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

Atta, Supriya; Li, Joy Qiaoyi; Vo-Dinh, Tuan

doi:10.1039/d3an00636k

Cited by 11 publications

(2 citation statements)

References 59 publications

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“…42 The SERS enhancement factor of GNS depends on several factors, such as the plasmon resonance peak maximum of the nanoparticles, and the number of hot spots. 37,44–47 Therefore, it is imperative to fine-tune the GNS morphology to achieve maximum SERS enhancement. For instance, smaller-sized seeds can produce GNS with a plasmon resonance peak closer to the laser excitation wavelength (785 nm).…”

Section: Resultsmentioning

confidence: 99%

A simple low-cost flexible plasmonic patch based on spiky gold nanostars for ultra-sensitive SERS sensing

Atta,

Canning,

Vo-Dinh

2024

Analyst

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Section: Resultsmentioning

confidence: 99%

A simple low-cost flexible plasmonic patch based on spiky gold nanostars for ultra-sensitive SERS sensing

Atta,

Canning,

Vo-Dinh

2024

Analyst

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“…A thorough examination of detecting multiplex pesticides demands meticulous effort and parameter optimization for conducting SERS measurements. It is noteworthy that we have successfully analyzed the SERS data of a multicomponent mixture consisting of five polyaromatic hydrocarbons (PAH) using artificial intelligence (AI) with machine learning algorithms based on the convolutional neural network (CNN) model in order to discriminate the PAH in samples more precisely and accurately . A detailed investigation for quantitative determination of a large number of pesticides will be conducted in future studies, which could include AI-enabled analysis.…”

Section: Resultsmentioning

confidence: 99%

Rapid Solution-Based SERS Detection of Pesticides Using Graphene Oxide-Coated Silver–Gold Nanostars

Atta,

Sharaf,

Vo-Dinh

2024

ACS Appl. Nano Mater.

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This study presents the development of an affordable and straightforward solution-based surface-enhanced Raman scattering (SERS) detection platform, employing plasmonic-active silver−gold nanostars coated with graphene oxide (GO-SGNS). The inclusion of GO with SGNS produced several advantages, including colloidal stability, SERS detection reproducibility, and improved sensitivity. The solution-based SERS platform exhibited an ultralow detection capability with high reproducibility, and we achieved the limit of detection (LOD) as low as 10, 50, 100, and 100 pM for the pesticides-ziram, phorate, triazophos, and azinphos-methyl, respectively. To illustrate the potential for food safety, the surfaces of apples were pretreated with pesticides and were directly analyzed without any sample pretreatment. Furthermore, the solution-based SERS platform provided rapid detection of the four pesticides in binary and quaternary mixtures on apple surfaces, indicating good feasibility and multiplex capability of the method.

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Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Bi,

Lin,

Chen

et al. 2023

Small Methods

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Surface‐enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever‐sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high‐level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above‐mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.

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

Abstract: Polycyclic aromatic hydrocarbons (PAH) have gained a lot of environmental concern because of their carcinogenic, and mutagenic properties, and they can easily contaminate natural resources such as drinking water and...

Cited by 11 publications

References 59 publications

A simple low-cost flexible plasmonic patch based on spiky gold nanostars for ultra-sensitive SERS sensing

A simple low-cost flexible plasmonic patch based on spiky gold nanostars for ultra-sensitive SERS sensing

Rapid Solution-Based SERS Detection of Pesticides Using Graphene Oxide-Coated Silver–Gold Nanostars

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

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