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Past Present and Future of Raman Spectroscopy
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Past Present and Future of Raman Spectroscopy

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Analytical Chemistry".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 8174

Special Issue Editors

Dr. Kundan Sivashanmugan

E-Mail Website
Guest Editor
1. Nano Life Science Institute, Kanazawa University, Kanazawa, Japan
2. University of Maryland School of Medicine, Baltimore, MD, USA
Interests: plasmonic; biosensing; biophotonics; SERS; nanoendoscopy
Special Issues, Collections and Topics in MDPI journals
Dr. Xianming Kong

E-Mail Website
Co-Guest Editor
School of Petrochemical Engineering, Liaoning Petrochemical University, Fushun 113001, China
Interests: spectroscopy; nanomaterials; plasmonic; sensor
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In this issue, we concentrate on the fundamental advancements in Raman spectroscopy for a wide range of applications as well as the emergence of artificial intelligence-based Raman spectroscopic sensing.

In the last 20 years, more and more scientific fields have become interested in Raman spectroscopy because it could be used in fields that need non-destructive microscopic chemical sensing and biological imaging. The “Raman effect” is remarkable for being based on the inelastic scattering of an incident photon by atoms and molecules in a substance. It may occur in solids, liquids, or gases. The technology behind Raman spectroscopy has made tremendous progress in recent years to address problems including fluorescence, limited sensitivity, and weak Raman signals. In addition, many more advanced Raman techniques than the conventional dispersive Raman approach have been developed to fulfill the challenges of analysis. These techniques include a Fourier Transform Raman Spectrometer, Confocal Raman Microscopy, Surface Enhanced Raman Scattering (SERS), Tip-enhanced Raman Scattering (TERS), and Coherent Anti-Stokes Raman Scattering (CARS). Physicists and chemists have used Raman scattering to investigate the chemical composition of several liquid and solid materials. On the other hand, biomedical research has just lately begun to use SERS, TERS, and CARS spectroscopy. Medical researchers are increasingly using Raman spectroscopy because it can provide exact quantitative analyses of the biochemical composition of biological materials, such as cell aging and virus identification. Further, probe-based Raman spectroscopy was used to study in vivo cell and tissue samples for diagnosis. Overall, Raman spectroscopy is presently employed for a broad variety of material characterizations as well as biomolecule sensing or diagnostics.

This Special Issue is especially interested in 1) Raman spectroscopy-based material characterization, 2) the Raman effect from plasmonic nanomaterials for analyzing chemical and biological samples, 3) probe-based Raman spectroscopy for clinical applications, and 4) computational and developing artificial intelligence based on Raman libraries. Researchers are encouraged to contribute to this Special Issue; Full research papers, brief communications, or reviews focusing on the keywords specified below are all acceptable forms of contribution.

Dr. Kundan Sivashanmugan
Dr. Xianming Kong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • materials characterization
  • plasmonic-nanomaterials
  • biomaterials
  • biophotonics
  • biosensing
  • label-free molecular imaging
  • probe-based Raman spectroscopy
  • computational Raman
  • TERS
  • SERS

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Published Papers (4 papers)

