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

ABSTRACT: 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. We utilized CTAB-capped GNSs

SUBMITTER: Atta S 

PROVIDER: S-EPMC12768947 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature