• Journal of Korea Multimedia Society
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• v.14 no.8
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• pp.1010-1019
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• 2011

This was a preliminary research for the goal of understanding between internal structure of Osteogenesis Imperfecta Murine (OIM) bone and its fragility. 54 hyperspectral bone data sets were captured by using JASCO 2000 Raman spectrometer at UMKC-CRISP (University of Missouri-Kansas City Center for Research on Interfacial Structure and Properties). Each data set consists of 1,091 data points from 9 OIM bones. The original captured hyperspectral data sets were noisy and base-lined ones. We removed the noise and corrected the base-lined data for the final efficient classification. High dimensional Raman hyperspectral data on OIM bones was reduced by Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) and efficiently classified for the first time. We confirmed OIM bones could be classified such as strong, middle and weak one by using the coefficients of their PCA or LDA. Through experiment, we investigated the efficiency of classification on the reduced OIM bone data by the Bayesian classifier and K -Nearest Neighbor (K-NN) classifier. As the experimental result, the case of LDA reduction showed higher classification performance than that of PCA reduction in the two classifiers. K-NN classifier represented better classification rate, compared with Bayesian classifier. The classification performance of K-NN was about 92.6% in case of LDA.

• Analytical Science and Technology
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• v.5 no.4
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• pp.359-366
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• 1992

Using micro-Raman spectrometer, we investigated the evaluation of microstress on silicon surface after the local thermal oxidation. The induced stress of silicon surface after local thermal oxidation shows maximum value at the interface of silicon oxide and active area. The smaller the size of active area, the larger stress. From the evaluation of three other device isolation processes, A, B and moB, whose active size has $0.45{\mu}m$ in length, moB process is turned out to have the lowest stress value and the smallest bird's beak effect.

• 한국신재생에너지학회:학술대회논문집
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• 2008.10a
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• pp.223-225
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• 2008

하이드레이트는 저온.고압에서 저분자량의 게스트(guest)가 호스트(host)인 물분자 속에 포획되어 만들어지는데 일련의 과정은 물리적 반응을 통해 생성된다.본 연구에서는$CO_2$보다 지구온난화지수(Global Warming Potential)가 23,900배 높은 $SF_6$의 회수 및 정제기술로써 하이드레이트화를 이용하는 신기술 개발의 일환으로 분광학적 접근을 통해 $SF_6$ 혼합 하이드레이트의 정성 및 정량분석을 수행하였다. Raman Shift 분석 결과 $SF_6$의 $770cm^{-1}$에서 $v_1$ 진동주파수를 확인함으로써 하이드레이트 내 $SF_6$가 안정적으로 포집됨을 확인하였고 혼합가스 내 $SF_6$ 농도별로 만들어진 샘플의 Raman Shift를 통해서 $SF_6$의 하이드레이트 전환율을 가늠할 수 있었다.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.15-15
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• 2011

Atom-thick graphene membrane and nano-sized graphene objects (NGOs) hold substantial potential for applications in future molecular-scale integrated electronics, transparent conducting membranes, nanocomposites, etc. To realize this potential, chemical properties of graphene need to be understood and diagnostic methods for various NGOs are also required. To meet these needs, chemical properties of graphene and optical diagnostics of graphene nanoribbons (GNRs) have been explored by Raman spectroscopy, AFM and STM scanning probes. The first part of the talk will illustrate the role of underlying silicon dioxide substrates and ambient gases in the ubiquitous hole doping of graphene. An STM study reveals that thermal annealing generates out-of-plane deformation of nanometer-scale wavelength and distortion in $sp^2$ bonding on an atomic scale. Graphene deformed by annealing is found to be chemically active enough to bind molecular oxygen, which leads to a strong hole-doping. The talk will also introduce Raman spectroscopy studies of GNRs which are known to have nonzero electronic bandgap due to confinement effect. GNRs of width ranging from 15 nm to 100 nm have been prepared by e-beam lithographic patterning of mechanically exfoliated graphene followed by oxygen plasma etching. Raman spectra of narrow GNRs can be characterized by upshifted G band and strong disorder-related D band originating from scattering at ribbon edges. Detailed analysis of the G, D, and 2D bands of GNRs proves that Raman spectroscopy is still a reliable tool in characterizing GNRs despite their nanometer width.

• Current Optics and Photonics
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• v.3 no.2
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• pp.150-153
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• 2019

