Quantitative Analysis of Virus Adsorption and Co-adsorption Behavior Using BET Modeling and SERS Spectroscopy

Area of Science:

• Nanotechnology
• Biophysics
• Analytical Chemistry

Background:

• Virus-surface interactions are crucial for biosensor development and antiviral strategies.
• Accurate detection of respiratory viruses requires understanding their adsorption behavior on sensor surfaces.
• Existing models often overlook the complexities of virus mixtures and physiological environments.

Purpose of the Study:

• To systematically investigate the adsorption and coadsorption of 12 respiratory viruses on SiO2-coated silver nanorod arrays.
• To model virus adsorption using a modified Brunauer-Emmett-Teller (BET) framework and surface-enhanced Raman scattering (SERS).
• To analyze virus behavior in both water and human saliva, considering single viruses (SVs) and binary virus mixtures (2VMs).

Main Methods:

• Utilized SERS on SiO2-coated silver nanorod arrays for virus analysis.
• Applied a modified BET adsorption framework to model spectral data.
• Employed linear least-squares spectral decomposition to extract adsorption coefficients.

Main Results:

• All 12 respiratory viruses exhibited multilayer physisorption (Type II isotherms).
• Binary virus mixtures (2VMs) showed significantly enhanced adsorption (4-25x greater BET constant q than SVs).
• Human saliva modulated virus-surface interactions in virus-specific ways, increasing complexity.

Conclusions:

• Single-virus calibration is insufficient for accurate detection in mixed-virus samples.
• Mixture-aware analytical models are essential for advancing biosensing applications.
• This study provides a framework for quantitative modeling of virus adsorption in diagnostic and monitoring contexts.