Kumar Joshin, Xu Meng, Li Yuezhi August, You Shu-Wen, Doherty Brookelyn M, Gardiner Woodrow D, Cirrito John R, Yuede Carla M, Benegal Ananya, Vahey Michael D, Joshi Astha, Seehra Kuljeet, Boon Adrianus C M, Huang Yin-Yuan, Puthussery Joseph V, Chakrabarty Rajan K
Center for Aerosol Science and Engineering, Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States.
Department of Neurology, Hope Center for Neurological Disease, Knight Alzheimer's Disease Research Center, Washington University in St. Louis, St. Louis, Missouri 63110, United States.
ACS Sens. 2025 May 23;10(5):3381-3389. doi: 10.1021/acssensors.4c03087. Epub 2025 Feb 21.
Airborne transmission via aerosols is a dominant route for the transmission of respiratory pathogens, including avian H5N1 influenza A virus and bacteria. Rapid and direct detection of respiratory pathogen aerosols has been a long-standing technical challenge. Herein, we develop a novel label-free capacitive biosensor using an interlocked Prussian blue (PB)/graphene oxide (GO) network on a screen-printed carbon electrode (SPCE) for direct detection of avian H5N1 and . A single-step electro--deposition process grows GO branches on the SPCE surface, while the PB nanocrystals simultaneously decorate around the GO branches, resulting in an ultrasensitive capacitive response at nanofarad levels. We tested the biosensor for H5N1 concentrations from 2.0 viral RNA copies/mL to 1.6 × 10 viral RNA copies/mL, with a limit of detection (LoD) of 56 viral RNA copies/mL. We tested it on for concentrations ranging from 2.0 bacterial cells/mL to 1.8 × 10 bacterial cells/mL, with a LoD of 5 bacterial cells/mL. The detection times for both pathogens were under 5 min. When integrated with a custom-built wet cyclone bioaerosol sampler, our biosensor could detect and quasi-quantitatively estimate H5N1 and concentrations in air with spatial resolutions of 93 viral RNA copies/m and 8 bacterial cells/m, respectively. The quasi-quantification method, based on dilution and binary detection (positive/negative), achieved an overall accuracy of >90% for pathogen-laden aerosol samples. This biosensor is adaptable for multiplexed detection of other respiratory pathogens, making it a versatile tool for real-time airborne pathogen monitoring and risk assessment.
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