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Record W4400212949 · doi:10.1021/jacs.4c04234

Functional Noncentrosymmetric Nanoparticle–Nanofiber Hybrids via Selective Fragmentation

2024· article· en· W4400212949 on OpenAlexafffund
Harvey K. MacKenzie, Yifan Zhang, Weijia Zheng, Huda Shaikh, Liam R. MacFarlane, Rebecca A. Musgrave, Ian Manners

Bibliographic record

VenueJournal of the American Chemical Society · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of Victoria
FundersNational Key Research and Development Program of ChinaSichuan University of Science and EngineeringNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research ChairsUniversity of Victoria
KeywordsChemistryFragmentation (computing)NanoparticleNanofiberNanotechnologyHybrid

Abstract

fetched live from OpenAlex

Noncentrosymmetric nanostructures are an attractive synthetic target as they can exhibit complex interparticle interactions useful for numerous applications. However, generating uniform, colloidally stable, noncentrosymmetric nanoparticles with low aspect ratios is a significant challenge using solution self-assembly approaches. Herein, we outline the synthesis of noncentrosymmetric multiblock co-nanofibers by subsequent living crystallization-driven self-assembly of block co-polymers, spatially confined attachment of nanoparticles, and localized nanofiber fragmentation. Using this strategy, we have fabricated uniform diblock and triblock noncentrosymmetric π-conjugated nanofiber–nanoparticle hybrid structures. Additionally, in contrast to Brownian motion typical of centrosymmetric nanoparticles, we demonstrated that these noncentrosymmetric nanofibers undergo ballistic motion in the presence of H 2 O 2 and thus could be employed as nanomotors in various applications, including drug delivery and environmental remediation.

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 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 score0.008
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.255
Teacher spread0.244 · 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

Citations11
Published2024
Admission routes2
Has abstractyes

Explore more

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