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Record W4293298399 · doi:10.1126/science.abn8699

Controlled tough bioadhesion mediated by ultrasound

2022· article· en· W4293298399 on OpenAlex
Zhenwei Ma, Claire Bourquard, Qiman Gao, Shuaibing Jiang, Tristan De Iure‐Grimmel, Ran Huo, Xuan Li, Zixin He, Zhen Yang, Galen Yang, Yixiang Wang, Edmond Lam, Zu‐Hua Gao, Outi Supponen, Jianyu Li

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

VenueScience · 2022
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsNational Research Council CanadaMcGill University Health CentreUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsBioadhesiveTransdermalNanotechnologyControllabilitySelf-healing hydrogelsAdhesionDrug deliveryMaterials scienceBiomedical engineeringComposite materialEngineeringPolymer chemistry

Abstract

fetched live from OpenAlex

Tough bioadhesion has important implications in engineering and medicine but remains challenging to form and control. We report an ultrasound (US)-mediated strategy to achieve tough bioadhesion with controllability and fatigue resistance. Without chemical reaction, the US can amplify the adhesion energy and interfacial fatigue threshold between hydrogels and porcine skin by up to 100 and 10 times. Combined experiments and theoretical modeling suggest that the key mechanism is US-induced cavitation, which propels and immobilizes anchoring primers into tissues with mitigated barrier effects. Our strategy achieves spatial patterning of tough bioadhesion, on-demand detachment, and transdermal drug delivery. This work expands the material repertoire for tough bioadhesion and enables bioadhesive technologies with high-level controllability.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.254
Teacher spread0.245 · 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