Bibliographic record
Abstract
Growth of monodisperse quantum dots (QDs) is a pressing requirement in the context of commercial application as well as academic study, as the size uniformity is directly related to color purity in display products which have been commercialized. In the growth, surface ligands serve not only as a protection layer for QD surface but also controller for reaction kinetics. I will present our recent findings pertaining to these issues on the basis of different material examples. Our study on the colloidal synthesis of InP QDs in the presence of Zn precursors will be discussed, in which size uniformity is markedly enhanced as compared to the case of InP QDs synthesized without Zn precursors. In the presence of zinc carboxylate, intermediate species containing Zn-P bonding appears. The Zn-P intermediate complex with P(SiMe3)3 exhibits lower reactivity than In-P complex. The formation of stable Zn-P intermediate complex results in lower reactivity, hence monodisperse QDs. Insights from the experimental and theoretical studies advance the mechanistic understanding and controlling of nucleation and growth of InP QDs. We investigated diffusion of active species monomers through ligand layers using CdSe nanorods (NRs) as a model system. Colloidal NRs are of special interest for optoelectronic applications because its shape anisotropy leads to unique optical and physical characteristics, expandable with morphological and structural deviation. I present a new answer for detailed growth mechanism of colloidal semiconductor NRs. For this, we developed dual-diameter nanorod (DDNR) structure via colloidal synthesis, where two sections along the long axis in each NR have different diameters at a few nanometer scale. The vivid segmentation is an ideal platform for monitoring the growth process of NRs, presenting important determinants in the reactivity of distinguishable NR facets. By controlling the discovered factors, single-diameter NRs with controllable core position also became available. I will put the findings in perspective by outlining the effect of diffusion of monomers and surface growth reactions.
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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 | 0.001 | 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".