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Record W3019646180 · doi:10.1039/d0cc01934h

Smart azobenzene-containing tubular polymersomes: fabrication and multiple morphological tuning

2020· article· en· W3019646180 on OpenAlexaff
Lishan Li, Songbo Cui, An Hu, Wei Zhang, Yiwen Li, Nianchen Zhou, Zhengbiao Zhang, Xiulin Zhu

Bibliographic record

VenueChemical Communications · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPhotochromic and Fluorescence Chemistry
Canadian institutionsNational Institute for NanotechnologyUniversity of Waterloo
FundersCollaborative Innovation Center of Suzhou Nano Science and TechnologyPriority Academic Program Development of Jiangsu Higher Education InstitutionsSoochow UniversityNational Natural Science Foundation of China
KeywordsPolymersomeAzobenzeneFabricationMaterials scienceNanotechnologySmart materialChemical engineeringCopolymerPolymerAmphiphileComposite materialEngineering

Abstract

fetched live from OpenAlex

A fundamental challenge in nanomaterial science is to facilely fabricate nonspherical polymersomes. Here, several kinds of novel tubular polymersomes were fabricated via self-assembly of amphiphilic azobenzene-containing block copolymers. Besides, their shape could be tuned by multiple approaches including changes in the chemical structure, self-assembly conditions and external stimuli.

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)0.001
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 score0.006
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
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 head0.052
GPT teacher head0.264
Teacher spread0.212 · 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
Published2020
Admission routes1
Has abstractyes

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