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Record W4378717771 · doi:10.1002/adfm.202300866

Development of an Asymmetric Hydrophobic/Hydrophilic Ultrathin Graphene Oxide Membrane as Actuator and Conformable Patch for Heart Repair

2023· article· en· W4378717771 on OpenAlexaff
Xingying Zhang, Chen Song, Huijia Nong, Kaige Xu, Xiaozhuo Wu, Wen Zhong, Malcolm Xing, Leyu Wang

Bibliographic record

VenueAdvanced Functional Materials · 2023
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsConformable matrixMaterials scienceGrapheneMicroscale chemistryNanotechnologyParyleneCoatingOxideMembranePolymerComposite materialChemistry

Abstract

fetched live from OpenAlex

Abstract A conductive engineered cardiac patch (ECP) can reconstruct the biomimetic regenerative microenvironment of an infarcted myocardium. Direct ink writing (DIW) and 3D printing can produce an ECP with precisely controlled microarchitectures. However, developing a printed ECP with high conductivity and flexibility for gapless attachment to conform to epicardial geometry remains a challenge. Herein, an asymmetrical DIW hydrophobic/hydrophilic membrane using heat‐processed graphene oxide (GO) ink is developed. The “Masked spin coating” method is also developed that leads to a microscale GO (hydrophilic)/reduced GO (rGO, hydrophobic) physiological sensor, as well as a macroscale moisture‐driven GO/rGO actuator. Depositing mussel‐inspired polydopamine (PDA) coating on the one side of the DIW rGO , the ultrathin (approximately 500 nm) PDA‐rGO (hydrophilic)/rGO (hydrophobic) microlattice (DrGOM) ECP is bestowed with the flexibility and moisture‐responsive actuation that allows gapless attachment to the curved surface of the epicardium. Conformable DrGOM exhibits a promising therapeutic effect on rats' infarcted hearts through conductive microenvironment reconstruction and improved neovascularization.

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 score0.025
Threshold uncertainty score0.620

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 head0.018
GPT teacher head0.273
Teacher spread0.255 · 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

Citations20
Published2023
Admission routes1
Has abstractyes

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