Junhang Dong, Zhenli Zhu, Lujie Li, Pengju Xing, Shuyang Li, Lei Ouyang, Xing Liu, Wei Guo, Hongtao Zheng and Rong Qian
Single particle inductively coupled plasma mass spectrometry (spICP-MS) has become a powerful tool for the simultaneous characterization of the size, elemental composition and concentration of nanoparticles (NPs). However, the conventional pneumatic nebulization (PN) sampling system used in spICP-MS suffers from low transport efficiency (1–5%), limiting its effectiveness in analyzing environmentally relevant samples with low NP concentrations. To address this limitation, we evaluated a self-designed high-efficiency miniaturized ultrasonic nebulization (MUN) sampling system for spICP-MS analysis. This novel sampling system achieves an exceptionally high transport efficiency of approximately 80% for silver (Ag) NPs. Remarkably, this high transport efficiency is maintained across a sample uptake rate range of 10–25 μL min−1, outperforming other reported highly efficient nebulizers where TE significantly decreased with the increase in sampling rate. The effectiveness and reliability of the MUN system were further demonstrated by analyzing standard Ag NPs of 60 nm and 100 nm, confirming the accurate characterization of particle size distribution. Overall, our MUN-spICP-MS offers a cost-effective and highly efficient method for characterizing NPs, which is of great significance for the NP characterization in natural environmental samples with low particle sizes and concentrations.
{"title":"Evaluation of miniaturized ultrasonic nebulization for high-efficiency sampling in characterization of silver nanoparticles by single particle inductively coupled plasma mass spectrometry†","authors":"Junhang Dong, Zhenli Zhu, Lujie Li, Pengju Xing, Shuyang Li, Lei Ouyang, Xing Liu, Wei Guo, Hongtao Zheng and Rong Qian","doi":"10.1039/D4JA00320A","DOIUrl":"https://doi.org/10.1039/D4JA00320A","url":null,"abstract":"<p >Single particle inductively coupled plasma mass spectrometry (spICP-MS) has become a powerful tool for the simultaneous characterization of the size, elemental composition and concentration of nanoparticles (NPs). However, the conventional pneumatic nebulization (PN) sampling system used in spICP-MS suffers from low transport efficiency (1–5%), limiting its effectiveness in analyzing environmentally relevant samples with low NP concentrations. To address this limitation, we evaluated a self-designed high-efficiency miniaturized ultrasonic nebulization (MUN) sampling system for spICP-MS analysis. This novel sampling system achieves an exceptionally high transport efficiency of approximately 80% for silver (Ag) NPs. Remarkably, this high transport efficiency is maintained across a sample uptake rate range of 10–25 μL min<small><sup>−1</sup></small>, outperforming other reported highly efficient nebulizers where TE significantly decreased with the increase in sampling rate. The effectiveness and reliability of the MUN system were further demonstrated by analyzing standard Ag NPs of 60 nm and 100 nm, confirming the accurate characterization of particle size distribution. Overall, our MUN-spICP-MS offers a cost-effective and highly efficient method for characterizing NPs, which is of great significance for the NP characterization in natural environmental samples with low particle sizes and concentrations.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Eduardo Bolea-Fernandez, Robert Clough, Andy Fisher, Bridget Gibson and Ben Russell
This update covers the literature published between approximately June 2023 and April 2024 and is the latest part of a series of annual reviews. It is designed to provide the reader with an overview of the current state of the art with respect to the atomic spectrometric analysis of various metals, chemicals and materials. Data processing appears to be the hottest topic in many of the areas. This is especially true for LIBS and (TOF)-SIMS, where huge amounts of data can be acquired. Methods have been used to decrease the dimensions of the data whilst still retaining the most important information. This can then be input into a machine-learning algorithm so that the provenance of a sample, the sample type, or, in the case of TOF-SIMS data, a clear characterisation of the surface of the sample can be obtained while using less computing power and less processing time. Although these algorithms have been used for some years, their use is expanding into new areas. Another development is the combination of complementary techniques on the same instrument platform. This enables data from the two techniques to be obtained simultaneously and from the same spot on the sample. With regard to the different analytical techniques used, LIBS is continuing to increase in popularity, bolstering its reputation as being the rising superstar of the analytical world.
