Flow-based approach for scalable fabrication of Ag nanostructured substrate as a platform for surface-enhanced Raman scattering

Kanike, Chiranjeevi; Wu, Hongyan; A.W., Zaibudeen; Li, Yanan; Wei, Zixiang; Unsworth, Larry D.; Atta, Arnab; Zhang, Xuehua

doi:10.1016/j.cej.2023.144019

Cited by 10 publications

(19 citation statements)

References 58 publications

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“…In the first set (Ouzo), reported by Dabodiya et al, aqueous samples containing R6G or TC were preconcentrated into a tiny droplet by the evaporating Ouzo method. 22 In the second set (Ag-NP), all three compounds were tested using Ag nanostructured Si substrate fabricated using a flow-based approach as reported by Kanike et al 6 Figure 3a provides an overview of the evaporating Ouzo technique, while Figure 3b shows the flow approach. The set of environmental spectra was collected using the methodology from Kanike et al, and its spectra are available in the Supporting Information, Figure S4.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…All spectral acquisitions were carried out with 0.1 W power, gratings of 1200 grooves/mm, exposure time of 10 s, and 10 acquisitions with the improved signal-to-noise ratio. 6,22 Machine Learning Techniques. In this work, we employed a supervised classification approach for the prediction of concentrations.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…4,5 Finally, nonuniformity in SERS substrate contributes hugely to variance and can lead to SERS measurements that have no signal or spectra from higher concentrations having similar intensity to that of lower concentration. 6 Figure 4 displays a principal component manifold for the R6G data sets. Spectra that have reduced signal due to substrate density spatial variation will be isolated or overlap with other classes.…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

“…23,25 For TC, four bulk powder spectra were collected for comparison. 6 These bulk powder spectra are averaged and presented in Figure S6 as the "Raw" spectra. Due to the peak enhancement and possible peak shift of SERS spectra, the matching of the SERS data to bulk powder data in the TC case will not be as good as the matching for the other chemicals, which use SERS references.…”

Section: Identification Of Characteristic Peaksmentioning

confidence: 99%

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Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

Jayaprakash,

You,

Kanike

et al. 2024

Environ. Sci. Technol.

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Surface-enhanced Raman spectroscopy (SERS) has been well explored as a highly effective characterization technique that is capable of chemical pollutant detection and identification at very low concentrations. Machine learning has been previously used to identify compounds based on SERS spectral data. However, utilization of SERS to quantify concentrations, with or without machine learning, has been difficult due to the spectral intensity being sensitive to confounding factors such as the substrate parameters, orientation of the analyte, and sample preparation technique. Here, we demonstrate an approach for predicting the concentration of sample pollutants from SERS spectra using machine learning. Frequency domain transform methods, including the Fourier and Walsh−Hadamard transforms, are applied to spectral data sets of three analytes (rhodamine 6G, chlorpyrifos, and triclosan), which are then used to train machine learning algorithms. Using standard machine learning models, the concentration of the sample pollutants is predicted with >80% crossvalidation accuracy from raw SERS data. A cross-validation accuracy of 85% was achieved using deep learning for a moderately sized data set (∼100 spectra), and 70−80% was achieved for small data sets (∼50 spectra). Performance can be maintained within this range even when combining various sample preparation techniques and environmental media interference. Additionally, as a spectral pretreatment, the Fourier and Hadamard transforms are shown to consistently improve prediction accuracy across multiple data sets. Finally, standard models were shown to accurately identify characteristic peaks of compounds via analysis of their importance scores, further verifying their predictive value.

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Section: ■ Materials and Methodsmentioning

confidence: 99%

Section: ■ Materials and Methodsmentioning

confidence: 99%

Section: ■ Materials and Methodsmentioning

confidence: 99%

Section: Identification Of Characteristic Peaksmentioning

confidence: 99%

See 2 more Smart Citations

Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

Jayaprakash,

You,

Kanike

et al. 2024

Environ. Sci. Technol.

Self Cite

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“…26,27 Recently Kanike et al developed a droplet-based microfluidic approach to fabricate ordered silver nanostructures over a surface area >60 cm 2 . 28 They achieved a limit of detection with R6G of 10 −12 M. Whilst these approaches have shown promise, they are often associated with high cost and complexity. Furthermore, they may not be suitable for mass production, particularly when achieving a consistent SERS signal across a large area is necessary.…”

Section: Introductionmentioning

confidence: 99%

Focusing ion funnel-assisted ambient electrospray enables high-density and uniform deposition of non-spherical gold nanoparticles for highly sensitive surface-enhanced Raman scattering

Akbali,

Boisdon,

Smith

et al. 2023

Analyst

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Plasmonic Nanostructures Grown from Reacting Droplet‐In‐Microwell Array on Flexible Films for Quantitative Surface‐Enhanced Raman Spectroscopy in Plant Wearable In Situ Detection

Kanike,

Lu,

et al. 2024

Advanced Materials

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Plant wearable detection has garnered significant interest in advancing agricultural intelligence and promoting sustainable food production amidst the challenges of climate change. Accurately monitoring plant health and agrochemical residue levels necessitates qualities such as precision, affordability, simplicity, and noninvasiveness. Here, a novel attachable plasmonic film is introduced and designed for on‐site detection of agrochemical residues utilizing surface‐enhanced Raman spectroscopy (SERS). By functionalizing a thin polydimethylsiloxane film with silver nanoparticles via controlled droplet reactions in micro‐well arrays, a plasmonic film is achieved that not only maintains optical transparency for precise analyte localization but also conforms closely to the plant surface, facilitating highly sensitive SERS measurements. The reliability of this film enables accurate identification and quantification of individual compounds and their mixtures, boasting an ultra‐low detection limit ranging from 10−16 to 10−13 m, with mini mal relative standard deviation. To showcase its potential, on‐field detection of pesticide residues on fruit surfaces is conducted using a handheld Raman spectrometer. This advancement in fabricating plasmonic nanostructures on flexible films holds promise for expanding SERS applications beyond plant monitoring, including personalized health monitoring, point‐of‐care diagnosis, wearable devices for human–machine interface, and on‐site monitoring of environmental pollutants.

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Flow-based approach for scalable fabrication of Ag nanostructured substrate as a platform for surface-enhanced Raman scattering

Cited by 10 publications

References 58 publications

Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra

Focusing ion funnel-assisted ambient electrospray enables high-density and uniform deposition of non-spherical gold nanoparticles for highly sensitive surface-enhanced Raman scattering

Plasmonic Nanostructures Grown from Reacting Droplet‐In‐Microwell Array on Flexible Films for Quantitative Surface‐Enhanced Raman Spectroscopy in Plant Wearable In Situ Detection

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