ArticleBiosensors2025
Development and Optimization of a Cost-Effective Electrochemical Immunosensor for Rapid COVID-19 Diagnosis.
Article in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Optical and Electrochemical Biosensors for Detection of Pathogens Using Metal Nanoclusters: A Systematic Review.Biosensors · 2025Pooled it
- Rapid Detection of SARS-CoV‑2 Spike Protein Using a Fully 3D-Printed Electrochemical Biosensor.ACS omega · 2025Article
- Comparative Analysis of RT-PCR and a Colloidal Gold Immunochromatographic Assay for SARS-CoV-2 Detection.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The coronavirus disease (COVID-19) pandemic has created an urgent need for rapid, accurate, and cost-effective diagnostic tools. In this study, an economical electrochemical immunosensor for the rapid diagnosis of COVID-19 was developed and optimized based on charge transfer resistance (Rct) values obtained by electrochemical impedance spectroscopy (EIS) from the interaction between antibodies (anti-SARS-CoV-2) immobilized as a bioreceptor and the virus (SARS-CoV-2). The sensor uses modified pencil graphite electrodes (PGE) coated with poly(4-hydroxybenzoic acid), anti-SARS-CoV-2, and silver nanoparticles. The immobilization of anti-SARS-CoV-2 antibodies was optimized at a concentration of 1:250 for 30 min, followed by blocking the surface with 0.01% bovine serum albumin for 10 min. The optimal conditions for virus detection in clinical samples were a 1:10 dilution with a response time of 20 min. The immunosensor responded linearly in the range of 0.2-2.5 × 10
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Registered trials
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.