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Record W2346948984 · doi:10.1002/anie.201601341

Grafting Poly(3‐hexylthiophene) from Silicon Nanocrystal Surfaces: Synthesis and Properties of a Functional Hybrid Material with Direct Interfacial Contact

2016· article· en· W2346948984 on OpenAlexafffund
Muhammad Amirul Islam, Tapas K. Purkait, Md Hosnay Mobarok, Ignaz M. D. Hoehlein, Regina Sinelnikov, Muhammad Iqbal, Doron Azulay, I. Balberg, Oded Millo, Bernhard Rieger, Jonathan G. C. Veinot

Bibliographic record

VenueAngewandte Chemie International Edition · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesDeutsche Forschungsgemeinschaft
KeywordsMaterials scienceNanotechnologySemiconductorSiliconPolymerNanocrystalSinc functionConjugated systemHeterojunctionCovalent bondHybrid materialSurface photovoltageAcceptorElectron transferOptoelectronicsPhotochemistryChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Hybrid functional materials (HFMs) comprised of semiconductor nanoparticles and conjugated polymers offer the potential of synergetic photophysical properties. We have developed HFMs based upon silicon nanocrystals (SiNCs) and the conductive polymer poly(3-hexylthiophene) (SiNC@P3HT) by applying surface-initiated Kumada catalyst transfer polycondensation (SI-KCTP). One unique characteristic of the developed SiNC@P3HT is the formation of a direct covalent bonding between SiNCs and P3HT. The presented method for obtaining direct interfacial attachment, which is not accessible using other methods, may allow for the development of materials with efficient electronic communication at the donor-acceptor interfaces. Systematic characterization provides evidence of a core-shell structure, enhanced interfacial electron and/or energy transfer between the P3HT and SiNC components, as well as formation of a type-II heterostructure.

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 score0.003
Threshold uncertainty score0.999

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)0.0020.000

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 head0.201
Teacher spread0.188 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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