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Research

16 pages, 2170 KiB  
Article
Unsupervised Clustering-Assisted Method for Consensual Quantitative Analysis of Methanol–Gasoline Blends by Raman Spectroscopy
by Biao Lu, Shilong Wu, Deliang Liu, Wenping Wu, Wei Zhou and Lei-ming Yuan
Molecules 2024, 29(7), 1427; https://doi.org/10.3390/molecules29071427 - 22 Mar 2024
Viewed by 977
Abstract
Methanol–gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol–gasoline blends using [...] Read more.
Methanol–gasoline blends have emerged as a promising and environmentally friendly bio-fuel option, garnering widespread attention and promotion globally. The methanol content within these blends significantly influences their quality and combustion performance. This study explores the qualitative and qualitative analysis of methanol–gasoline blends using Raman spectroscopy coupled with machine learning methods. Experimentally, methanol–gasoline blends with varying methanol concentrations were artificially configured, commencing with initial market samples. For qualitative analysis, the partial least squares discriminant analysis (PLS-DA) model was employed to classify the categories of blends, demonstrating high prediction performance with an accuracy of nearly 100% classification. For the quantitative analysis, a consensus model was proposed to accurately predict the methanol content. It integrates member models developed on clustered variables, using the unsupervised clustering method of the self-organizing mapping neural network (SOM) to accomplish the regression prediction. The performance of this consensus model was systemically compared to that of the PLS model and uninformative variable elimination (UVE)–PLS model. Results revealed that the unsupervised consensus model outperformed other models in predicting the methanol content across various types of methanol gasoline blends. The correlation coefficients for prediction sets consistently exceeded 0.98. Consequently, Raman spectroscopy emerges as a suitable choice for both qualitative and quantitative analysis of methanol–gasoline blend quality. This study anticipates an increasing role for Raman spectroscopy in analysis of fuel composition. Full article
(This article belongs to the Special Issue Past Present and Future of Raman Spectroscopy)
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13 pages, 2578 KiB  
Article
Impact of Formalin- and Cryofixation on Raman Spectra of Human Tissues and Strategies for Tumor Bank Inclusion
by Giulia Mirizzi, Finn Jelke, Michel Pilot, Karoline Klein, Gilbert Georg Klamminger, Jean-Jacques Gérardy, Marily Theodoropoulou, Laurent Mombaerts, Andreas Husch, Michel Mittelbronn, Frank Hertel and Felix Bruno Kleine Borgmann
Molecules 2024, 29(5), 1167; https://doi.org/10.3390/molecules29051167 - 6 Mar 2024
Cited by 1 | Viewed by 1325
Abstract
Reliable training of Raman spectra-based tumor classifiers relies on a substantial sample pool. This study explores the impact of cryofixation (CF) and formalin fixation (FF) on Raman spectra using samples from surgery sites and a tumor bank. A robotic Raman spectrometer scans samples [...] Read more.
Reliable training of Raman spectra-based tumor classifiers relies on a substantial sample pool. This study explores the impact of cryofixation (CF) and formalin fixation (FF) on Raman spectra using samples from surgery sites and a tumor bank. A robotic Raman spectrometer scans samples prior to the neuropathological analysis. CF samples showed no significant spectral deviations, appearance, or disappearance of peaks, but an intensity reduction during freezing and subsequent recovery during the thawing process. In contrast, FF induces sustained spectral alterations depending on molecular composition, albeit with good signal-to-noise ratio preservation. These observations are also reflected in the varying dual-class classifier performance, initially trained on native, unfixed samples: The Matthews correlation coefficient is 81.0% for CF and 58.6% for FF meningioma and dura mater. Training on spectral differences between original FF and pure formalin spectra substantially improves FF samples’ classifier performance (74.2%). CF is suitable for training global multiclass classifiers due to its consistent spectrum shape despite intensity reduction. FF introduces changes in peak relationships while preserving the signal-to-noise ratio, making it more suitable for dual-class classification, such as distinguishing between healthy and malignant tissues. Pure formalin spectrum subtraction represents a possible method for mathematical elimination of the FF influence. These findings enable retrospective analysis of processed samples, enhancing pathological work and expanding machine learning techniques. Full article
(This article belongs to the Special Issue Past Present and Future of Raman Spectroscopy)
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10 pages, 3371 KiB  