{"title":"Atomic spectrometry update: review of advances in the analysis of metals, chemicals and materials","authors":"Eduardo Bolea-Fernandez, Robert Clough, Andy Fisher, Bridget Gibson and Ben Russell","doi":"10.1039/D4JA90052A","DOIUrl":"https://doi.org/10.1039/D4JA90052A","url":null,"abstract":"<p >This update covers the literature published between approximately June 2023 and April 2024 and is the latest part of a series of annual reviews. It is designed to provide the reader with an overview of the current state of the art with respect to the atomic spectrometric analysis of various metals, chemicals and materials. Data processing appears to be the hottest topic in many of the areas. This is especially true for LIBS and (TOF)-SIMS, where huge amounts of data can be acquired. Methods have been used to decrease the dimensions of the data whilst still retaining the most important information. This can then be input into a machine-learning algorithm so that the provenance of a sample, the sample type, or, in the case of TOF-SIMS data, a clear characterisation of the surface of the sample can be obtained while using less computing power and less processing time. Although these algorithms have been used for some years, their use is expanding into new areas. Another development is the combination of complementary techniques on the same instrument platform. This enables data from the two techniques to be obtained simultaneously and from the same spot on the sample. With regard to the different analytical techniques used, LIBS is continuing to increase in popularity, bolstering its reputation as being the rising superstar of the analytical world.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alex Wangeci, Maria Knadel, Olga De Pascale, Mogens H. Greve and Giorgio S. Senesi
Laser-induced breakdown spectroscopy (LIBS) has contributed to the advanced and rapid determination of soil properties including soil organic carbon (SOC) and texture. Recent developments of commercial handheld LIBS (hLIBS) have allowed the use of the technique directly in the field. However, to date, the performance of hLIBS on different types of soils covering wide geographical distributions has not been evaluated. In this study, a total of 305 soil samples covering a continental scale were used to assess the repeatability and reproducibility of LIBS data acquired using a commercially available hLIBS instrument. Furthermore, the performance of the prediction models for SOC and texture was evaluated based on the prediction error. The repeatability and reproducibility of LIBS data were evaluated based on the relative standard deviation (RSD) for measurements performed under similar and different environmental conditions (temperature and humidity). First, the RSD of the signal ratios and the predicted values for soil properties under investigation were calculated. Then, the prediction accuracy of the various soil properties was compared based on the standardized root mean error of prediction (SRMSEP) and the ratio of performance to interquartile distance (RPIQ). The signal ratios assessed using the C, Si, Ca, and K LIBS emission lines achieved a repeatability of 4–9% and a reproducibility of 7–10%, whereas the repeatability and reproducibility for predicting SOC and texture were <25%. The prediction of sand content exhibited the lowest error (SRMSEP = 0.14) followed by clay and silt (SRMSEP = 0.15), and then SOC (SRMSEP = 0.16). The results of this work underscore the promising potential of hLIBS for large-scale SOC and texture determination, with the opportunity to enhance the prediction accuracy by integrating soil mineralogy information for soil classification before applying the modeling process.
{"title":"Assessing the performance of handheld LIBS for predicting soil organic carbon and texture in European soils†","authors":"Alex Wangeci, Maria Knadel, Olga De Pascale, Mogens H. Greve and Giorgio S. Senesi","doi":"10.1039/D4JA00292J","DOIUrl":"https://doi.org/10.1039/D4JA00292J","url":null,"abstract":"<p >Laser-induced breakdown spectroscopy (LIBS) has contributed to the advanced and rapid determination of soil properties including soil organic carbon (SOC) and texture. Recent developments of commercial handheld LIBS (hLIBS) have allowed the use of the technique directly in the field. However, to date, the performance of hLIBS on different types of soils covering wide geographical distributions has not been evaluated. In this study, a total of 305 soil samples covering a continental scale were used to assess the repeatability and reproducibility of LIBS data acquired using a commercially available hLIBS