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Record W4210256774 · doi:10.1002/adma.202107940

A Two‐Pathway Model for the Evolution of Colloidal Compound Semiconductor Quantum Dots and Magic‐Size Clusters

2022· article· en· W4210256774 on OpenAlexaff
Yang Li, N. L. Rowell, Chaoran Luan, Meng Zhang, Xiaoqin Chen, Kui Yu

Bibliographic record

VenueAdvanced Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsNational Research Council Canada
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsQuantum dotNanotechnologyMaterials scienceNucleationNanomaterialsTernary operationNanocrystalSemiconductorMAGIC (telescope)Computer scienceOptoelectronicsChemistryPhysics

Abstract

fetched live from OpenAlex

A fundamental understanding of formation pathways is critical to the controlled synthesis of colloidal semiconductor nanocrystals. As ultrasmall-size quantum dots (QDs) sometimes emerge in reactions along with magic-size clusters (MSCs), distinguishing their individual pathway of evolution is important, but has proven difficult. To decouple the evolution of QDs and MSCs, an unconventional, selective approach has been developed, along with a two-pathway model that provides a fundamental understanding of production selectivity. For on-demand production of either ultrasmall QDs or MSCs, the key enabler is in how to allow a reaction to proceed in the time prior to nucleation and growth of QDs. In this prenucleation stage, an intermediate compound forms, which is the precursor compound (PC) to the MSC. Here, the two-pathway model and the manipulation of such PCs to synthesize either ultrasmall QDs or binary and ternary MSCs are highlighted. The two-pathway model will assist the development of nucleation theory as well as provide a basis for a mechanism-enabled design and predictive synthesis of functional nanomaterials.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.253
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations52
Published2022
Admission routes1
Has abstractyes

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