Parameter estimations from SARS-CoV-2 electrochemical interactions

Date

2024

Journal Title

Journal ISSN

Volume Title

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Abstract

Electrochemical pathogen sensing has gathered limelight for its effective and ultrafast detection capabilities. More recently, several electrochemical sensors were developed to counter the increasing testing requirement for the 2019 coronavirus disease (COVID-19) diagnosis. One such sensor developed was the Ultrafast COVID-19 (UFC-19) diagnostic sensor which could detect the SARS-CoV-2 spike protein in saliva samples. Although UFC-19 was established in literature to sense SARS-CoV-2 in saliva samples, the factors causing such an interaction or parameters to model such an interaction are yet to be studied in detail. In this work, an attempt has been made to electrochemically characterize the interactions at the interface by employing cyclic and linear sweep voltammetry on a rotating disk electrode setup. As a result, electrochemical parameters were calculated using chemical and electrochemical principles. The electrochemical surface area, electrode surface coverage, diffusion coefficient, reaction order with respect to SARS-CoV-2 whole virus, electron transfer coefficient have been estimated providing additional insights into the events occurring at the electrical double layer. The reaction order for the interaction was reckoned to be 0.7 confirming a non-elementary, multi-step process occurring at the interface. Theoretical and experimental calculations confirm higher hydroxide ion accumulation at the interface in the presence of SARS-CoV-2 whole virus. Results from this work lay the foundation for developing models for electrochemical SARS-CoV-2 interaction and possible extension toward other pathogenic viruses.

Description

© 2024 The Author(s) cc-by-nc-nd

Keywords

Electric double-layer, Electrochemical characterization, Electrochemical COVID-19 diagnosis, Electrochemistry of spike protein, Nickel SARS-CoV-2 interface, SARS-CoV-2 detection

Citation

Ramanujam, A., & Botte, G.G.. 2024. Parameter estimations from SARS-CoV-2 electrochemical interactions. Chemical Engineering Journal Advances, 18. https://doi.org/10.1016/j.ceja.2024.100611

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