instrument. Furthermore, the performance of the prediction models for SOC and texture was evaluated based on the prediction error. The repeatability and reproducibility of LIBS data were evaluated based on the relative standard deviation (RSD) for measurements performed under similar and different environmental conditions (temperature and humidity). First, the RSD of the signal ratios and the predicted values for soil properties under investigation were calculated. Then, the prediction accuracy of the various soil properties was compared based on the standardized root mean error of prediction (SRMSEP) and the ratio of performance to interquartile distance (RPIQ). The signal ratios assessed using the C, Si, Ca, and K LIBS emission lines achieved a repeatability of 4–9% and a reproducibility of 7–10%, whereas the repeatability and reproducibility for predicting SOC and texture were <25%. The prediction of sand content exhibited the lowest error (SRMSEP = 0.14) followed by clay and silt (SRMSEP = 0.15), and then SOC (SRMSEP = 0.16). The results of this work underscore the promising potential of hLIBS for large-scale SOC and texture determination, with the opportunity to enhance the prediction accuracy by integrating soil mineralogy information for soil classification before applying the modeling process.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/ja/d4ja00292j?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaomei Lin, Jiangfei Yang, Yutao Huang, Jingjun Lin and Changjin Che
Metal Additive Manufacturing (AM) holds significant importance in advancing intelligent manufacturing and sustainable development. However, due to the unique manufacturing process of AM, defect detection in AM components has always been a challenging issue. This study employed Laser-Induced Breakdown Spectroscopy (LIBS) technology to capture spectral information and utilized a high-speed camera to record plasma images, comprehensively extracting pertinent details from each laser event. LIBS spectral scores were obtained via principal component analysis (PCA) and plasma image features were extracted to generate a bimodal fusion descriptor. This descriptor was employed to enhance the detection capability of three common surface defects in metal AM, specifically holes, cracks and bulges. Building on this foundation, a mid-level data fusion technique was employed to integrate the scores of LIBS spectra derived from PCA with seven features extracted from plasma images, resulting in the development of a bimodal fusion approach. Subsequently, the distribution of spectral data, plasma image features and bimodal fusion descriptors was discussed. Finally, three models, namely Random Forest (RF), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA), were used to evaluate the recognition accuracy of component defects. Additionally, the application scenarios of these three different models in spectral data, plasma image features and bimodal fusion descriptors were compared. The results indicate that the LDA model, utilizing bimodal fusion descriptors, yields the most effective classification. For samples #1–#100, the accuracy increased from 99.08% and 97.94% for spectral data and plasma image features to 99.92% for fusion data. Similarly, for samples #101–#120, the accuracy increases from 97.19% and 96.21% for spectral data and plasma image features to 97.34% for fusion data. This method improves the recognition of different defects of metal AM components. This study represents a first attempt to enhance the capability of LIBS in distinguishing various surface defects of metal AM components by inputting laser plasma image data and spectral information to generate statistical descriptors. The bimodal fusion approach offers an efficient method for detecting surface defects of metal AM components, characterized by low data complexity.
金属增材制造(AM)在推进智能制造和可持续发展方面具有重要意义。然而,由于 AM 制造工艺的特殊性,AM 部件的缺陷检测一直是一个具有挑战性的问题。本研究采用激光诱导击穿光谱(LIBS)技术捕捉光谱信息,并利用高速相机记录等离子体图像,全面提取每个激光事件的相关细节。通过主成分分析 (PCA) 获得 LIBS 光谱分数,并提取等离子体图像特征,生成双峰融合描述符。该描述符用于增强金属 AM 中三种常见表面缺陷的检测能力,特别是孔、裂纹和凸起。在此基础上,采用了中层数据融合技术,将 PCA 得出的 LIBS 光谱得分与等离子图像提取的七个特征进行整合,从而开发出一种双模融合方法。随后,讨论了光谱数据、等离子图像特征和双模融合描述符的分布。最后,使用随机森林(RF)、支持向量机(SVM)和线性判别分析(LDA)这三种模型来评估组件缺陷的识别精度。此外,还比较了这三种不同模型在光谱数据、等离子图像特征和双模融合描述符中的应用场景。结果表明,利用双模融合描述符的 LDA 模型能产生最有效的分类。对于 #1-#100 样品,准确率从光谱数据和等离子图像特征的 99.08% 和 97.94% 提高到融合数据的 99.92%。同样,对于 #101-#120 样品,准确率从光谱数据和等离子图像特征的 97.19% 和 96.21% 提高到融合数据的 97.34%。这种方法提高了对金属 AM 组件不同缺陷的识别率。这项研究是首次尝试通过输入激光等离子图像数据和光谱信息来生成统计描述符,从而增强激光等离子体分析仪在区分金属 AM 组件各种表面缺陷方面的能力。双模态融合方法为检测金属 AM 组件的表面缺陷提供了一种高效方法,其特点是数据复杂度低。
