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Record W4324144110 · doi:10.3390/polym15061423

A High-Linearity Glucose Sensor Based on Silver-Doped Con A Hydrogel and Laser Direct Writing

2023· article· en· W4324144110 on OpenAlexaff
Yulin Hu, Dasheng Yang, Hongbo Zhang, Yang Gao, Wenjun Zhang, Ruixue Yin

Bibliographic record

VenuePolymers · 2023
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsUniversity of Saskatchewan
FundersNational Natural Science Foundation of China
KeywordsLinearityMaterials scienceContinuous glucose monitoringSelf-healing hydrogelsBiomedical engineeringNanotechnologyOptoelectronicsElectronic engineeringMedicineInsulinInternal medicine

Abstract

fetched live from OpenAlex

A continuous glucose monitoring (CGM) system is an ideal monitoring system for the blood glucose control of diabetic patients. The development of flexible glucose sensors with good glucose-responsive ability and high linearity within a large detection range is still challenging in the field of continuous glucose detection. A silver-doped Concanavalin A (Con A)-based hydrogel sensor is proposed to address the above issues. The proposed flexible enzyme-free glucose sensor was prepared by combining Con-A-based glucose-responsive hydrogels with green-synthetic silver particles on laser direct-writing graphene electrodes. The experimental results showed that in a glucose concentration range of 0–30 mM, the proposed sensor is capable of measuring the glucose level in a repeatable and reversible manner, showing a sensitivity of 150.12 Ω/mM with high linearity of R2 = 0.97. Due to its high performance and simple manufacturing process, the proposed glucose sensor is excellent among existing enzyme-free glucose sensors. It has good potential in the development of CGM devices.

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 score0.122
Threshold uncertainty score0.768

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 head0.008
GPT teacher head0.208
Teacher spread0.199 · 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

Citations13
Published2023
Admission routes1
Has abstractyes

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