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• W4310376520 startingPage "54550" @default.
• W4310376520 abstract "Human respiratory aerosols contain diverse potential biomarkers for early disease diagnosis. Here, we report the direct and label-free detection of SARS-CoV-2 in respiratory aerosols using a highly adsorptive Au-TiO2 nanocomposite SERS face mask and an ablation-assisted autoencoder. The Au-TiO2 SERS face mask continuously preconcentrates and efficiently captures the oronasal aerosols, which substantially enhances the SERS signal intensities by 47% compared to simple Au nanoislands. The ultrasensitive Au-TiO2 nanocomposites also demonstrate the successful detection of SARS-CoV-2 spike proteins in artificial respiratory aerosols at a 100 pM concentration level. The deep learning-based autoencoder, followed by the partial ablation of nondiscriminant SERS features of spike proteins, allows a quantitative assay of the 101-104 pfu/mL SARS-CoV-2 lysates (comparable to 19-29 PCR cyclic threshold from COVID-19 patients) in aerosols with an accuracy of over 98%. The Au-TiO2 SERS face mask provides a platform for breath biopsy for the detection of various biomarkers in respiratory aerosols." @default.
• W4310376520 created "2022-12-10" @default.
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• W4310376520 date "2022-11-30" @default.
• W4310376520 modified "2023-10-15" @default.
• W4310376520 title "Highly Adsorptive Au-TiO₂ Nanocomposites for the SERS Face Mask Allow the Machine-Learning-Based Quantitative Assay of SARS-CoV-2 in Artificial Breath Aerosols" @default.
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• W4310376520 doi "https://doi.org/10.1021/acsami.2c16446" @default.
• W4310376520 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36448483" @default.
• W4310376520 hasPublicationYear "2022" @default.
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