ZnO Nanoflower-Based Electrochemical SARS-CoV-2 Molecular Biosensors with Improved Diagnostic Accuracy

Abstract

Despite the promising performance of nanomaterial-based electrochemical detection techniques, product-level pointof-care sensor systems are still evolving. Unique approaches toward nanoscale tunability, enriched measurements, and data analysis are therefore imperative. The recent outbreak of coronavirus disease provides a suitable test bench toward this pursuit, attracting several innovative efforts from the sensor research community. Accordingly, this work reports an ultrasensitive and selective molecular biosensor for RT-PCR-confirmed hospital samples of SARS-CoV-2 built on ZnO nanoflowers. The unique experimental and analytical methodologies drastically improve the device efficiency, relying on three conserved genomic sequences (E, N, and ORF1ab genes). It involves multiple techniques like cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse voltammetry where normalized responses of these techniques are used to create 1D, 2D, and 3D data points representing each sample for clustering both by heuristic (2D separation plane) and ML approach (logistic regression) for accurate diagnosis. Enhancing data features also enlarges the Euclidean distances (ED) of data points, which raises the separation efficiency of the device to 93.75%. As the 3D space also allows the data (with nearly identical EDs) to have different spatial orientations for efficient clustering, samples were dichotomized into positive and negative with 100% efficiency, sensitivity, and specificity. Furthermore, owing to the ultrasensitive nanostructured transducer, the viral load was successfully quantified in the spiked control samples with a commendable detection sensitivity and limits of detection as low as 14.137 fM (E-gene). The device's fast response time (<45 min) justifies its potential on-field applicability.