Dual-Modal Colorimetric and Surface-Enhanced Raman Scattering (SERS)-Based Lateral Flow Immunoassay for Ultrasensitive Detection of SARS-CoV-2 Using a Plasmonic Gold Nanocrown.

The 2019 coronavirus disease (COVID-19) outbreak created an unprecedented need for rapid, sensitive, and cost-effective point-of-care diagnostic tests to prevent and mitigate the spread of the SARS-CoV-2 virus. Herein, we demonstrated an advanced lateral flow immunoassay (LFIA) platform with dual-functional [colorimetric and surface-enhanced Raman scattering (SERS)] detection of the spike 1 (S1) protein of SARS-CoV-2. The nanosensor was integrated with a specially designed core-gap-shell morphology consisting of a gold shell decorated with external nanospheres, a structure referred to as gold nanocrown (GNC), labeled with a Raman reporter molecule 1,3,3,1',3',3'-hexamethyl-2,2'-indotricarbocyanine iodide (HITC) to produce a strong colorimetric signal as well as an enhanced SERS signal.

Surface-Enhanced Raman Spectroscopy-Based Detection of Micro-RNA Biomarkers for Biomedical Diagnosis Using a Comparative Study of Interpretable Machine Learning Algorithms.

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications due to narrow spectral features that allow multiplex analysis. We have previously developed a multiplexed, SERS-based nanosensor for micro-RNA (miRNA) detection called the inverse molecular sentinel (iMS). Machine learning (ML) algorithms have been increasingly adopted for spectral analysis due to their ability to discover underlying patterns and relationships within large and complex data sets.

Multiplex SERS detection of polycyclic aromatic hydrocarbon (PAH) pollutants in water samples using gold nanostars and machine learning analysis.

Polycyclic aromatic hydrocarbons (PAHs) have attracted a lot of environmental concern because of their carcinogenic and mutagenic properties, and the fact they can easily contaminate natural resources such as drinking water and river water. This study presents a simple and sensitive point-of-care SERS detection of PAHs combined with machine learning algorithms to predict the PAH content more precisely and accurately in real-life samples such as drinking water and river water. We first synthesized multibranched sharp-spiked surfactant-free gold nanostars (GNSs) that can generate strong surface-enhanced Raman scattering (SERS) signals, which were further coated with cetyltrimethylammonium bromide (CTAB) for long-term stability of the GNSs as well as to trap PAHs.

Machine learning using convolutional neural networks for SERS analysis of biomarkers in medical diagnostics.

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications because of narrow spectral features that allow multiplexed analysis. Machine learning (ML) has been used for non-dye-labeled SERS spectra but has not been applied to SERS dye-labeled materials with known spectral shapes. Here, we compare the performances of spectral decomposition, support vector regression, random forest regression, partial least squares regression, and convolutional neural network (CNN) for SERS "spectral unmixing" from a multiplexed mixture of 7 SERS-active "nanorattles" loaded with different dyes for mRNA biomarker detection.

Plasmonic nanorod probes' journey inside plant cells for SERS sensing and multimodal imaging.

Nanoparticle-based platforms are gaining strong interest in plant biology and bioenergy research to monitor and control biological processes in whole plants. However, monitoring of biomolecules using nanoparticles inside plant cells remains challenging due to the impenetrability of the plant cell wall to nanoparticles beyond the exclusion limits (5-20 nm). To overcome this physical barrier, we have designed unique bimetallic silver-coated gold nanorods (AuNR@Ag) capable of entering plant cells, while conserving key plasmonic properties in the near-infrared (NIR).

miRNA probe integrated biosensor platform using bimetallic nanostars for amplification-free multiplexed detection of circulating colorectal cancer biomarkers in clinical samples.

There is a critical need for sensitive and rapid detection technologies utilizing molecular biotargets such as microRNAs (miRNAs), which regulate gene expression and are a promising class of diagnostic biomarkers for disease detection. Here, we present the development and fabrication of a highly reproducible and robust plasmonic bimetallic nanostar biosensing platform to detect miRNA targets using surfaced-enhanced Raman scattering (SERS)-based gene probes called the inverse Molecular Sentinel (iMS). We investigated and optimized the integration of iMS gene probes onto this SERS substrate, achieving ultra-sensitive detection with limits of detection of 6.