium arsenide, and cadmium chalcogenide, have been successfully prepared using nanocluster intermediates as single-source precursors. Still, the early stage conversion chemistry of these nanoclusters preceding CQD formation has not been fully unveiled yet. Herein, we report the first-order conversion of amorphous nanoclusters (AMCs) to InAs MSCs prior to the formation of CQDs. We find that MSC, isolated via gel-permeation chromatography, is more stable than purified AMCs, as demonstrated in various chemical and thermolytic reactions. While the surface of InAs AMCs and MSC is similarly bound with carboxylate ligands, detailed structural analyses employing synchrotron X-ray scattering and X-ray absorption spectroscopy unveil subtle distinctions arising from the distinct surface properties and structural disorder characteristics of InAs nanoclusters. We propose that InAs AMCs undergo a surface reduction and structural ordering process, resulting in the formation of an InAs MSC in a thermodynamically local minimum state. Furthermore, we demonstrate that both types of nanoclusters serve as viable precursors, providing a similar monomer supply rate at elevated temperatures of around 300 °C. This study offers invaluable insights into the interplay of structure and chemical stability in binary nanoclusters, enhancing our ability to design these nanoclusters as precursors for highly monodisperse CQDs.
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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) | 0.000 | 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 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".