MétaCan
Menu
Back to cohort
Record W4367054853 · doi:10.1002/anie.202304056

Direct Arylation of Silicon Nanocrystals with Hexadehydro‐Diels–Alder‐Derived Benzynes

2023· article· en· W4367054853 on OpenAlexaff
Ming Lai, Ying Zhang, Liang Zhao, Yuhua Huang, Lei Zhang, Wenlong Fu, Pu Chen, Xudong Wang, Tingshun Zhu, Zhenyu Yang

Bibliographic record

VenueAngewandte Chemie International Edition · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Waterloo
FundersScience and Technology Planning Project of Guangdong ProvinceNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of China
KeywordsAryneArylSiliconReagentCombinatorial chemistryMaterials scienceNanotechnologyChemistryPhotochemistryOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Colloidal silicon nanocrystals (SiNCs) have garnered significant interest in optoelectronics and biomedical applications. Direct arylation provides pathways to enhance the solution processability of particles and manipulate the photophysical and electronic properties of SiNCs. Unfortunately, existing methods employed to prepare aryl-functionalized SiNCs are based on organometallic coupling or transition-metal-catalyzed strategies, which require metal-based reagents for preactivation or the precursors and complicated post-treatment processes for product purification. Herein, we demonstrate a metal-free method that directly functionalizes SiNCs with aryl-based ligands. We design a series of benzyne derivatives formed from the thermal cyclization of predesigned alkynes, allowing efficient arylation on hydride-terminated silicon surfaces under mild conditions. These aryl-functionalized SiNCs exhibit strong blue emissions with nanosecond-scaled decay, suggesting the formation of a new radiative recombination channel on SiNC surfaces.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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.

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

Citations7
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueAngewandte Chemie International EditionSame topicSilicon Nanostructures and PhotoluminescenceFrench-language works237,207