Microplasma Controlled Nanogold Sensor for SERS of Aliphatic and Aromatic Explosives with PCA-KNN Recognition

Nanogold is an emerging material for enhancing surface-enhanced Raman scattering (SERS), which enables the detection of hazardous analytes at trace levels. This study presents a simple, single-step plasma synthesis method to control the size and yield of Au nanoparticles by using plasma-liquid redox chemistry. The pin-based argon plasma reduces the Au<sup>3+</sup> precursor in under 5 min, synthesizing Au spherical particles ranging from ∼20 nm at 0.025 mM to ∼90 nm at 1.0 mM, in addition to plate-like particles occurring at concentrations of 0.25-1.0 mM. The enhanced SERS responses correlated with the UV-vis absorption and reflectance profiles, which can be attributed to synergistic plasmonic hotspots created by the sphere-sphere, plate-sphere, and plate-plate nanogold interactions. This nanogold mixture, combined with gold-plated CPU grid pin arrays, facilitated the detection of trace explosives, including aromatic (TNT, TNB, and TNP) and aliphatic (RDX, PETN, and HMX) compounds. We demonstrate that stabler aliphatic analytes, associated with lower vapor pressure (10<sup>-8</sup>-10<sup>-11</sup> atm), exhibit smaller signal fluctuations (RSD ∼ 6-10%) compared to their more volatile (10<sup>-5</sup> atm) aromatic (RSD ∼ 12-17%) counterparts at similar analyte concentrations. The calculated limit of detection (LoD) was found to be ∼2-6 nM and ∼600-900 pM for aromatic and aliphatic explosives, respectively. Finally, we show that the poorer performance of aromatic explosives under the same sensing conditions affects SERS-PCA separation, which can then be improved either by a machine learning approach (PCA with k-NN classification) or by consideration of a specific NO<sub>2</sub> symmetric stretching fingerprint range.

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Publication Details

Journal
ACS Sensors
Published
2024-12-24
DOI
https://doi.org/10.1021/acssensors.4c02651
Citations
8
Primary Topic
Gold and Silver Nanoparticles Synthesis and Applications
Type
article
Field-Weighted Citation Impact
0.94

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article

Microplasma Controlled Nanogold Sensor for SERS of Aliphatic and Aromatic Explosives with PCA-KNN Recognition

Martina Modic, Uroš Cvelbar, Vasyl Shvalya, Jaka Olenik et al.
8 citations
ACS Sensors
Gold and Silver Nanoparticles Synthesis and Applications
0.94
article

Microplasma Controlled Nanogold Sensor for SERS of Aliphatic and Aromatic Explosives with PCA-KNN Recognition

Martina Modic, Uroš Cvelbar, Vasyl Shvalya, Jaka Olenik, James L. Walsh, Damjan Vengust
article en
8 citations

Abstract

Nanogold is an emerging material for enhancing surface-enhanced Raman scattering (SERS), which enables the detection of hazardous analytes at trace levels. This study presents a simple, single-step plasma synthesis method to control the size and yield of Au nanoparticles by using plasma-liquid redox chemistry. The pin-based argon plasma reduces the Au<sup>3+</sup> precursor in under 5 min, synthesizing Au spherical particles ranging from ∼20 nm at 0.025 mM to ∼90 nm at 1.0 mM, in addition to plate-like particles occurring at concentrations of 0.25-1.0 mM. The enhanced SERS responses correlated with the UV-vis absorption and reflectance profiles, which can be attributed to synergistic plasmonic hotspots created by the sphere-sphere, plate-sphere, and plate-plate nanogold interactions. This nanogold mixture, combined with gold-plated CPU grid pin arrays, facilitated the detection of trace explosives, including aromatic (TNT, TNB, and TNP) and aliphatic (RDX, PETN, and HMX) compounds. We demonstrate that stabler aliphatic analytes, associated with lower vapor pressure (10<sup>-8</sup>-10<sup>-11</sup> atm), exhibit smaller signal fluctuations (RSD ∼ 6-10%) compared to their more volatile (10<sup>-5</sup> atm) aromatic (RSD ∼ 12-17%) counterparts at similar analyte concentrations. The calculated limit of detection (LoD) was found to be ∼2-6 nM and ∼600-900 pM for aromatic and aliphatic explosives, respectively. Finally, we show that the poorer performance of aromatic explosives under the same sensing conditions affects SERS-PCA separation, which can then be improved either by a machine learning approach (PCA with k-NN classification) or by consideration of a specific NO<sub>2</sub> symmetric stretching fingerprint range.

ACS SensorsVol. 10(1)
Jožef Stefan Institute (SI), Plasma Technology (United States) (US), University of York (GB), Gas Technology Institute (US)
North Atlantic Treaty Organization, Javna Agencija za Raziskovalno Dejavnost RS, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 27%
Gold and Silver Nanoparticles Synthesis and Applications
0.94
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