The accuracy(repeatability and reproducibility) of the iron content analysis of galvanized coating using an X-ray fluorescence spectrometer with an L-spectrum is not better than that of flame atomic absorption spectro...The accuracy(repeatability and reproducibility) of the iron content analysis of galvanized coating using an X-ray fluorescence spectrometer with an L-spectrum is not better than that of flame atomic absorption spectrometry, sometimes it exceeds the quality control limit.Influences, such as current, voltage, equipment(internal circulating water, 10%CH4+90%Ar, and vacuum) checking, instrument monitoring, sample cleaning, and oper-ators, were investigated by means of 6-sigma and lean operations to improve accuracy.展开更多
Large areas of muddy sediments on the coastal shelves of China provide important samples for studying climate and ecological changes. Analysis of a large number of such samples, which is essential for systematic study...Large areas of muddy sediments on the coastal shelves of China provide important samples for studying climate and ecological changes. Analysis of a large number of such samples, which is essential for systematic study on environmental information recorded in mud areas because of complicated sedimentary environment and variable sedimentary rate, requires a fast and economical method. In this study, we investigated the potential of X-ray fluorescence core scanner （XRFS）, a fast analytical instrument for measuring the elemental concentrations of muddy sediments, and observed a significant correlation between the element concentrations of muddy sediments determined by regular X-ray fluorescence spectrometer （XRF） and XRFS, respectively. The correlations are mainly determined by excitation energy of elements, but also influenced by solubility of element ions. Furthermore, we found a striking link between A1 concentrations and marine-originated organic carbon （MOC）, a proxy of marine primary productivity. This indicates that MOC is partly controlled by sedimentary characteristics. Therefore, XRFS method has a good potential in fast analysis of a large number of muddy sediment samples, and it can also be used to calibrate MOC in ecological study of coastal seas.展开更多
本文利用理学ZSX Primus Ⅱ X射线荧光光谱仪的无标样线性E-Z扫描方法,匹配软件自带的数据库,用SQX定性分析计算结果,应用到一般盲样矿石的分析中,结果表明,EZ扫描能为常规的化学分析方法制订方案起了很好的指导作用,从而缩短了分析周期...本文利用理学ZSX Primus Ⅱ X射线荧光光谱仪的无标样线性E-Z扫描方法,匹配软件自带的数据库,用SQX定性分析计算结果,应用到一般盲样矿石的分析中,结果表明,EZ扫描能为常规的化学分析方法制订方案起了很好的指导作用,从而缩短了分析周期,提高了检测效率。展开更多
Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging ...Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest(RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models(DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO_(2) contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.展开更多
文摘The accuracy(repeatability and reproducibility) of the iron content analysis of galvanized coating using an X-ray fluorescence spectrometer with an L-spectrum is not better than that of flame atomic absorption spectrometry, sometimes it exceeds the quality control limit.Influences, such as current, voltage, equipment(internal circulating water, 10%CH4+90%Ar, and vacuum) checking, instrument monitoring, sample cleaning, and oper-ators, were investigated by means of 6-sigma and lean operations to improve accuracy.
基金supported by the National Basic Research Program of China(2010CB428902)National Natural Science Foundation of China(40876088)
文摘Large areas of muddy sediments on the coastal shelves of China provide important samples for studying climate and ecological changes. Analysis of a large number of such samples, which is essential for systematic study on environmental information recorded in mud areas because of complicated sedimentary environment and variable sedimentary rate, requires a fast and economical method. In this study, we investigated the potential of X-ray fluorescence core scanner （XRFS）, a fast analytical instrument for measuring the elemental concentrations of muddy sediments, and observed a significant correlation between the element concentrations of muddy sediments determined by regular X-ray fluorescence spectrometer （XRF） and XRFS, respectively. The correlations are mainly determined by excitation energy of elements, but also influenced by solubility of element ions. Furthermore, we found a striking link between A1 concentrations and marine-originated organic carbon （MOC）, a proxy of marine primary productivity. This indicates that MOC is partly controlled by sedimentary characteristics. Therefore, XRFS method has a good potential in fast analysis of a large number of muddy sediment samples, and it can also be used to calibrate MOC in ecological study of coastal seas.
基金BL Allen Endowment in Pedology at Texas Tech University,USAthe Brazilian funding agencies National Council for Scientific and Technological Development (CNPq) (Nos.301930/2019-8 and 306389/2019-7)+1 种基金the Coordination for the Improvement of Higher Education Personnel (CAPES),Brazil (No.590-2014)Research Support Foundation of the State of Minas Gerais (FAPEMIG),Brazil (No.PPM 00305-17) for the financial support provided。
文摘Portable X-ray fluorescence(pXRF) spectrometry and magnetic susceptibility(MS) via magnetometer have been increasingly used with terrain variables for digital soil mapping. However, this methodology is still emerging in many countries with tropical soils. The objective of this study was to use proximal soil sensor data associated with terrain variables at varying spatial resolutions to predict soil classes using the Random Forest(RF) algorithm. The study was conducted on a 316-ha area featuring highly variable soil classes and complex soil-landscape relationships in Minas Gerais State, Brazil. The overall accuracy and Kappa index were evaluated using soils that were classified at 118 sites, with 90 being used for modeling and 28 for validation. Digital elevation models(DEMs) were created at 5-, 10-, 20-, and 30-m resolutions using contour lines from two sources. The resulting DEMs were processed to generate 12 terrain variables. Total Fe, Ti, and SiO_(2) contents were obtained using pXRF, with MS determined via a magnetometer. Soil class prediction was performed using the RF algorithm. The quality of the soil maps improved when using only the five most important covariates and combining proximal sensor data with terrain variables at different spatial resolutions. The finest spatial resolution did not always provide the most accurate maps. The high soil complexity in the area prevented highly accurate predictions. The most important variables influencing the soil mapping were MS, Fe, and Ti. Proximal sensor data associated with terrain information were successfully used to map Brazilian soils at variable spatial resolutions.
