共轴双光束LIBS检测土壤中Pb的参数优化

为了提高共轴双光束激光诱导击率光谱(DB-LIBS)检测土壤中重金属元素的检测精度,对试验装置进行了参数优化。试验时,以土壤中重金属Pb为例,首先对双光束与单光束作用效果进行对比,单束激光脉冲工作能量均为120mJ,得到双光束作用下土壤中Pb的特征光谱强度明显比单光束作用下增强了2~3倍。然后,分别设置两束激光之间的脉冲延迟时间为0、50和100ns,在每个激光脉冲延迟时间下,设置光谱采集延迟时间从2.6~5.2μs以步长为0.2μs递增,分别对样品的光谱进行采集,获取Pb元素在405.78nm处的特征光谱信息。通过对特征光谱强度和信背比的综合比较,得到最佳的激光脉冲延迟时间为50ns、光谱采集延迟时间为4.6μs,在此条件下,光谱强度的相对标准偏差为0.10。由此表明,参数优化有利于提高共轴DB-LIBS装置的检测精度。     

Laser-induced breakdown spectroscopy for the classification of wood materials using machine learning methods combined with feature selection

In this paper, we explore whether a feature selection method can improve model performance by using some classical machine learning models, artificial neural network, k-nearest neighbor,partial least squares-discrimination analysis, random forest, and support vector machine(SVM),combined with the feature selection methods, distance correlation coefficient(DCC), important weight of linear discriminant analysis(IW-LDA), and Relief-F algorithms, to discriminate eight species of wood(African rosewood, Brazilian bubinga, elm, larch, Myanmar padauk,Pterocarpus erinaceus, poplar, and sycamore) based on the laser-induced breakdown spectroscopy(LIBS) technique. The spectral data are normalized by the maximum of line intensity and principal component analysis is applied to the exploratory data analysis. The feature spectral lines are selected out based on the important weight assessed by DCC, IW-LDA,and Relief-F. All models are built by using the different number of feature lines(sorted by their important weight) as input. The relationship between the number of feature lines and the correct classification rate(CCR) of the model is analyzed. The CCRs of all models are improved by using a suitable feature selection. The highest CCR achieves(98.55...0.39)% when the SVM model is established from 86 feature lines selected by the IW-LDA method. The result demonstrates that a suitable feature selection method can improve model recognition ability and reduce modeling time in the application of wood materials classification using LIBS.     

Uranium measurements using laser-induced breakdown spectroscopy in lithium chloride-potassium chloride salt of pyroprocessing

In pyroprocessing,uranium(U) is recovered from molten LiCl-KCl salt,and,for safeguard purposes,it is important to analyze the U and Plutonium(Pu) concentrations in a timely manner.In the present work,salt samples containing U were fabricated.The laser used in the present work was an Nd:YAG laser with a wavelength of 532 nm,a laser energy on the sample of11.5 mJ,and a pulse repetition rate of 10 Hz.The plasma emission light was measured with an Echelle spectrometer.A total of 100 points on the sample surface were measured as the laser incident position was changed.The U and potassium(K) peaks in the spectrum were identified.Univariate and multivariate analyzes were conducted to determine the accuracy and limit of detection(LOD) of the laser-induced breakdown spectroscopy.     

Monitoring of the heavy-metal hyperaccumulation in vegetal tissues by X-ray radiography and by femto-second laser induced breakdown spectroscopy

This article reports on the utilization of X-ray microradiography and laser induced breakdown spectroscopy (LIBS) techniques for investigation of the metal accumulation in different part of leaf samples. The potential of the LIBS-analysis for finding the proper plant species for phytoremediation is compared with the results of microradiography measurements at the HERCULES source at ENEA, Rome (Italy) and X-ray microradiography experiments at the ELETTRA Synchrotron, Trieste (Italy).     

基于激光诱导击穿光谱技术的废旧金属分类辨识

废旧金属回收是工业中金属的重要来源之一,是发展循环经济的重要内容。废旧金属产量巨大,通常表面覆盖杂质,凹凸不平,因此对分类方法的判别能力和计算速度提出较高要求。采用激光诱导击穿光谱技术研究分析了7种废旧金属分类识别问题,包括生铝、熟铝、镁、不锈钢、锌、黄铜与红铜。为了符合现场应用条件,实验中每个样本点只激发一次建立并分析了多种分类模型,包括支持向量机(SVM)分类模型,主成分分析方法结合支持向量机(PCA-SVM)分类模型,遗传算法结合支持向量机(GA-SVM)分类模型,遗传算法选择特征光谱结合主成分分析方法和支持向量机(GA-PCA-SVM)分类模型,以及遗传算法选择特征光谱结合主成分分析方法和人工神经网络(GA-PCA-BP)分类模型。通过遗传算法选取包含丰富特征的谱段组合与支持向量机方法相结合建立GA-SVM分类模型,490组验证样本分类准确率为93.47%。为了判断该模型的鲁棒性,对一批新样品,在自研的分选系统上以传送带匀速运行的方式进行测试,获取的750组光谱测试数据,分类准确率为88.27%,证明了该分类模型具有很好的移植性和应用性。     