{"title":"Research on a bimodal fusion detection method for surface defects of metal AM components based on LIBS","authors":"Xiaomei Lin, Jiangfei Yang, Yutao Huang, Jingjun Lin and Changjin Che","doi":"10.1039/D4JA00159A","DOIUrl":"https://doi.org/10.1039/D4JA00159A","url":null,"abstract":"<p >Metal Additive Manufacturing (AM) holds significant importance in advancing intelligent manufacturing and sustainable development. However, due to the unique manufacturing process of AM, defect detection in AM components has always been a challenging issue. This study employed Laser-Induced Breakdown Spectroscopy (LIBS) technology to capture spectral information and utilized a high-speed camera to record plasma images, comprehensively extracting pertinent details from each laser event. LIBS spectral scores were obtained <em>via</em> principal component analysis (PCA) and plasma image features were extracted to generate a bimodal fusion descriptor. This descriptor was employed to enhance the detection capability of three common surface defects in metal AM, specifically holes, cracks and bulges. Building on this foundation, a mid-level data fusion technique was employed to integrate the scores of LIBS spectra derived from PCA with seven features extracted from plasma images, resulting in the development of a bimodal fusion approach. Subsequently, the distribution of spectral data, plasma image features and bimodal fusion descriptors was discussed. Finally, three models, namely Random Forest (RF), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA), were used to evaluate the recognition accuracy of component defects. Additionally, the application scenarios of these three different models in spectral data, plasma image features and bimodal fusion descriptors were compared. The results indicate that the LDA model, utilizing bimodal fusion descriptors, yields the most effective classification. For samples #1–#100, the accuracy increased from 99.08% and 97.94% for spectral data and plasma image features to 99.92% for fusion data. Similarly, for samples #101–#120, the accuracy increases from 97.19% and 96.21% for spectral data and plasma image features to 97.34% for fusion data. This method improves the recognition of different defects of metal AM components. This study represents a first attempt to enhance the capability of LIBS in distinguishing various surface defects of metal AM components by inputting laser plasma image data and spectral information to generate statistical descriptors. The bimodal fusion approach offers an efficient method for detecting surface defects of metal AM components, characterized by low data complexity.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ji-Hao Zhu, Feng-You Chu, Feng Liang, Xian-Ying Luo, Qiang Liu, Quan-Hui Xu, Wei Yu, Yong-Chun Li, Jiang-Gu Lu, Yun-Xiu Li, Yan-Hui Dong, Huai-Ming Li, Jun Zhao and Cai Zhang
The determination of rare earth elements (REEs) in seawater, especially marine sediment porewater and open-ocean seawater, is challenging because of their ultra-trace concentrations (ng L−1 to pg L−1) and the high salinity of the matrix (approximately 35‰ NaCl), which limits their application in marine science. Herein, we developed an online method for accurate analysis of ultra-trace REEs in seawater using a traditional Q-ICP-MS. The key aspects were: (i) high sensitivity detection in standard mode with no collision/reaction cell functioned, (ii) online automated matrix removal and preconcentration using a commercially available seaFAST system, (iii) use of membrane desolvation to enhance the sensitivity and limit the interferences of LREE oxides on HREEs, and (iv) monitoring and correction of variations in REE signal intensities caused by instrument drift using standard–samples–standard bracketing and an indium internal standard for normalization. The detection limits (0.1–8.0 pg L−1) and procedural blank values (<3 pg L−1 except for La, Ce, and Nd) of this method were low enough for accurate determination of REEs in seawater, even for REE concentrations at tens of picograms per liter level. The good accuracy and long-term precision (30 h, average: 3.5%, 1σ RSD, n = 10) were achieved for all the REEs as verified using certified seawater reference standards NASS-7 and CASS-6, and a 10 ng L−1 artificial seawater standard, respectively. Each run required only approximately 8 mL of sample and 12 min for the measurement, which are suitable values for practical application. The developed method was used to analyze various natural seawater samples, which demonstrated its effectiveness for exploring subtle changes in REE concentrations, fractionation patterns and anomalies in different marine environments.
