Recent Developments on Metal Nanoparticles for SERS Applications

Bora, Tanujjal

doi:10.5772/intechopen.71573

Cited by 17 publications

(14 citation statements)

References 71 publications

(77 reference statements)

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“…SERS is a surface-sensitive technique that can enhance the intensity of Raman scattering at the level of several orders of magnitude by exploiting surface plasmons (SPs) of metallic nanostructures ( Pérez-Jiménez et al, 2020 ), also known as SERS substrate, which is sufficient to analyze bacterial samples at single-cell resolution ( Weiss et al, 2019 ). Common examples for SERS substrates include silver and gold nanoparticles (NPs) since they do not have any Raman active modes ( Bora, 2018 ) and show outstanding SERS enhancements ( Pérez-Jiménez et al, 2020 ). Comparatively speaking, silver colloidal nanoparticles (AgNPs) has a high molar extinction coefficient from visible to near infrared region, whereas gold is commonly used for red and near infrared regions; in addition, AgNPs show higher plasmon quality than that of gold NPs ( Wei et al, 2018 ; Pérez-Jiménez et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

et al. 2021

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Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

et al. 2021

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“…Nanoparticles (NPs) have been widely investigated by the scientific community, as they give flexibility to a wide variety of applications, including photovoltaics [1], electrical insulation systems [2], electronic [3], photonic devices [4] and sensors [5]. The importance of NPs was especially realized when it was discovered that the size of the NPs can have an influence on the final properties compared to the bulk material.…”

Section: Introductionmentioning

confidence: 99%

Modification of the optical properties and nano-crystallinity of anatase TiO2nanoparticles thin film using low pressure O2 plasma treatment

et al. 2020

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TiO2 nanoparticles (NPs) thin films with a thickness of 50 nm and a coreshell nanostructure were prepared using the spin coating technique and were treated using an inductively coupled O2 plasma at low pressure. While no significant features were observed during pumping, two kinetics can be identified during plasma processing using in situ spectroscopic ellipsometry. For very short plasma treatment times, the removal of solvent-based organic moieties surrounding TiO2 nanoparticles produces a densification of the inorganic TiO2 films. Such mineralization effect by the O2 plasma treatment was confirmed by X-ray photoelectron microscopy. For larger time scales, the average size of TiO2 NPs rises and significant changes of the optical properties occur. While no significant changes in the film nanostructure were observed by Scanning Electron Microscopy, a rise in the crystallite size and an increase in the agglomeration of TiO2 nanoparticles were confirmed by Transmission Electron Microscopy and Atomic Force Microscopy, respectively. The mechanisms involved in such plasma-induced modification of nanostructured TiO2 thin films are discussed in line with the energy fluxes of plasma-generated species.

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“…The changes in the scattered photon frequency are proportional to the difference in vibrational energy levels of the molecule. However, only a small fraction of incident photons shows inelastic scattering and the Raman signal is extremely weak [ 69 , 70 ]. McQuillan and collaborators observed for the first time a strong enhancement in the intensity of the pyridine Raman signal, when dropped on a rough silver electrode [ 71 ].…”

Section: Anisotropic Gold Nanoparticles In Bio-sensingmentioning

confidence: 99%

Anisotropic Gold Nanoparticles in Biomedical Applications

Kohout

Santi

Polito

2018

IJMS

104

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Gold nanoparticles (AuNPs) play a crucial role in the development of nanomedicine, principally due to their unique photophysical properties and high biocompatibility. The possibility to tune and customize the localized surface plasmon resonance (LSPR) toward near-infrared region by modulating the AuNP shape is one of the reasons for the huge widespread use of AuNPs. The controlled synthesis of no-symmetrical nanoparticles, named anisotropic, is an exciting goal achieved by the scientific community which explains the exponential increase of the number of publications related to the synthesis and use of such type of AuNPs. Even with such steps forward and the AuNP translation in clinic being done, some key issues are still remain and they are related to a reliable and scalable production, a full characterization, and to the development of nanotoxicology studies on the long run. In this review we highlight the very recent advances on the synthesis of the main classes of anisotropic AuNPs (nanorods, nanourchins and nanocages) and their use in the biomedical fields, in terms of diagnosis and therapeutics.

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Recent Developments on Metal Nanoparticles for SERS Applications

Cited by 17 publications

References 71 publications

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species

Modification of the optical properties and nano-crystallinity of anatase TiO2nanoparticles thin film using low pressure O2 plasma treatment

Anisotropic Gold Nanoparticles in Biomedical Applications

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