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Record W4284886427 · doi:10.1002/anie.202205784

A Real‐Time In Situ Demonstration of Direct and Indirect Transformation Pathways in CdTe Magic‐Size Clusters at Room Temperature

2022· article· en· W4284886427 on OpenAlexaff
Chaoran Luan, Qiu Shen, N. L. Rowell, Meng Zhang, Xiaoqin Chen, Wen Huang, Kui Yu

Bibliographic record

VenueAngewandte Chemie International Edition · 2022
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsNational Research Council Canada
FundersNational Major Science and Technology Projects of ChinaNational Natural Science Foundation of China
KeywordsRedshiftTransformation (genetics)MAGIC (telescope)Cadmium telluride photovoltaicsSemiconductorAbsorption (acoustics)Cluster (spacecraft)PhysicsMaterials scienceChemistryAstrophysicsNanotechnologyOptoelectronicsComputer scienceOptics

Abstract

fetched live from OpenAlex

The transformations of colloidal semiconductor magic-size clusters (MSCs) are expected to occur with only discrete, step-wise redshifts in optical absorption. Here, we challenge this assumption presenting a novel, conceptually different transformation, for which the redshift is continuous. In the room-temperature transformation from CdTe MSC-448 to MSC-488 (designated by the peak wavelengths in nanometer), the redshift of absorption monitored in situ displays distinctly continuous and/or step-wise behavior. Based on conclusive evidence provided by real-time experiments, the former transformation is apparently direct and intra-cluster with a relatively large energy barrier. The latter transformation is indirect and assisted by MSC precursor compounds (PCs). The former transformation follows the latter often, being predominant at a relatively high temperature. The present findings encourage a reconsideration of the absorption redshift reported previously for transformations of binary II-VI MSCs, together with the pathway associated without the increase of cluster mass.

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 score0.010
Threshold uncertainty score0.780

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)0.0010.000

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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

Citations24
Published2022
Admission routes1
Has abstractyes

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