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Record W3092465320 · doi:10.1039/d0an01672a

An enzyme-free capacitive glucose sensor based on dual-network glucose-responsive hydrogel and coplanar electrode

2020· article· en· W3092465320 on OpenAlexaff
Yingjie Cai, Dasheng Yang, Ruixue Yin, Yang Gao, Hongbo Zhang, Wenjun Zhang

Bibliographic record

VenueThe Analyst · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsSaskatoon Medical ImagingUniversity of Saskatchewan
FundersShanghai Key Laboratory of Orthopaedic ImplantsNational Natural Science Foundation of China
KeywordsCapacitive sensingElectrodeCuring (chemistry)Materials scienceOptoelectronicsGlucose oxidaseNanotechnologyBiosensorChemistryPolymer chemistryElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Glucose sensors are vital devices for blood glucose detection in the diabetes care. Different from traditional electrochemical devices based on glucose oxidase, the glucose sensor based on the glucose-responsive hydrogel is more robust owing to its enzyme-free principle. However, integrating the high sensitivity, fast response, wide measuring range and low-cost fabrication into a hydrogel sensor is still challenging. In this study, we present a physical capacitive sensor, which consists of interdigital carbon electrodes (ICEs) fabricated by a direct laser writing technology and glucose-responsive hydrogel (DexG-Con A hydrogel) built by UV curing in situ. The dielectric property of DexG-Con A hydrogel changes accordingly with the change in environmental glucose concentration. Experimental results demonstrate that in a glucose concentration range of 0-30 mM, the proposed hydrogel sensor is capable of measuring the glucose level in a repeatable and reversible manner, showing a short responsive time of less than 2 min and a high sensitivity of 8.81 pF mM-1 at a glucose range of 0-6 mM. Owing to its simple fabrication process, low-cost and high performance, the proposed glucose sensor shows great potential on batch production for continuous glucose monitoring application.

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 score0.279
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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

Citations23
Published2020
Admission routes1
Has abstractyes

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