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Record W4220987956 · doi:10.3390/polym14061207

Employing Cellulose Nanofiber-Based Hydrogels for Burn Dressing

2022· article· en· W4220987956 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePolymers · 2022
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooUniversity of Tehran
KeywordsSelf-healing hydrogelsNanofiberCelluloseCitric acidFourier transform infrared spectroscopyHydroxyethyl celluloseNuclear chemistryAbsorption of waterSwellingMaterials scienceWound dressingPolymer chemistryViability assayChemical engineeringChemistryOrganic chemistryComposite materialCellBiochemistry

Abstract

fetched live from OpenAlex

The aim of this research was to fabricate a burn dressing in the form of hydrogel films constructed with cellulose nanofibers (CNF) that has pain-relieving properties, in addition to wound healing. In this study, the hydrogels were prepared in the form of film. For this, CNF at weight ratios of 1, 2, and 3 wt.%, 1 wt.% of hydroxyethyl cellulose (HEC), and citric acid (CA) crosslinker with 10 and 20 wt.% were used. FE-SEM analysis showed that the structure of the CNF was preserved after hydrogel preparation. Cationization of CNF by C6H14NOCl was confirmed by FTIR spectroscopy. The drug release analysis results showed a linear relationship between the amount of absorption and the concentration of the drug. The MTT test (assay protocol for cell viability and proliferation) showed the high effectiveness of cationization of CNF and confirmed the non-toxicity of the resulting 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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.142
Threshold uncertainty score0.999

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.267
Teacher spread0.251 · 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