Biolayer-Interferometry-Guided Functionalization of Screen-Printed Graphene for Label-Free Electrochemical Virus Detection.

ACS applied materials & interfaces(2024)

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摘要
Additive manufacturing holds promise for rapid prototyping and low-cost production of biosensors for diverse pathogens. Among additive manufacturing methods, screen printing is particularly desirable for high-throughput production of sensing platforms. However, this technique needs to be combined with carefully formulated inks, rapid postprocessing, and selective functionalization to meet all requirements for high-performance biosensing applications. Here, we present screen-printed graphene electrodes that are processed with thermal annealing to achieve high surface area and electrical conductivity for sensitive biodetection via electrochemical impedance spectroscopy. As a proof-of-concept, this biosensing platform is utilized for electrochemical detection of SARS-CoV-2. To ensure reliable specificity in the presence of multiple variants, biolayer interferometry (BLI) is used as a label-free and dynamic screening method to identify optimal antibodies for concurrent affinity to the Spike S1 proteins of Delta, Omicron, and Wild Type SARS-CoV-2 variants while maintaining low affinity to competing pathogens such as Influenza H1N1. The BLI-identified antibodies are robustly bound to the graphene electrode surface via oxygen moieties that are introduced during the thermal annealing process. The resulting electrochemical immunosensors achieve superior metrics including rapid detection (55 s readout following 15 min of incubation), low limits of detection (approaching 500 ag/mL for the Omicron variant), and high selectivity toward multiple variants. Importantly, the sensors perform well on clinical saliva samples detecting as few as 103 copies/mL of SARS-CoV-2 Omicron, following CDC protocols. The combination of the screen-printed graphene sensing platform and effective antibody selection using BLI can be generalized to a wide range of point-of-care immunosensors.
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