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Ro, Chul-Un 한국분석과학회 1995 분석과학 Vol.8 No.4
Two types of regularization method (singular system and HMP approaches) for generating depth-concentration profiles from angle-resolved XPS data were evaluated. Both approaches showed qualitatively similar results although they employed different numerical algorithms. The application of the regularization method to simulated data demonstrates its excellent utility for the complex depth profile system. It includes the stable restoration of the depth-concentration profiles from the data with considerable random error and the self choice of smoothing parameter that is imperative for the successful application of the regularization method. The self choice of smoothing parameter is based on generalized cross-validation method which lets the data themselves choose the optimal value of the parameter.
RO, CHUL-UN,OH, KEUN-YOUNG,KIM HYEKYEONG,KIM, YONG-PYO,LEE, CHONG-BUM,KIM, KI-HYUN,KANG, CHANG-HEE,J?NOS OS?N,JOHAN DE HOOG,ANNA WOROBIEC,REN? VAN GRIEKEN 江原大學校 附設 環境硏究所 2001 環境硏究 Vol.18 No.-
A recently developed electron probe X-ray microanalysis(EPMA), called low-Z EPMA, employing an ultrathin window energy-dispersive X-ray detector, was applied to characterize aerosol particles collected at two sampling sites, namely, Kosan and 1100 Hill of Cheju Island, Korea, on a summer day in 1999. Since low-Z EPMA can provide quantitative information on the chemical composition of aerosol particles, the collected aerosol particles were classified and analyzed based on their chemical species, Many different particle types were identified, such as marine-originated, carbonaceous, soil-derived, and anthropogenic particles. Marine-originated particles, such as NaNO_(3-) and Na₂SO_(4-)containing particles, are very frequently encountered in the two samples. In this study, it was directly proven that the observed nitrate particles were from sea salts. In addition, two types of nitrate particles from sea salts were observed, with and without Mg. The sodium nitrate particles without Mg were believed to be collected as crystalline form, either with the sodium nitrate particles being fractionally recrystallized within evaporating seawater drops or with recrystallized sodium chloride particles having reacted with gaseous nitrogen species in the air to form the crystalline sodium nitrate particles. The other seemed to be collected as seawater drops, where the atmospheric reaction had occurred in the droplets, and thus sodium as well as magnesium nitrates were observed. Carbonaceous particles are the most abundant in the samples at both sites. From this study, it was found that about three-quarters of the carbonaceous particles in the samples were biogenic, which partially explains a previously reported observation of a large concentration of organic carbon particles as compared to elemental carbon. Various soil-derived particles were also observed. In addition to aluminosilicate- and iron oxide-containing particles, which are ubiquitous components in soil-derived particles, CaCO_(3-) and Cr-containing particles were also frequently encountered.
Application of Regularization Method to Angle - resolved XPS Data
노철언(Chul-Un Ro) 한국진공학회(ASCT) 1996 Applied Science and Convergence Technology Vol.5 No.2
각분해 X-선광전자분광법 데이터로부터 화학종의 깊이분포에 대한 정보를 얻기 위한 두가지 종류의 regularization 방법 (singular system과 HMP 방법)을 연구하였다. 두 방법은 매우 다른 알고리즘을 채택하고 있지만 정성적으로 유사한 결과를 보였다. 시뮬레이션을 통하여 ARXPS 데이터에 regularization 방법을 적용 하였을 때 복잡한 형태의 깊이분포를 가진 시료에 대하여 유용함을 알 수 있었다. 이방법으로부터 상당한 양의 실험오차를 가지고 있는 데이터로부터 의미 있는 깊이분포를 얻을 수 있었다. generalized cross-validation 방법을 이용하여 ARXPS 데이터로부터 regularization 방법에서 중요한 변수인 smoothing parameter 값의 최적치를 자동으로 구하도록 하였다. Two types of regularization method (singular system and HMP approaches) for generating depth-concentration profiles from angle-resolved XPS data were evaluated. Both approaches showed qualitatively similar results although they employed different numerical algorithms. The application of the regularization method to simulated data demonstrates its excellent utility for the complex depth profile system. It includes the stable restoration of the depth-concentration profiles from the data with considerable random error and the self choice of smoothing parameter that is imperative for the successful application of the regularization method. The self choice of smoothing parameter is based on generalized cross-validation method which lets the data themselves choose the optimal value of the parameter.