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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 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.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.094
Threshold uncertainty score1.000

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 head0.015
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