journal article·2025-06-13

Multibranched Magnetic Core–Shell Gold Nanostars for In Situ Solution-Based SERS Detection

Supriya Atta, Taylor L. Thorsen, Sebastian Sanchez, Yuanhao Zhao, Tuan Vo-Dinh

ACS Applied Nano Materials· Volume 8· Issue 23
Publisher page

Abstract

Core–shell gold nanoparticles offer significant potential for enhancing surface-enhanced Raman spectroscopy (SERS) detection by integrating the elemental properties of both the core and shell materials. However, achieving an optimized core–shell nanoparticle system with uniformly distributed, densely packed hotspots for highly sensitive, direct in situ SERS detection of analytes remains a significant challenge. In this study, we introduce a simple, sensitive, and direct in situ SERS detection platform using multibranched magnetic core–shell gold nanostars (mGNS). This system capitalizes on the enhanced SERS signal from the branched nanostar morphology coupled with magnetic concentration effects, leading to a significantly amplified SERS response. The optimized mGNS-3, with ideal size and spike density, demonstrated the highest SERS enhancement using para-mercaptobenzoic acid (pMBA) as a model analyte. This solution-based magnetic SERS method achieved a detection limit of 1.5 nM, with the SERS signal being five times stronger than conventional SERS measurements. To showcase its practical utility, we employed the platform for the direct detection of ceftriaxone, an antibiotic, in milk without any sample preparation. The platform achieved a detection limit of 2.4 nM, which is significantly lower than the regulatory limits set by the USA and the European Union for antibiotic concentrations in milk. Overall, this magnetic SERS platform based on mGNS highlights its potential for highly sensitive antibiotic detection in point-of-care settings without the need for preprocessing.
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Research context

OpenAlex topics: Gold and Silver Nanoparticles Synthesis and Applications · Advanced biosensing and bioanalysis techniques · Nanocluster Synthesis and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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