{"title":"Accurate determination of ultra-trace REEs in seawater using a membrane desolvation Q-ICP-MS coupled with an online automatic separation system†","authors":"Ji-Hao Zhu, Feng-You Chu, Feng Liang, Xian-Ying Luo, Qiang Liu, Quan-Hui Xu, Wei Yu, Yong-Chun Li, Jiang-Gu Lu, Yun-Xiu Li, Yan-Hui Dong, Huai-Ming Li, Jun Zhao and Cai Zhang","doi":"10.1039/D4JA00240G","DOIUrl":"https://doi.org/10.1039/D4JA00240G","url":null,"abstract":"<p >The determination of rare earth elements (REEs) in seawater, especially marine sediment porewater and open-ocean seawater, is challenging because of their ultra-trace concentrations (ng L<small><sup>−1</sup></small> to pg L<small><sup>−1</sup></small>) and the high salinity of the matrix (approximately 35‰ NaCl), which limits their application in marine science. Herein, we developed an online method for accurate analysis of ultra-trace REEs in seawater using a traditional Q-ICP-MS. The key aspects were: (i) high sensitivity detection in standard mode with no collision/reaction cell functioned, (ii) online automated matrix removal and preconcentration using a commercially available seaFAST system, (iii) use of membrane desolvation to enhance the sensitivity and limit the interferences of LREE oxides on HREEs, and (iv) monitoring and correction of variations in REE signal intensities caused by instrument drift using standard–samples–standard bracketing and an indium internal standard for normalization. The detection limits (0.1–8.0 pg L<small><sup>−1</sup></small>) and procedural blank values (<3 pg L<small><sup>−1</sup></small> except for La, Ce, and Nd) of this method were low enough for accurate determination of REEs in seawater, even for REE concentrations at tens of picograms per liter level. The good accuracy and long-term precision (30 h, average: 3.5%, 1<em>σ</em> RSD, <em>n</em> = 10) were achieved for all the REEs as verified using certified seawater reference standards NASS-7 and CASS-6, and a 10 ng L<small><sup>−1</sup></small> artificial seawater standard, respectively. Each run required only approximately 8 mL of sample and 12 min for the measurement, which are suitable values for practical application. The developed method was used to analyze various natural seawater samples, which demonstrated its effectiveness for exploring subtle changes in REE concentrations, fractionation patterns and anomalies in different marine environments.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guanhong Zhu, Zhenmin Ge, Le Zhang, Gangjian Wei and Jinlong Ma
Fe and Mg isotopes have increasingly served as combined proxies for geological processes. Fe and Mg isotope determination requires consuming different splits of samples and multi-column chromatographic purification to obtain pure Mg and Fe fractions in conventional chemical procedures, which is time-consuming and not suitable for rare and valuable samples. This study presents a novel and efficient chromatographic procedure to purify both Fe and Mg from geological matrices, using a single column loaded with AGMP-50 resin, followed by precise measurements of Fe and Mg isotopes by multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). In our experiment, the Fe fraction was first collected using 7 mL of a mixture of 0.2 M HCl and 0.5 M HF, and then the Mg fraction was collected using 9 mL of 1.3 M HCl. This procedure is suitable for processing different types of rock samples and enabling an Fe recovery of >98% and full recovery of Mg, with effective removal of matrix elements such as Al, Ti, Na, K, Ca, and other trace elements. Using this method, the Fe and Mg isotopic compositions of various geological reference materials were reported. All of the Fe and Mg isotopic analytical results were in agreement with the reported data within analytical uncertainties, verifying that the method established here is robust and reproducible. Thus, this procedure will serve as a great option for obtaining both Fe and Mg isotopic compositions of geological samples and tracing geochemical or astrochemical processes in the future.
