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Record W3177956364 · doi:10.1039/d1tb01075a

Tunable, conductive, self-healing, adhesive and injectable hydrogels for bioelectronics and tissue regeneration applications

2021· article· en· W3177956364 on OpenAlexaff
Vineeta Panwar, Anand Babu, Anjana Sharma, Jijo Thomas, Vianni Chopra, Pinki Malik, Swati Rajput, Monika Mittal, Rajdeep Guha, Naibedya Chattopadhyay, Dipankar Mandal, Deepa Ghosh

Bibliographic record

VenueJournal of Materials Chemistry B · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsASTER
FundersScience and Engineering Research Board
KeywordsBioelectronicsSelf-healing hydrogelsMaterials scienceSelf-healingNanotechnologyRegeneration (biology)AdhesiveConductive polymerElectrical conductorElectrically conductiveElectronicsPolymerComposite materialBiosensorPolymer chemistryEngineeringElectrical engineeringMedicine

Abstract

fetched live from OpenAlex

) in these gels is attributed to ionic conductivity. Very few conductive hydrogels are endowed with additional properties like injectability, adhesiveness and self-healing, which would help to widen their scope for applications. While the dynamic Schiff base coupling in our hydrogels facilitated self-healing and injectable properties, polydopamine imparted tissue adhesiveness. The porosity, rheological, mechanical and conductive properties of the hydrogels are regulated by the CMC-dialdehyde-polydopamine (CMC-D-PDA) content. The hydrogel was evaluated in various bioelectronics applications like ECG monitoring and triboelectric nanogenerators (TENG). The ability of the hydrogel to support cell growth and serve as a template for tissue regeneration was confirmed using in vitro and in vivo studies. In summary, the integration of such remarkable features in the ionic-conductive hydrogel would enable its usage in bioelectronics and biomedical applications.

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 score0.032
Threshold uncertainty score0.462

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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

Citations42
Published2021
Admission routes1
Has abstractyes

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