Plasmonic nanostructure-enhanced Raman scattering for detection of SARS-CoV-2 nucleocapsid protein and spike protein variants.

Epidemiological control and public health monitoring during the outbreaks of infectious viral diseases rely on the ability to detect viral pathogens. Here we demonstrate a rapid, sensitive, and selective nanotechnology-enhanced severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection based on the surface-enhanced Raman scattering (SERS) responses from the plasma-engineered, variant-specific antibody-functionalized silver microplasma-engineered nanoassemblies (AgMEN) interacting with the SARS-CoV-2 spike (S) and nucleocapsid (N) proteins. The three-dimensional (3D) porous AgMEN with plasmonic-active nanostructures provide a high sensitivity to virus detection via the remarkable SERS signal collection. Moreover, the variant-specific antibody-functionalization on the SERS-active AgMEN enabled the high selectivity of the SARS-CoV-2 S variants, including wild-type, Alpha, Delta, and Omicron, under the simulated human saliva conditions. The exceptional ultrahigh sensitivity of our SERS biosensor was demonstrated via SARS-CoV-2 S and N proteins at the detection limit of 1 fg mL-1 and 0.1 pg mL-1, respectively. Our work demonstrates a versatile SERS-based detection platform can be applied for the ultrasensitive detection of virus variants, infectious diseases, and cancer biomarkers.

Distributed Strain Monitoring Using Nanocomposite Paint Sensing Meshes.

Strain measurements are vital for monitoring the load-bearing capacity and safety of structures. A common approach is to affix strain gages onto structural surfaces. On the other hand, most aerospace, automotive, civil, and mechanical structures are painted and coated, often with many layers, prior to their deployment. There is an opportunity to design smart and multifunctional paints that can be directly pre-applied onto structural surfaces to serve as a sensing layer among their other layers of functional paints. Therefore, the objective of this study was to design a strain-sensitive paint that can be used for structural monitoring. Carbon nanotubes (CNT) were dispersed in paint by high-speed shear mixing, while paint thinner was employed for adjusting the formulation's viscosity and nanomaterial concentration. The study started with the design and fabrication of the CNT-based paint. Then, the nanocomposite paint's electromechanical properties and its sensitivity to applied strains were characterized. Third, the nanocomposite paint was spray-coated onto patterned substrates to form "Sensing Meshes" for distributed strain monitoring. An electrical resistance tomography (ERT) measurement strategy and algorithm were utilized for reconstructing the conductivity distribution of the Sensing Meshes, where the magnitude of conductivity (or resistivity) corresponded to the magnitude of strain, while strain directionality was determined based on the strut direction in the mesh.