铁和镁同位素越来越多地成为地质过程的综合代用指标。在传统化学方法中,铁和镁同位素的测定需要对样品进行不同的分割和多柱色谱纯化,以获得纯净的镁和铁馏分,这不仅耗时,而且不适合稀有珍贵的样品。本研究提出了一种新颖高效的色谱程序,利用装有 AGMP-50 树脂的单柱从地质基质中提纯铁和镁,然后利用多收集器电感耦合等离子体质谱法(MC-ICP-MS)精确测量铁和镁的同位素。在我们的实验中,首先用 7 mL 0.2 M HCl 和 0.5 M HF 的混合物收集铁组分,然后用 9 mL 1.3 M HCl 收集镁组分。这种方法适用于处理不同类型的岩石样本,可使铁的回收率达到 98%,镁的回收率达到 100%,并能有效去除基质元素,如 Al、Ti、Na、K、Ca 和其他微量元素。利用这种方法,报告了各种地质参考材料的铁和镁同位素组成。所有的铁和镁同位素分析结果都在分析不确定性范围内与所报告的数据一致,这验证了本文所建立的方法是可靠和可重复的。因此,该方法将成为未来获取地质样本中铁和镁同位素组成以及追踪地球化学或天体化学过程的一个重要选择。
{"title":"A single-column and efficient procedure for separating Fe and Mg from geological materials for isotopic analyses using MC-ICP-MS†","authors":"Guanhong Zhu, Zhenmin Ge, Le Zhang, Gangjian Wei and Jinlong Ma","doi":"10.1039/D4JA00272E","DOIUrl":"https://doi.org/10.1039/D4JA00272E","url":null,"abstract":"<p >Fe and Mg isotopes have increasingly served as combined proxies for geological processes. Fe and Mg isotope determination requires consuming different splits of samples and multi-column chromatographic purification to obtain pure Mg and Fe fractions in conventional chemical procedures, which is time-consuming and not suitable for rare and valuable samples. This study presents a novel and efficient chromatographic procedure to purify both Fe and Mg from geological matrices, using a single column loaded with AGMP-50 resin, followed by precise measurements of Fe and Mg isotopes by multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). In our experiment, the Fe fraction was first collected using 7 mL of a mixture of 0.2 M HCl and 0.5 M HF, and then the Mg fraction was collected using 9 mL of 1.3 M HCl. This procedure is suitable for processing different types of rock samples and enabling an Fe recovery of >98% and full recovery of Mg, with effective removal of matrix elements such as Al, Ti, Na, K, Ca, and other trace elements. Using this method, the Fe and Mg isotopic compositions of various geological reference materials were reported. All of the Fe and Mg isotopic analytical results were in agreement with the reported data within analytical uncertainties, verifying that the method established here is robust and reproducible. Thus, this procedure will serve as a great option for obtaining both Fe and Mg isotopic compositions of geological samples and tracing geochemical or astrochemical processes in the future.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Samples with very high Ca/Mg ratios present challenges for measuring their Mg isotope ratios. Here, we present an efficient method to separate Mg from samples with high Ca/Mg matrices, which also allows for quantitative separation of Sr, Ca and K. The method comprises a three-step chromatographic separation using DGA and AG50W-X12 (200–400 mesh) cation exchange resin. By utilising the automated sample purification system prepFAST MC™ for two of the three separations, the labour is substantially minimised. This analytical approach results in a quantitative Mg yield and a pure Mg solution, with other cations reduced to below the limit of detection (<53 ng mL−1). We demonstrate the efficacy of this method using a set of geochemical reference materials with Ca/Mg ratios ranging from 1.32 to 1271 mol mol−1. This approach enhances sample throughput and ensures high-quality separations in carbonate samples characterised by high Ca/Mg ratios.
{"title":"Mg separation from samples with very high Ca/Mg ratios for Mg isotope analysis","authors":"Niklas Keller and Michael Tatzel","doi":"10.1039/D4JA00266K","DOIUrl":"https://doi.org/10.1039/D4JA00266K","url":null,"abstract":"<p >Samples with very high Ca/Mg ratios present challenges for measuring their Mg isotope ratios. Here, we present an efficient method to separate Mg from samples with high Ca/Mg matrices, which also allows for quantitative separation of Sr, Ca and K. The method comprises a three-step chromatographic separation using DGA and AG50W-X12 (200–400 mesh) cation exchange resin. By utilising the automated sample purification system prepFAST MC™ for two of the three separations, the labour is substantially minimised. This analytical approach results in a quantitative Mg yield and a pure Mg solution, with other cations reduced to below the limit of detection (<53 ng mL<small><sup>−1</sup></small>). We demonstrate the efficacy of this method using a set of geochemical reference materials with Ca/Mg ratios ranging from 1.32 to 1271 mol mol<small><sup>−1</sup></small>. This approach enhances sample throughput and ensures high-quality separations in carbonate samples characterised by high Ca/Mg ratios.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/ja/d4ja00266k?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Konstantin Skudler, Michael Walter, Michael Sommer and Matthias Müller
Near-Edge X-ray Absorption Fine Structure (NEXAFS) spectra tend to be damped due to self-absorption effects when measured in fluorescence-yield mode in samples which are neither thin nor dilute. While established self-absorption correction methods are only valid for infinitely thick samples and partly inapplicable if the samples are too concentrated, the novel forward correction presented here is widely applicable, especially for intermediately thick and concentrated samples. Aiming towards quantitative analysis supporting the development of lithium sulfur battery materials, which are intermediately thick and not dilutable, the forward correction is applied to organo-sulfur liquid films as a proof of concept.
