Evidence map›Paper›PMID 41150223›Full record

ArticleIEEE transactions on biomedical circuits and systems2026

A Verilog-A-Based Redox-Signal Transduction Model for Co-Simulating Surface-Bound Electrochemical Biosensors and Circuits.

Wei Foo, Jun-Chau Chien

Abstract read
In one paragraph

Article in IEEE transactions on biomedical circuits and systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Wei Foo
Jun-Chau Chien

Funding

A CMOS-based miniaturized and disposable molecular detection system enabling sample in-situ biosensingR01EB038309 · NIBIB · UNIVERSITY OF CALIFORNIA BERKELEY · PI CHIEN, JUN-CHAU · 2025 to 2025
$2.3M
SEMI-SIMOA: A Chip-scale Flow-based Single-Molecule Assay at the Point-of-Care using Semiconductor TechnologyR21EB036154 · NIBIB · UNIVERSITY OF CALIFORNIA BERKELEY · PI Jun-Chau Chien · 2024 to 2026
$475k
NIBIB NIH HHS R01 EB038309NIBIB NIH HHS R21 EB036154
6 · The paper itself

Abstract

Surface-bound electrochemical aptamer-based (E-AB) sensors are a promising approach for continuous in-vivo and in-vitro biomolecular monitoring because they offer high selectivity, sensitivity, and real-time detection. However, accurately co-simulating E-AB sensors with readout circuits remains challenging due to the redox reporter's position-dependent electron-transfer kinetics and the electrical double layer's (EDL) complex behavior at the electrode-electrolyte interface. Here, we present a compact, SPICE-compatible electrochemical cell model that combines a Verilog-A implementation of the Marcus-Hush-based electron-transfer (ET) kinetics with a fractional-order RC-ladder representation of the EDL's non-ideal capacitance. The conventional Butler-Volmer model is replaced by Marcus-Hush kinetics, which features bounded and quantum mechanically derived ET rate constants, improving not only the model's physical interpretability but also numerical stability in circuit simulations. The model was validated with two E-AB sensors using square-wave voltammetry (SWV) across a range of excitation frequencies and target concentrations to confirm that the simulated transient currents accurately capture ET kinetics, thermodynamics, and the Langmuir isotherm's concentration response. When co-simulated with a transimpedance amplifier constructed with the TI OPA4354, the model produced electronic noise spectra that more closely matched experimental data when compared with spectra simulated using the simplified Randles circuit model. These results demonstrate that the proposed model provides a physically grounded framework for simulating surface-bound redox-based electrochemical biosensors and enables accurate co-simulation with readout circuits.

Indexed as

Biosensing TechniquesElectrochemical TechniquesSignal TransductionElectron TransportKineticsOxidation-Reduction

Identifiers

PMID41150223
PMCPMC13613573

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.