MétaCan
Menu
Back to cohort
Record W3021097583 · doi:10.1039/d0sm00269k

Evaluation of the subtle trade-off between physical stability and thermo-responsiveness in crosslinked methylcellulose hydrogels

2020· article· en· W3021097583 on OpenAlexaff
Lorenzo Bonetti, Luigi De Nardo, Fabio Variola, Silvia Farè

Bibliographic record

VenueSoft Matter · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSelf-healing hydrogelsStability (learning theory)Chemical engineeringMaterials sciencePolymer scienceChemistryPolymer chemistryNanotechnologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Methylcellulose (MC) hydrogels, undergoing sol-gel reversible transition upon temperature changes, lend themselves to smart system applications. However, their reduced stability in aqueous environment and unsatisfactory mechanical properties limit the breadth of their possible applications. Here, a crosslinking strategy based on citric acid (CA) was developed: exploiting three crosslinking parameters (CA concentration, crosslinking time, and crosslinking temperature) by a design of experiment approach, optimized crosslinked MC hydrogels (MC-L, MC-M, MC-H) were obtained and characterized. Swelling tests in water revealed the effectiveness of CA crosslinking in modulating the water uptake of MC hydrogels. Both theoretical and experimental analyses showed an increase in the crosslinking density by the rationale selection of process parameters. The extent of sol-gel transition was assessed by swelling tests, Raman spectroscopy and rheological analyses. MC-M samples demonstrated to preserve their thermo-responsive behavior around their lower critical solution temperature (LCST), while showing increased stability and enhanced mechanical properties when compared to pristine MC 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 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)0.001
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 score0.082
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.042
GPT teacher head0.286
Teacher spread0.244 · 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

Citations19
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSoft MatterSame topicHydrogels: synthesis, properties, applicationsFrench-language works237,207