Quantitative energy-dispersive X-ray fluorescence analysis for unknown samples using full-spectrum least-squares regression

The full-spectrum least-squares(FSLS) method is introduced to perform quantitative energy-dispersive X-ray fluorescence analysis for unknown solid samples.Based on the conventional least-squares principle, this spectrum evaluation method is able to obtain the background-corrected and interference-free net peaks, which is significant for quantization analyses. A variety of analytical parameters and functions to describe the features of the fluorescence spectra of pure elements are used and established, such as the mass absorption coefficient, the Gi factor, and fundamental fluorescence formulas. The FSLS iterative program was compiled in the C language. The content of each component should reach the convergence criterion at the end of the calculations. After a basic theory analysis and experimental preparation, 13 national standard soil samples were detected using a spectrometer to test the feasibility of using the algorithm. The results show that the calculated contents of Ti, Fe, Ni, Cu, and Zn have the same changing tendency as the corresponding standard content in the 13 reference samples. Accuracies of 0.35% and 14.03% are obtained, respectively, for Fe and Ti, whose standard concentrations are 8.82% and 0.578%, respectively. However, the calculated results of trace elements (only tens of lg/g) deviate from the standard values. This may be because of measurement accuracy and mutual effects between the elements.

A TOTAL CONTENT X—RAY FLUORESCENCE METHOD FOR COPPER PROSPECTING

A new method is proposed to prospect copper deposits with portable XRF analyzer. The method is based on the close relation between Cu and the chalcophile elements or some other elements in the geochemical anomalies of a Cu deposit. Applications of the technique in Northeast China are presented.

Application of a Portable XRF Spectrometer for <i>In-Situ</i>and Nondestructive Investigation of Pigments in Two 15th Century Icons

A simple portable X-Ray Fluorescence (XRF) spectrometer was successfully used for in-situ and nondestructive identification of the painting materials in two 15th century icons from the Onufri Museum in Beart, Albania. The spectrometer is based on a low power X-ray tube, a thermoelectrically cooled Si PIN detector and the spectrum acquisition system. It was assembled and adjusted at our laboratory for the investigation of the icons. A small number of pigments were clearly identified by X-Ray Fluorescence (XRF) measurements in both icons. This includes Lead white for the white color, gold and yellow ochre for the yellow color, red lead, cinnabar and red ochre for the red color, as well as cooper based pigments for the green color. At the same time, the investigation raised some new questions that need further investigations by the use of additional analytical techniques. The results show that in both icons are used similar pigments, which are in accordance with the Byzantine icon painting tradition.

A New Discovery of Ag-Pb-Zn Mineralization via Modern Portable Analytical Technology and Stream Sediment Data Processing Methods in Dajiacuo Area, Western Tibet(China)

Tibet, which is rich in mineral resources, is a treasure trove for geological explorers. However, prospecting work has been slow, especially in the western part, due to the precipitous terrain, changeable climate and low access. Hence, modern advanced field analytical technology and effective data processing methods play significant roles in rapid and efficient exploration in Tibet. In this paper, spectrum-area fractal modeling and portable X-ray fluorescence analysis(pXRFA) were used to identify and verify geochemical anomalies associated with Ag-Pb-Zn mineralization based on a stream-sediment dataset of 39 elements in the Dajiacuo-Xurucuo region of western Tibet. First, staged factor analysis(SFA) was used to obtain the Ag-Pb-Zn-Cd geochemical assemblage. Second, the first-factor pattern obtained using SFA was dissociated by a spectrum-area(S-A) fractal model and a digital elevation model(DEM)-based geochemical model(DGM) was constructed. Finally, the sections of Ag, Cd, Pb, and Zn were obtained using pXRFA. The results show that Ag-Pb-Zn-Cd enrichment zones were mostly located around the contact belt of volcanic rocks and intrusions, or along SE-NW trending faults. Considering the variable terrain and catchment basin, the extension of long axes of Ag-Pb-Zn-Cd anomalies into higher elevation areas that are favorable for Ag-Pb-Zn mineralization should be investigated. Anomaly maps created with the aid of a DGM show promising potential for mineralization in the Dajiacuo-Xurucuo region, and abundant Ag-Pb-Zn mineralization was identified with the assistance of pXRFA in the source areas for the geochemical anomalies in the Dajiacuo. We conclude that SFA and the S-A fractal model constitute a valid tool to identify or verify geochemical anomalies in areas of low-density stream-sediment sampling. The pXRFA can accurately determine the source of geochemical anomalies and improve anomaly verification efficiency.

