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Record W2329847229 · doi:10.1021/acs.macromol.5b00436

Swelling and Thermoresponsive Behavior of Linear versus Cyclic Poly(<i>N</i>-isopropylacrylamide) Thin Films

2015· article· en· W2329847229 on OpenAlexaff
David Magerl, Martine Philipp, Xing‐Ping Qiu, Françoise M. Winnik, Peter Müller‐Buschbaum

Bibliographic record

VenueMacromolecules · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversité de Montréal
FundersCenter for NanoScience, Ludwig-Maximilians-Universität MünchenDeutsche Forschungsgemeinschaft
KeywordsSwellingLower critical solution temperaturePoly(N-isopropylacrylamide)Materials sciencePolymerThin filmKineticsPolymer chemistrySubstrate (aquarium)Phase (matter)Chemical engineeringPhase transitionNanometreComposite materialNanotechnologyChemistryCopolymerThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Polymer topology and reduced dimensions can have a significant impact on the properties and phase transitions of polymeric films with thicknesses below a few hundred nanometers. We study the impact of these effects in the case of thin films of cyclic and linear poly( N -isopropylacrylamide) (PNIPAM) exposed to water vapor. Specifically, we monitor the swelling kinetics of the thin PNIPAM films, their interfacial interactions, and their LCST-type demixing phase transition, using white light interferometry and X-ray reflectivity. As the film thickness decreases, the swelling ratio increases, presumably due to the increasingly dominant effect of polymer/substrate interactions. The time constants of the swelling process depend on both the film thickness and the PNIPAM topology. Consistent with earlier observations for PNIPAM solutions, in thin swollen films of comparable concentration, cyclic PNIPAM exhibits a broader and thus less cooperative demixing transition than the linear PNIPAM counterpart.

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 score0.004
Threshold uncertainty score0.776

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)0.0000.000

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 head0.027
GPT teacher head0.269
Teacher spread0.242 · 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

Citations42
Published2015
Admission routes1
Has abstractyes

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