近边缘 X 射线吸收精细结构(NEXAFS)光谱在既不薄也不稀释的样品中以荧光-产量模式测量时,往往会由于自吸收效应而产生阻尼。已有的自吸收校正方法仅适用于无限厚的样品,如果样品过于浓缩,则部分方法不适用,而本文介绍的新型正向校正方法则广泛适用,尤其适用于中间厚和浓缩的样品。为了进行定量分析以支持锂硫电池材料的开发(这些材料厚度适中且不可稀释),正向校正法被应用于有机硫液体薄膜,作为概念验证。
{"title":"Self-absorption correction of NEXAFS spectra for intermediate sample thicknesses applied to organo-sulfur model compounds†","authors":"Konstantin Skudler, Michael Walter, Michael Sommer and Matthias Müller","doi":"10.1039/D4JA00232F","DOIUrl":"https://doi.org/10.1039/D4JA00232F","url":null,"abstract":"<p >Near-Edge X-ray Absorption Fine Structure (NEXAFS) spectra tend to be damped due to self-absorption effects when measured in fluorescence-yield mode in samples which are neither thin nor dilute. While established self-absorption correction methods are only valid for infinitely thick samples and partly inapplicable if the samples are too concentrated, the novel forward correction presented here is widely applicable, especially for intermediately thick and concentrated samples. Aiming towards quantitative analysis supporting the development of lithium sulfur battery materials, which are intermediately thick and not dilutable, the forward correction is applied to organo-sulfur liquid films as a proof of concept.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.rsc.org/en/content/articlepdf/2024/ja/d4ja00232f?page=search","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shu Chai, Jie Ren, Suming Jiang, Aochen Li, Ziqing Zhao, Haimeng Peng, Qiwen Zhang and Wendong Wu
Laser-induced breakdown spectroscopy (LIBS) is a promising technique to monitor carbon emissions in post-combustion flue gas. However, its measurement accuracy is susceptible to variations in gas temperature. In this work, a mid-level data fusion method integrating spectral and acoustic signals generated by laser-induced plasmas (LIPs) was proposed to improve the measurement accuracy. This method utilizes the high sensitivity of acoustic signals to variations in gas temperature, enabling a correction of temperature effects. The acoustic features were extracted from both the time-domain waveforms and frequency spectra, while the spectral features were selected using a SelectKBest method. These features were fused into a new array, on whose basis multivariate regression models including Partial Least Squares (PLS), Support Vector Machines (SVM), and Random Forest (RF) were trained. Data fusion significantly improved the predictive precision and trueness of SVM and RF models, with the RF model achieving the best performance: a coefficient of determination (R2) of 0.9941, a root-mean-square error (RMSE) of 0.4864, a mean absolute error (MAE) of 0.2587, and a mean absolute deviation (MAD) of 0.0980. Shapley additive explanation (SHAP) analysis revealed that in the RF model, the acoustic features that exhibited higher temperature sensitivity could be more frequently selected in the training process and thus had greater impacts on model outputs, which can better correct for the gas temperature effect. Furthermore, the potential of this method in industrial applications was demonstrated in an unsteady flow.
{"title":"Data fusion of spectral and acoustic signals in LIBS to improve the measurement accuracy of carbon emissions at varying gas temperatures","authors":"Shu Chai, Jie Ren, Suming Jiang, Aochen Li, Ziqing Zhao, Haimeng Peng, Qiwen Zhang and Wendong Wu","doi":"10.1039/D4JA00287C","DOIUrl":"https://doi.org/10.1039/D4JA00287C","url":null,"abstract":"<p >Laser-induced breakdown spectroscopy (LIBS) is a promising technique to monitor carbon emissions in post-combustion flue gas. However, its measurement accuracy is susceptible to variations in gas temperature. In this work, a mid-level data fusion method integrating spectral and acoustic signals generated by laser-induced plasmas (LIPs) was proposed to improve the measurement accuracy. This method utilizes the high sensitivity of acoustic signals to variations in gas temperature, enabling a correction of temperature effects. The acoustic features were extracted from both the time-domain waveforms and frequency spectra, while the spectral features were selected using a SelectKBest method. These features were fused into a new array, on whose basis multivariate regression models