RESUMEN
The intensity and sensitivity of surface-enhanced Raman scattering (SERS) spectra are highly dependent on the consistency and homogeneity of the nanomaterials. In this study, we developed a large-area three-dimensional (3D) hotspot substrate with good homogeneity and reproducibility in SERS signals. The substrate is based on the synergistic structures of nanoporous gold (NPG) and gold nanoparticles (AuNPs). NPG was combined with a periodic V-shaped nanocavity array to create nanoporous gold with a V-cavity (NPGVC) array featuring uniform hotspots. A nanoporous gold V-shaped resonant cavity (NPGVRC) structure was developed by incorporating AuNPs into the NPGVC array. The coupling action between the AuNPs and NPGVC resulted in a SERS-enhanced electromagnetic field with 3D hotspot distribution. The strategic incorporation of NPG and V-cavity array significantly expanded the surface area available for analyte adsorption and interaction with AuNPs. Using rhodamine 6G (R6G) and malachite green (MG) as probe molecules, the SERS performance was investigated, and the NPGVRC substrate not only showed excellent enhancement with the limit of detection as low as 10-11 M, but also presented good homogeneity. NPGVRC was then used for biological detection of the influenza A virus, where we acquired and examined the characteristic SERS spectra of two spike proteins. It is demonstrated that there is significant potential for our proposed SERS platform to be used in biosensors.
RESUMEN
The coronavirus disease 2019 (COVID-19) epidemic has given a warning that it is important to explore the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical specimens or environmental samples for public health strategies and future variants. The surface-enhanced Raman spectroscopy (SERS) technique was demonstrated to achieve this goal. However, the consistency of signals originating from the poor compatibility of virions with SERS hotspots remains a key scientific challenge for the practical applications of SERS. Herein, we develop a SERS platform for the ultrasensitive and rapid detection of SARS-CoV-2 antigen within 20 min by the combination of a highly consistent SERS substrate and a supervised deep learning algorithm. A V-shaped resonant cavity array (VRC) substrate was fabricated to trap SARS-CoV-2 virions in the periodic V cavity array and stimulate the integral SERS signal of the virus via a resonance coupling effect. Benefiting from the unique architecture of the VRC substrate, we were able to directly detect the SARS-CoV-2 virus with high sensitivity and high consistency. These excellent performances enabled us to identify five different kinds of SARS-CoV-2 variants and detect SARS-CoV-2 from clinical and environmental samples with high accuracies.
RESUMEN
A rapid, on-site, and accurate SARS-CoV-2 detection method is crucial for the prevention and control of the COVID-19 epidemic. However, such an ideal screening technology has not yet been developed for the diagnosis of SARS-CoV-2. Here, we have developed a deep learning-based surface-enhanced Raman spectroscopy technique for the sensitive, rapid, and on-site detection of the SARS-CoV-2 antigen in the throat swabs or sputum from 30 confirmed COVID-19 patients. A Raman database based on the spike protein of SARS-CoV-2 was established from experiments and theoretical calculations. The corresponding biochemical foundation for this method is also discussed. The deep learning model could predict the SARS-CoV-2 antigen with an identification accuracy of 87.7%. These results suggested that this method has great potential for the diagnosis, monitoring, and control of SARS-CoV-2 worldwide.
