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Record W4407642222 · doi:10.1021/acsomega.4c08089

Dual Cross-Linked Chitosan-Based Films with pH-Sensitive Coloration and Drug Release Kinetics for Smart Wound Dressings

2025· article· en· W4407642222 on OpenAlexaff
Jongjit Chalitangkoon, Arnat Ronte, Tanaporn Sintoppun, Nuttaporn Manapradit, Pathavuth Monvisade

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of British Columbia
FundersThailand Science Research and Innovation
KeywordsChitosanKineticsDrugDual (grammatical number)ChemistryMaterials scienceBiomedical engineeringPharmacologyMedicineOrganic chemistryArtPhysics

Abstract

fetched live from OpenAlex

In this study, we developed dual-cross-linked hydrogel films based on carboxyethyl chitosan (CECS) and sodium alginate (SA), utilizing dialdehyde β-cyclodextrin (DA-βCD) and gluconic acid δ-lactone (GDL) as cross-linkers. Designed as smart wound dressings, the films exhibit pH sensitivity due to the incorporation of carboxyethylated phenol red-grafted chitosan (CS-PR-AA), which allows them to change color from orange to purple in response to pH variations. FT-IR and TGA analyses confirmed the formation of imine bonds and polyelectrolyte complexes, indicating successful cross-linking. The films demonstrated high cell viability, confirming their biocompatibility and nontoxicity. The swelling behavior varied with pH, underscoring their adaptability to different wound environments. Additionally, drug release kinetics were studied for films incorporating diclofenac sodium (DCF) at various pH levels, revealing that the release rate was influenced by cross-linking density and environmental pH. These findings suggest that the dual-cross-linked hydrogel films have significant potential as smart wound dressings, offering controlled drug release and pH-responsive behavior suitable for wound care applications.

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 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 score0.017
Threshold uncertainty score0.636

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 head0.010
GPT teacher head0.255
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

Citations14
Published2025
Admission routes1
Has abstractyes

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