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Record W2286253888 · doi:10.1080/09205063.2015.1130407

Synthetic polycations with controlled charge density and molecular weight as building blocks for biomaterials

2016· article· en· W2286253888 on OpenAlexafffund
Rachelle M. Kleinberger, Nicholas A. D. Burke, Christal Zhou, Harald D. H. Stöver

Bibliographic record

VenueJournal of Biomaterials Science Polymer Edition · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMethacrylamideCopolymerPolyelectrolyteCationic polymerizationPolymer chemistrySelf-healing hydrogelsChemistryRaftChemical engineeringMaterials scienceOrganic chemistryPolymerAcrylamide

Abstract

fetched live from OpenAlex

A series of polycations prepared by RAFT copolymerization of N-(3-aminopropyl)methacrylamide hydrochloride (APM) and N-(2-hydroxypropyl)methacrylamide, with molecular weights of 15 and 40 kDa, and APM content of 10-75 mol%, were tested as building blocks for electrostatically assembled hydrogels such as those used for cell encapsulation. Complexation and distribution of these copolymers within anionic calcium alginate gels, as well as cytotoxicity, cell attachment, and cell proliferation on surfaces grafted with the copolymers were found to depend on composition and molecular weight. Copolymers with lower cationic charge density and lower molecular weight showed less cytotoxicity and cell adhesion, and were more mobile within alginate gels. These findings aid in designing improved polyelectrolyte complexes for use as biomaterials.

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)0.0010.000
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 head0.008
GPT teacher head0.247
Teacher spread0.239 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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Same venueJournal of Biomaterials Science Polymer EditionSame topicHydrogels: synthesis, properties, applicationsFrench-language works237,207