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Record W4312635122 · doi:10.1109/ojsscs.2022.3221924

A Review of Electrochemical Electrodes and Readout Interface Designs for Biosensors

2022· review· en· W4312635122 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Open Journal of the Solid-State Circuits Society · 2022
Typereview
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of Toronto
FundersBeijing Innovation Center for Future ChipNational Natural Science Foundation of China
KeywordsElectronic circuitElectrodeBiosensorTransistorMaterials scienceNanotechnologyElectrochemistryComputer scienceOptoelectronicsElectronic engineeringElectrical engineeringVoltageChemistryEngineering

Abstract

fetched live from OpenAlex

Electrochemical detection is widely used in biosensing fields, such as medical diagnosis and health monitoring due to its real-time response and high accuracy. Both passive and active electrodes and the corresponding readout circuits have been continuously improved over the past decades. This article summarizes the redox reaction method, state-of-the-art electrode materials, and readout circuits based on the passive three-electrode. The redox-current-based readout circuits are widely used and developed toward multichannel high precision and low power consumption. In terms of active electrodes, this article reviews the development of field-effect transistors (FETs)-based electrochemical detection and readout circuits. In the past decade, the development of organic electrochemical transistors (OECTs) has also enabled more precise electrochemical detection.

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.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.800
Threshold uncertainty score0.895

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.138
GPT teacher head0.408
Teacher spread0.270 · 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