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Record W4368282472 · doi:10.1039/d3mh00056g

Conductive polymer based hydrogels and their application in wearable sensors: a review

2023· review· en· W4368282472 on OpenAlexafffund
Dong Liu, Chenxi Huyan, Zibi Wang, Zhanhu Guo, Xuehua Zhang, Hamdi Torun, Daniel M. Mulvihill, Ben Bin Xu, Chen Fei

Bibliographic record

VenueMaterials Horizons · 2023
Typereview
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Alberta
FundersEngineering and Physical Sciences Research CouncilChina Postdoctoral Science FoundationXi’an Jiaotong UniversityNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsSelf-healing hydrogelsWearable computerMaterials scienceElectrical conductorNanotechnologyWearable technologyConductive polymerPolymerElectrically conductiveComposite materialPolymer chemistryComputer scienceEmbedded system

Abstract

fetched live from OpenAlex

m). However, considerable challenges remain to be overcome, such as the limited strain sensing range due to the mechanical strength, the signal loss/instability caused by swelling/deswelling, the significant hysteresis of sensing signals, the de-hydration induced malfunctions, and the surface/interfacial failure during manufacturing/processing. This review aims to offer a targeted scan of recent advancements in CPH based wearable sensor technology, from the establishment of dedicated structure-property relationships in the lab to the advanced manufacturing routes for potential scale-up production. The application of CPHs in wearable sensors is also explored, with suggested new research avenues and prospects for CPHs in the future also included.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.038
GPT teacher head0.285
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations252
Published2023
Admission routes2
Has abstractyes

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