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Record W4415020021 · doi:10.1016/j.matdes.2025.114899

Multifunctional mussel-inspired gelatin methacryloyl hydrogels for infection-sensitive applications

2025· article· en· W4415020021 on OpenAlexafffund
Sorosh Abdollahi, Bahareh Zarin, Maryam Vatani, Mohsen Hassani, Fereshteh Vajhadin, Kartikeya Dixit, Keekyoung Kim, Amir Sanati‐Nezhad

Bibliographic record

VenueMaterials & Design · 2025
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsSelf-healing hydrogelsGelatinBromothymol blueAntimicrobialBiocompatible materialNanocompositeSurface modificationBacterial growth

Abstract

fetched live from OpenAlex

Bacterial infections remain a major health concern, with early and accessible detection critical for effective intervention. Here, we introduce a multifunctional and biocompatible gelatin methacryloyl hydrogel designed for real-time visual monitoring and localized antimicrobial action. The system incorporates bromothymol blue (BTB) encapsulated within pH-responsive smart pigments, enabling a controlled colorimetric shift from yellow to blue in response to basic microenvironments generated by Gram-negative bacteria. In vitro studies using Escherichia coli validate the platform’s utility for rapid infection detection. Catechol functionalization enhances the hydrogel’s mechanical strength and mussel-inspired adhesion, supporting appropriate contact with infection-prone surfaces. As a proof of concept for therapeutic integration, zinc oxide (ZnO) nanoparticles were embedded to demonstrate the hydrogel’s capacity for antimicrobial agent loading and release. This integrated, easy-to-use material provides a promising strategy for simultaneous detection and treatment of bacterial infections, especially in resource-limited or point-of-care settings.

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: Methods · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.660

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 head0.027
GPT teacher head0.294
Teacher spread0.267 · 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
GenreMethods

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
Published2025
Admission routes2
Has abstractyes

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