LIBS分析软件的研究及其宝石学应用

针对中国地质大学(武汉)珠宝学院自主研发的激光诱导离解光谱(LIBS)系统开发了其分析软件,实现了自动元素分析与用户自定义样品光谱数据库两大主要功能,大大地简化了原始的光谱分析过程.运用该软件识别和分析了天然翡翠和处理翡翠以及蓝宝石等样品的LIBS测试结果,初步证明该分析软件具有一定的实用性以及将LIBS技术应用于珠宝玉石检测与研究的可行性.     

激光诱导击穿光谱对34CrNiMo6钢中硫化锰夹杂物的评级表征

使用激光诱导击穿光谱(LIBS)对钢铁样品的扫描分析过程中,MnS夹杂物会引发S和Mn同时出现异常信号,并且信号强度与夹杂物面积之间存在线性关系.应用上述特征并参考德国标准DIN 50602中的检验要求,提出了一个用LIBS进行MnS夹杂物评级的方法.实验方法中,首先要在样品上统计得到的S、Mn元素信号强度与夹杂物面积...     

激光诱导击穿光谱技术对铁矿石中磷硫碳的快速定量表征

激光诱导击穿光谱(LIBS)技术分析P、S、C元素时,分析波长一般在165~200nm之间,为真空紫外线光谱区,定量分析存在一定难度。根据铁矿石样品特性和分析元素的特点,采用LIBS技术对压片处理(压力为20t、恒压时间10s)的铁矿石标准物质中P、S、C元素进行了定量表征。最终选定样品室环境为抽真空充氩气(Ar)、样品室真空度为50Pa、激发的剥蚀条件为20个预剥蚀、30个剥蚀,并绘制了P、S、C元素定量表征的校准曲线,线性相关系数分别为0.998、0.997、0.998,由此建立了基于LIBS技术的铁矿石成分定量表征方法。采用实验建立的表征方法对铁矿石标准物质中P、S、C进行了定量分析,标准物质GSB03-2023-2006中P、S的测试结果,标准物质GSB03-2855-2012中P、S、C的测试结果分别与认定值相符。结果表明,LIBS技术可以对铁矿石中P、S、C元素实现快速的定量表征。     

电感耦合等离子体质谱分析中金属元素的基体效应

电感耦合等离子体质谱是目前痕量元素分析领域最重要的方法,基体效应制约了该技术在复杂基体样品中的应用。文章梳理了金属元素基体效应的产生机理,包括电离抑制、空间电荷效应和协同作用等,其中,协同作用能更好地解释在不同条件下的基体干扰,受到越来越多研究者的认同。讨论了碱金属、碱土金属、铁、铜基体下痕量元素检测面临的基体干扰情况,高浓度的金属元素基体可能会导致采样锥堵塞,造成信号的不规律损失,使分析精度变差,也可能抑制部分待测元素的信号,导致测定结果偏低。总结了基体效应常用消除或校正方法,包括仪器硬件优化、仪器测试参数优化、校准方法优化及化学分离等。文章可为金属元素基体中痕量元素的准确测定提供参考借鉴。     

Spectrum-image dual-modality fusion empowered accurate and efficient classification system for traditional Chinese medicine

Traditional Chinese medicine (TCM) influences the Chinese and global medical systems, with its quality essential to its effectiveness. The origin of TCM material impacts the quality of the same TCM materials. However, the existing origin classification methods of the same TCM materials from different places mainly have two disadvantages: slow processing speed and extensive experience. To address these issues, a fast and real-time technology, laser-induced breakdown spectroscopy (LIBS), is introduced into our solution. We propose a TCM classification system that combines one-dimensional LIBS spectra with two-dimensional images. This dual-modality fusion approach represents a significant advancement in multi-view data analysis for TCM classification. As a case study, we focus on wolfberry and construct a new dataset comprising 10,800 pairs of LIBS spectrum and image data to fill the gap. To achieve superior multiple feature fusion, a two-stage fusion network (TFNet) in a coarse-to-fine way is proposed. In the first coarse fusion, the Depth Attention Fusion (DAF) module is applied to extract the key features of stacked spectrum and image. In the second fine fusion, the Line to Area (LTA) module entirely focuses on and highlights the critical spectral line features. Experimental accuracy is over 0.99 with less computation and parameters, indicating the high efficiency and accuracy of the proposed TFNet. Therefore, the classification system achieves exceptional accuracy and efficiency due to its simple sample preparation, real-time data collection and the high-accuracy lightweight network.     

Rapid identification of volatile organic compounds and their isomers in the atmosphere

Isomers are widely present in volatile organic compounds (VOCs), and it is a tremendous challenge to rapidly distinguish the isomers of VOCs in the atmosphere. In this work, laser-induced breakdown spectroscopy (LIBS) technology was developed to online distinguish VOCs and their isomers in the air. First, LIBS was used to directly detect halogenated hydrocarbons (a typical class of VOCs) and the characteristic peaks of the related halogens were observed in the LIBS spectra. Then, comparing the LIBS spectra of various samples, it was found that for VOCs with different molecular formulas, although the spectra are completely the same in elemental composition, there are still significant differences in the relative intensity of the spectral lines and other information. Finally, in light of the shortcomings of traditional LIBS technology in identifying isomers, machine learning algorithms were introduced to develop the LIBS technique to identify the isomers of atmospheric VOCs, and the recognition results were very good. It is proved that LIBS combined with machine learning algorithms is promising for online traceability of VOCs in the atmospheric environment.