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Record W2054146581 · doi:10.1115/smasis2010-3739

A Parametric Study and Characterization of Porous, Non-Permeable, and Conductive PEGDA-HEMA Hydrogels

2010· article· en· W2054146581 on OpenAlexafffund
Christine Chan, Shannon Chang, Hani E. Naguib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSelf-healing hydrogelsMaterials sciencePorosityCharacterization (materials science)SwellingPolymerizationChemical engineeringComposite materialElectrical conductorPhotopolymerPolymer chemistryPolymerNanotechnology

Abstract

fetched live from OpenAlex

This study involved the development and characterization of novel porous, non-permeable, and conductive hydrogels. The hydrogels were fabricated with HEMA and crosslinked with PEGDA through a complete parametric study of the synthesis parameters which included water content and crosslinking content. The hydrogels were fabricated using UV photopolymerization and in situ polymerization of PPy, and characterization was conducted with respect to their physical, thermal, mechanical, and electrical properties. The physical properties were analyzed with respect to their swelling ratio and equilibrium water content. The thermal properties were analyzed based on the decomposition temperature and residue weight. The mechanical properties examined the elastic modulus of the hydrogels, and the electrical properties investigated the conductivity of the hydrogels. The relationships observed between the processing, structure, and resulting properties provide the basis for further development and application of these porous, non-permeable, and conductive hydrogels.

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)0.001
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
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.218
Teacher spread0.209 · 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

Citations0
Published2010
Admission routes2
Has abstractyes

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