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Record W2102709139 · doi:10.1002/pat.891

Properties of a novel thermal sensitive polymer based on poly(vinyl alcohol) and its layer‐by‐layer assembly

2007· article· en· W2102709139 on OpenAlexaff
Hong Lu, Anna Zheng, Huining Xiao

Bibliographic record

VenuePolymers for Advanced Technologies · 2007
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsLower critical solution temperatureVinyl alcoholCationic polymerizationMaterials scienceAqueous solutionPolymerDissolutionChemical engineeringPolymer chemistryLayer (electronics)NanotechnologyComposite materialOrganic chemistryChemistryCopolymer

Abstract

fetched live from OpenAlex

Abstract Structurally modified poly(vinyl alcohol) (PVA) was prepared as novel thermally sensitive polymers by partially acetalyzing and/or ionizing the commercially available PVA. Their aqueous solutions experience completely reversible polymer aggregation and dissolution above and below the lower critical solution temperature (LCST), respectively. The LCST of a partially acetalyzed PVA (APVA) can be readily controlled by the degree of acetalysis or the molecular weight of the starting PVA. Introduction of a small amount of cationic group onto the APVA backbone increases the LCST obviously, while the LCST is highly sensitive to NaCl concentration. Then APVA and cationic APVA multilayers are assembled on rayon to make a thermal responsive fiber. The atomic force microscopy (AFM) images of the surface reveal the increment of roughness stimulated by temperature. Copyright © 2007 John Wiley & Sons, Ltd.

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 score0.002

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 head0.027
GPT teacher head0.249
Teacher spread0.222 · 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

Citations11
Published2007
Admission routes1
Has abstractyes

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