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Record W4386367641 · doi:10.3390/gels9090709

Highly Stretchable, Self-Healing, Injectable and pH Responsive Hydrogel from Multiple Hydrogen Bonding and Boron-Carbohydrate Interactions

2023· article· en· W4386367641 on OpenAlexaff
Yi‐Yang Peng, Qiuli Cheng, Meng Wu, Wenda Wang, Jianyang Zhao, Diana Diaz‐Dussan, Michelle McKay, Hongbo Zeng, Sarute Ummartyotin, Ravin Narain

Bibliographic record

VenueGels · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of Alberta
FundersNational Research Council of Thailand
KeywordsSelf-healing hydrogelsBoraxScaffoldHydrogen bondSugarTissue engineeringMaterials scienceChemical engineeringPolymerViability assayBoronBiomedical engineeringNanotechnologyChemistryPolymer chemistryComposite materialMoleculeOrganic chemistryCellBiochemistry

Abstract

fetched live from OpenAlex

A simple and cost-effective method for the fabrication of a safe, dual-responsive, highly stretchable, self-healing and injectable hydrogel is reported based on a combination of dynamic boronate ester bonds and hydrogen bonding interactions. The mechanical properties of the hydrogel are tunable by adjusting the molar ratios between sugar moieties on the polymer and borax. It was remarkable to note that the 2:1 ratio of sugar and borate ion significantly improves the mechanical strength of the hydrogel. The injectability, self-healing and stretchability properties of the hydrogel were also examined. In addition, the impact of the variation of the pH and the addition of free sugar responsiveness of the hydrogel was studied. High MRC-5 cell viability was noticed by the 3D live/dead assay after 24 h cell culture within the hydrogel scaffold. Hence, the developed hydrogels have desirable features that warrant their applications for drug delivery, scaffolds for cell and tissue engineering.

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)0.000
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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