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Record W2992114713 · doi:10.1021/acsapm.9b00779

Temperature-, Light-, and Host-Molecule-Responsive Polymers with UCST Behavior for Aqueous Sensing Applications

2019· article· en· W2992114713 on OpenAlexafffund
Chuanzhuang Zhao, Jianlei Lu, X. X. Zhu

Bibliographic record

VenueACS Applied Polymer Materials · 2019
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversité de Montréal
FundersNatural Science Foundation of Zhejiang ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsAzobenzeneUpper critical solution temperaturePhotoisomerizationCopolymerMoietyPolymerAqueous solutionMaterials sciencePhotochemistryPolymer chemistryChemistryCatalysisLower critical solution temperatureOrganic chemistryIsomerization

Abstract

fetched live from OpenAlex

A light-sensitive moiety, azobenzene, is incorporated into polyacrylamide (PAM) to yield copolymers with upper critical solution temperatures (UCST) in aqueous solutions. The thermoresponsiveness of the copolymers can be tuned not only by light irradiation, which causes the transition between the trans - and cis -forms of the azobenzene, but also by addition of α-cyclodextrin (α-CD), which preferentially complexes with the trans -form of azobenzene. The UCST transition is a combined effect of hydrogen bonding between the amide groups of the PAM chains and the hydrophobic interaction between azobenzene units, the latter of which can be influenced by the photoisomerization of azobenzene and the host–guest interaction between azobenzene and α-CD. The strategy of incorporating switchable hydrophobicity may inspire designs of UCST polymers, which may have potential applications as drug delivery devices and catalyst carriers.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.004
GPT teacher head0.210
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), 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
Published2019
Admission routes2
Has abstractyes

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