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Record W2053837780 · doi:10.1002/mawe.200400834

Hydrogels of Thermoreversible Comb‐Polymers Exhibit Increased Resistance for Dissolution

2004· article· en· W2053837780 on OpenAlexaff
Aamir Jalil, Hasan Uludağ

Bibliographic record

VenueMaterialwissenschaft und Werkstofftechnik · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLower critical solution temperaturePolymerPolymer chemistrySelf-healing hydrogelsMethyl methacrylatePolymer architectureMaterials scienceDissolutionSide chainChemical engineeringMethacrylateAqueous solutionCopolymerChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The N‐isopropylacrylamide (NiPAM) polymers exhibit thermoreversible properties in aqueous solutions. The resiliency of NiPAM hydrogels is believed to influence the outcome when the polymers are utilized in biomedical applications. To determine the influence of polymer architecture on hydrogel resiliency, polymers of NiPAM, methyl methacrylate (MMA) and acrylic acid were synthesized as random polymers and as comb polymers, where MMA was incorporated as side‐chains. Random polymers exhibited a Lower Critical Solution Temperature (LCST) that decreased in proportion to MMA content in polymers. The LCST of comb polymers was not dependent on MMA content or the length of MMA side‐chain (between 3.0 and 10.1 kD). Whereas low molecular weight (∼100 kD) random polymers did not form intact gels, comb‐polymers of equivalent molecular weight were capable of forming intact gels. Gel resiliency, as determined by propensity of hydrogels to dissolve upon cooling, was improved when the molecular weight of comb‐polymers was increased but the length of the MMA side‐branch did not influence the gel resiliency. We conclude that hydrogel dissolution was dependent on polymer architecture as well as the polymer molecular weight.

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 categoriesMeta-epidemiology (narrow)
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 score0.039
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.242
Teacher spread0.231 · 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.

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

Citations3
Published2004
Admission routes1
Has abstractyes

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