Article
Quantitative Sensing of Domoic Acid from Shellfish Using Biological Photonic Crystal Enhanced SERS Substrates
by Subhavna Juneja, Boxin Zhang, Nabila Nujhat and Alan X. Wang
Molecules 2022, 27(23), 8364; https://doi.org/10.3390/molecules27238364 - 30 Nov 2022
Cited by 5 | Viewed by 1888
Abstract
Frequent monitoring of sea food, especially shellfish samples, for the presence of biotoxins serves not only as a valuable strategy to mitigate adulteration associated health risks, but could also be used to develop predictive models to understand algal explosion and toxin trends. Periodic [...] Read more.
Frequent monitoring of sea food, especially shellfish samples, for the presence of biotoxins serves not only as a valuable strategy to mitigate adulteration associated health risks, but could also be used to develop predictive models to understand algal explosion and toxin trends. Periodic toxin assessment is often restricted due to poor sensitivity, multifarious cleaning/extraction protocols and high operational costs of conventional detection methods. Through this work, a simplistic approach to quantitatively assess the presence of a representative marine neurotoxin, Domoic acid (DA), from spiked water and crab meat samples is presented. DA sensing was performed based on surface-enhanced Raman scattering (SERS) using silver nanoparticle enriched diatomaceous earth—a biological photonic crystal material in nature. Distinctive optical features of the quasi-ordered pore patterns in diatom skeleton with sporadic yet uniform functionalization of silver nanoparticles act as excellent SERS substrates with improved DA signals. Different concentrations of DA were tested on the substrates with the lowest detectable concentration being 1 ppm that falls well below the regulatory DA levels in seafood (>20 ppm). All the measurements were rapid and were performed within a measurement time of 1 min. Utilizing the measurement results, a standard calibration curve between SERS signal intensity and DA concentration was developed. The calibration curve was later utilized to predict the DA concentration from spiked Dungeness crab meat samples. SERS based quantitative assessment was further complemented with principal component analysis and partial least square regression studies. The tested methodology aims to bring forth a sensitive yet simple, economical and an extraction free routine to assess biotoxin presence in sea food samples onsite. Full article
(This article belongs to the Special Issue Past Present and Future of Raman Spectroscopy)
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15 pages, 6832 KiB  
Article
Highly Sensitive, Robust, and Recyclable TiO2/AgNP Substrate for SERS Detection
by Hsing-Yu Wu, Hung-Chun Lin, Yung-Hsien Liu, Kai-Lin Chen, Yu-Hsun Wang, Yung-Shin Sun and Jin-Cherng Hsu
Molecules 2022, 27(19), 6755; https://doi.org/10.3390/molecules27196755 - 10 Oct 2022
Cited by 9 | Viewed by 2974
Abstract
Label-free biosensors provide an important platform for detecting chemical and biological substances without needing extra labeling agents. Unlike surface-based techniques such as surface plasmon resonance (SPR), interference, and ellipsometry, surface-enhanced Raman spectroscopy (SERS) possesses the advantage of monitoring analytes both on surfaces and [...] Read more.
Label-free biosensors provide an important platform for detecting chemical and biological substances without needing extra labeling agents. Unlike surface-based techniques such as surface plasmon resonance (SPR), interference, and ellipsometry, surface-enhanced Raman spectroscopy (SERS) possesses the advantage of monitoring analytes both on surfaces and in solutions. Increasing the SERS enhancement is crucial to preparing high-quality substrates without quickly losing their stability, sensitivity, and repeatability. However, fabrication methods based on wet chemistry, nanoimprint lithography, spark discharge, and laser ablation have drawbacks of waste of time, complicated processes, or nonreproducibility in surface topography. This study reports the preparation of recyclable TiO2/Ag nanoparticle (AgNP) substrates by using simple arc ion plating and direct-current (dc) magnetron sputtering technologies. The deposited anatase-phased TiO2 ensured the photocatalytic degradation of analytes. By measuring the Raman spectra of rhodamine 6G (R6G) in titrated concentrations, a limit of detection (LOD) of 10−8 M and a SERS enhancement factor (EF) of 1.01 × 109 were attained. Self-cleaning was performed via UV irradiation, and recyclability was achieved after at least five cycles of detection and degradation. The proposed TiO2/AgNP substrates have the potential to serve as eco-friendly SERS enhancers for label-free detection of various chemical and biological substances. Full article
(This article belongs to the Special Issue Past Present and Future of Raman Spectroscopy)
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