古代玻璃样品表面因素对便携式X射线荧光定量分析的影响

便携式X射线荧光光谱分析技术(PXRF)是一种表面分析技术,样品表面状况对定量分析结果有很大的影响。本文采用PXRF对新疆、广西、江苏等地出土的Na2O-CaO-SiO2,K2O-SiO2和PbO-BaO-SiO2体系古代玻璃的风化表面与内部进行了定量分析,指出了主要助熔剂的含量在不同部位的变化。另外,还研究了样品与参考平面之间的距离以及弧形表面对样品定量分析的影响,利用归一化方法和校正因子法,对测试结果进行了校正,并对校正后的结果进行了比较。结果表明归一化方法及校正因子法对于非理想状态表面样品的校正结果几乎相同,但实际测量中,在满足一定的条件下,归一化方法更适合古代文物的定量分析。研究结果对于古代玉器的PXRF定量分析也具有一定的指导意义。

FPXRF定量测定空气颗粒物中的重金属

提出采用螯合萃取法制备空气滤膜标样的方案，并制备了一批确定含量的不同梯度浓度的Mn、Ni、Cu、Cd、Pb混合的空气滤膜标样对手持式荧光分析仪进行标定，再运用标定的手持式荧光分析仪对采样滤膜进行现场快速重金属元素定量测量，实时获取了大气颗粒物中重金属元素的质量浓度值，并对其中的异常值进行分析，探究不同时间段重金属元素浓度的变化趋势。结果表明：在29d的采样监测中，Mn质量浓度范围为0．48～9．18μg／m^3，Ni为1．15～3．06μg／m^3，Cu为0．38～5．23μg／m^3，Cd为0．06～6．31μg／m^3，Pb为2．52～8．64μg／m^3；经此空气滤膜标样标定的手持式x荧光分析仪能够对空气颗粒物中重金属的浓度进行现场测量，测量结果显示：空气颗粒物中重金属的浓度在夜间最大，这与晚问气温相对较低、不利于污染物扩散及城市空气中的汽车排放废气、空气污染都在傍晚达到较高点的实际情况较为相符，此外空气颗粒物中重金属的浓度也与天气和周围环境的联系较为紧密。

手持式X射线荧光光谱仪在牙科非贵金属无损检测中的应用

采用手持式X射线荧光光谱(PXRF)法对两种口腔常用标准品和4种常用牙科非贵金属的主要元素进行无损检测,并且与原子吸收光谱(AAS)法进行比较。对YSBC18604–08钛铁标准品的主要元素Fe,Ti,Al,Mn和YSBC15343–2008不锈钢标准品的主要元素Fe,Cr,Mn以及4种已知元素含量的牙科钴铬支架(Co,Cr,Mo,W)、镍铬合金金属桥(Ni,Cr,Mo)、齿科钴铬烤瓷合金(Co,Cr,Mo,W)、镍基铸造合金(Ni,Cr,Cu,Sn)进行分析检测。对含量较高(>20%)的元素,其测定结果的相对标准偏差均小于0.8%(n=9),含量小于10%的元素其测定结果的相对标准偏差均小于4%(n=9)。不锈钢标准品YSBC15343–2008和钛铁标准品YSBC18604–08各元素测定结果与标称值基本一致,相对偏差在0.025%~2.54%范围内。PXRF法和AAS法对原材料中主要元素的测定结果基本一致,均与标称含量相符合。PXRF法对成品及原材料的分析不需要破坏试样,分析速度快,准确度高,重现性好,能同时检测多种元素,满足日常分析要求。

Proximal sensor-enhanced soil mapping in complex soil-landscape areas of Brazil

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.