Larynx cancer is a potentially terminal and severe type of neck and head cancer in which malignant cells start to grow and spread upwards in the larynx, or voice box. Smoking tobacco, drinking hot beverages and drinking alcohol are the main risk factors for these tumors. In this study, we aimed to develop a precise, accurate and rapid chemometrics assisted Raman spectroscopy method for diagnosis of larynx cancer in deparaffinized tissue samples. In the proposed method, samples were deparaffinized and 20 microns of each tissue were located on a coverslip. Both healthy (n = 13) and cancerous tissues (n = 13) were exposed to a Raman laser (785 nm) and excitations were recorded between wavenumbers of $50{\sim}1500cm^{-1}$. An Orthogonal Partial Least Square algorithm was applied to evaluate the Raman spectrum obtained. Sensitivity and specificity of the proposed method is high enough with the aid of Principal Component Analysis (PCA) to test the whole model. Healthy and cancerous tissues were accurately and precisely clustered. A rapid, easy and precise diagnosis algorithm was developed for larynx cancer. By this method, some useful data about differences in biomolecules of each group (phospholipids, amides, tyrosine, phenylalanine collagen etc.) was also obtained from the spectra. It is claimed that the optimized method has a great potential for clustering and separating tumor tissues from healthy ones. This novel, rapid, precise and objective diagnosis method may be an alternative for the conventional methods in literature for diagnosis of larynx cancer.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.20 no.2
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• pp.161-170
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• 2022

The effect of oxidation time on the characteristics and mechanical properties of spent nuclear fuel cladding was investigated using Raman spectroscopy, tube rupture test, and tensile test. As oxidation time increased, the Raman peak associated with the tetragonal zirconium oxide phase diminished and merged with the Raman peak associated with the monoclinic zirconium oxide phase near 333 cm^-1. Additionally, the other tetragonal zirconium oxide phase peak at 380 cm^-1 decreased after 100 d of oxidation, whereas the zirconium monoclinic oxide peak became the dominant peak. The oxidation time had no effect on the tube rupture pressure of the oxidized zirconium alloy tube. However, the yield and tensile stresses of the oxidized nuclear fuel cladding tube decreased after 100 d of oxidation. The results of the scanning electron microscopy and transmission electron microscopy were represented with the in-situ Raman analysis result for the oxide characteristics generated on the cladding of spent nuclear fuel.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.139-146
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• 2024

Surface-enhanced Raman scattering (SERS) enables the detection of various types of π-conjugated biological and chemical molecules owing to its exceptional sensitivity in obtaining unique spectra, offering nondestructive classification capabilities for target analytes. Herein, we demonstrate an innovative strategy that provides significant machine learning (ML)-enabled predictive SERS platforms through surface-engineered graphene via complementary hybridization with Au nanoparticles (NPs). The hybridized Au NPs/graphene SERS platforms showed exceptional sensitivity (10^-7 M) due to the collaborative strong correlation between the localized electromagnetic effect and the enhanced chemical bonding reactivity. The chemical and physical properties of the demonstrated SERS platform were systematically investigated using microscopy and spectroscopic analysis. Furthermore, an innovative strategy employing ML is proposed to predict various analytes based on a featured Raman spectral database. Using a customized data-preprocessing algorithm, the feature data for ML were extracted from the Raman peak characteristic information, such as intensity, position, and width, from the SERS spectrum data. Additionally, sophisticated evaluations of various types of ML classification models were conducted using k-fold cross-validation (k = 5), showing 99% prediction accuracy.

• Food Science and Industry
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• v.55 no.1
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• pp.45-57
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• 2022

This study was proceeded the analytical methods using various analytical instruments for polycyclic aromatic hydrocarbons (PAHs) in food products. Various analytical methods were developed to determine levels of PAHs including benzo[a]pyrene, benzo[a]anthracene, benzo[b]fluoranthene, and chrysene formed in various food products using gas chromatography-mass spectrometry (GC-MS), enzyme-linked immunosorbent assay (ELISA) and raman spectroscopy. Recently, the rapid on-site response for the detection of hazardous substances in food aims to develop an onsite rapid detection of a simplified technical analysis method to reduce the time and cost required for analysis of PAHs. Current PAHs detection methods have been reviewed along with new raman spectroscopy analytical method.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.615-617
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• 2005

[ $^{13}C$ ] NMR spectra were obtained for pure $CH_4$ hydrate in order to identify hydrate structure and cage occupancy of guest molecule. The NMR technique can provide both qualitative and quantitative hydrate characteristics. The moles of methane captured into pure $CH_4$ hydrate per mole of water were found to be similar to the full occupancy value. The overall results drawn from this study can be usefully applied to storage and transportation of natural gas.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.16-22
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• 2012

In this paper, we proposed a method of baseline correction for analysis of Raman spectra of platelets from Alzheimer's disease (AD) transgenic mice. Measured Raman spectra include the meaningful information and unnecessary noise which is composed of baseline and additive noise. The Raman spectrum is divided into the local region including several peaks and the spectrum of the region is modeled by curve fitting using Gaussian model. The additive noise is clearly removed from the process of replacing the original spectrum with the fitted model. The baseline correction after interpolating the local minima of the fitted model with linear, piecewise cubic Hermite and cubic spline algorithm. The baseline corrected models extract the feature with principal component analysis (PCA). The classification result of support vector machine (SVM) and maximum $a$ posteriori probability (MAP) using linear interpolation method showed the good performance about overall number of principal components, especially SVM gave the best performance which is about 97.3% true classification average rate in case of piecewise cubic Hermite algorithm and 5 principal components. In addition, it confirmed that the proposed baseline correction method compared with the previous research result could be effectively applied in the analysis of the Raman spectra of platelet.