激光诱导击穿光谱在金属材料在线分析方面的应用进展

激光诱导击穿光谱(LIBS)由于具有无需样品制备、分析速度快、可远距离在线遥测以及可进行元素的二维表面分布和深度分析等特点,引起光谱化学分析领域研究者的广泛关注。在冶金过程工艺控制领域,金属材料的在线分析以及元素多维信息的获取,对于实时监控金属材料的生产工艺及准确实现金属材料的快速分类方面具有重要的意义。与传统的分析方法相比较,LIBS技术因其众多优势已成为金属材料实时在线分析的最佳手段之一,而且已有多种商品化的仪器推出。论文首先对LIBS在线仪器装置进行介绍,然后对近些年LIBS在熔融金属在线分析、连铸钢坯鉴别、炉渣在线分析、金属材料分类识别、金属材料镀层分析等方面的发展现状及挑战进行论述,最后对LIBS在线分析进行展望。     

液态锂与316L不锈钢焊缝的相容性初步研究

利用金相、SEM/EDS及自主研制搭建的LIBS等分析方法,研究了316L不锈钢焊缝在350℃液态锂环境中浸泡500h的静态腐蚀行为,拟为锂回路管道焊缝与液态锂的相容性研究提供一定的数据参考。实验结果显示,316L不锈钢焊缝区在液态锂中以晶间腐蚀形式被腐蚀,焊缝区的晶界腐蚀行为更严重,但没有出现明显的相变。液态锂腐蚀后焊缝区表层的Li的渗透量约是母材区的1.55倍,Li在焊缝区和母材区的渗透深度分别约为5.48μm和3.29μm,均大于焊缝区和母材区截面观察的腐蚀疏松层厚度(~5.0μm和3.0μm)。腐蚀后,碳的质量分数增加了11.03%,表面生成了碳化物,但Cr、Ni、Mo和Mn的质量分数分别减少了2.28%、1.01%、0.47%和0.16%。焊缝表层的Cr在液态锂中的溶解损耗最严重。腐蚀前后焊缝表面的维氏硬度值之比约为3.45∶1,焊缝区的力学性能下降。总之,液态锂对316L不锈钢焊缝产生腐蚀效果。相对母材区,焊缝区更易被液态锂腐蚀。     

LIBS-an efficient approach for the determination of Cr in industrial wastewater

In the present paper, LIB spectra of different water samples having varying concentration of Cr (certified reference material, CRM) have been recorded by using liquid jet (fabricated in our laboratory) configuration. Calibration curves for different atomic lines of Cr are compared and it is found that calibration curve for Cr II (283.5 nm) atomic line is the best in terms of the Limit of detection (LOD) which is found to be 30 ppm. This calibration curve has been used for quantification of Cr in wastewater collected from Cr-electroplating industry where the concentration of Cr is found to be 1500 ppm. Its removal can be planned by biological system, which is in progress.     

内标元素强度筛选法在激光诱导击穿光谱定量分析土壤标样中铁钛锶的应用

利用激光诱导击穿光谱(LIBS)技术进行光谱定量分析时,由于实验条件不稳定造成的测量结果检出限高、测量精确度低的问题一直是一个亟待解决的难题。实验选取地质矿产部化探分析质量监控站的标准土壤样品为分析样品,以波长为1064nm的Nd∶YAG激光器为激发光源,脉冲重复频率1Hz,通过激光诱导击穿光谱实验装置采集土壤样品在420~440nm范围内的等离子体光谱图。采用以内标法为基础的数据平均处理和内标元素强度筛选法(即以选定的内标元素谱线强度为参考,进行数据筛选)处理数据分别分析了待测样品中的Fe、Ti和Sr元素的含量并进行了对比,利用内标元素强度筛选法测得Fe、Ti和Sr的质量分数分别为68.4mg/g、10777μg/g和75.5μg/g,相对误差由平均法测得的10.60%、3.20%和8.57%降低为2.01%、0.21%和1.95%。说明内标元素强度筛选法处理数据可以减少数据处理量,简化数据处理过程并提高分析精度,尤其适用于激光能量输出不稳定、时间延迟较难控制的实验条件下的物质成分定量分析。     

激光诱导击穿光谱法判断汽车板表面划痕缺陷

采用激光诱导击穿光谱法对汽车板表面划痕类缺陷进行了分析和判断。通过使用激光诱导击穿光谱深度分析的方法对比了汽车板表面划痕缺陷部位与非缺陷部位的深度分布特点,并在选取的积分段内,对样品表面进行线扫描,得到表面元素的分布情况。依据缺陷部位某些元素的缺失现象,推断了该部位缺陷类型为表面划痕缺陷。判断结果与扫描电镜的检测结果相符。本文建立的方法具有分析时间短（< 30 s）、分析准确度高、无需样品前处理等特点,适用于汽车板表面划痕类缺陷的快速判别。