including Partial Least Squares (PLS), Support Vector Machines (SVM), and Random Forest (RF) were trained. Data fusion significantly improved the predictive precision and trueness of SVM and RF models, with the RF model achieving the best performance: a coefficient of determination (<em>R</em><small><sup>2</sup></small>) of 0.9941, a root-mean-square error (RMSE) of 0.4864, a mean absolute error (MAE) of 0.2587, and a mean absolute deviation (MAD) of 0.0980. Shapley additive explanation (SHAP) analysis revealed that in the RF model, the acoustic features that exhibited higher temperature sensitivity could be more frequently selected in the training process and thus had greater impacts on model outputs, which can better correct for the gas temperature effect. Furthermore, the potential of this method in industrial applications was demonstrated in an unsteady flow.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yumeng Liu, Tianyu Chen, Tao Li, Weiqiang Li, Qingquan Hong and Jiubin Chen
Rhenium (Re) and its isotopes offer valuable information for understanding various geological processes throughout Earth's history. However, Re isotope analysis remains quite challenging owing to its ultra-trace concentration in geological materials. Previous studies have developed column chemistry and analytical methods for Re isotope analysis, but issues such as tedious pretreatment and incomplete Re recovery still exist. Herein, we present a novel procedure integrating preconcentration and fast column chemistry for Re isotope analysis. Utilizing Na2S solution and activated carbon powder under acidified conditions, we achieved the quantitative recovery of Re from aqueous solutions via filtration while removing most matrices. Standard addition to diverse matrix solutions yielded complete Re recovery (99.6 ± 6.7%, n = 10, 2SD) and precise isotopic compositions (δ187Re = −0.49 ± 0.04‰, n = 10, 2SD), as determined using multi-collector inductively coupled plasma–mass spectrometry. Our method was applied to seawater (7.1 pg g−1 for Re) and solid reference materials (∼0.5–75 ng g−1 for Re), which resulted in stable and high recovery with isotopic results consistent with published data. Our method exhibits efficient matrix removal with stable and essentially quantitative Re recovery, which paves the way for wide applications of Re isotopes in the earth and planetary sciences.
铼(Re)及其同位素为了解地球历史上的各种地质过程提供了宝贵的信息。然而,由于铼在地质材料中的超痕量浓度,铼同位素分析仍然具有相当大的挑战性。以往的研究已开发出用于 Re 同位素分析的柱化学和分析方法,但仍存在预处理繁琐和 Re 回收不完全等问题。在此,我们提出了一种集预浓缩和快速柱化学于一体的用于 Re 同位素分析的新方法。在酸化条件下,利用 Na2S 溶液和活性炭粉末,我们通过过滤实现了从水溶液中定量回收 Re,同时去除大部分基质。使用多收集器电感耦合等离子体质谱法测定,在不同基质溶液中添加标准物质可获得完全的铼回收率(99.6 ± 6.7%,n = 10,2SD)和精确的同位素组成(δ187Re = -0.49 ± 0.04‰,n = 10,2SD)。我们的方法适用于海水(Re 含量为 7.1 pg g-1)和固体参考材料(Re 含量为 ∼0.5-75 ng g-1),结果稳定且回收率高,同位素结果与已发表的数据一致。我们的方法具有高效的基质去除能力,能稳定且基本定量地回收 Re,为 Re 同位素在地球和行星科学中的广泛应用铺平了道路。
{"title":"Efficient preconcentration of ultra-trace rhenium from geological materials via induced adsorption for accurate isotope analysis†","authors":"Yumeng Liu, Tianyu Chen, Tao Li, Weiqiang Li, Qingquan Hong and Jiubin Chen","doi":"10.1039/D4JA00295D","DOIUrl":"https://doi.org/10.1039/D4JA00295D","url":null,"abstract":"<p >Rhenium (Re) and its isotopes offer valuable information for understanding various geological processes throughout Earth's history. However, Re isotope analysis remains quite challenging owing to its ultra-trace concentration in geological materials. Previous studies have developed column chemistry and analytical methods for Re isotope analysis, but issues such as tedious pretreatment and incomplete Re recovery still exist. Herein, we present a novel procedure integrating preconcentration and fast column chemistry for Re isotope analysis. Utilizing Na<small><sub>2</sub></small>S solution and activated carbon powder under acidified conditions, we achieved the quantitative recovery of Re from aqueous solutions <em>via</em> filtration while removing most matrices. Standard addition to diverse matrix solutions yielded complete Re recovery (99.6 ± 6.7%, <em>n</em> = 10, 2SD) and precise isotopic compositions (<em>δ</em><small><sup>187</sup></small>Re = −0.49 ± 0.04‰, <em>n</em> = 10, 2SD), as determined using multi-collector inductively coupled plasma–mass spectrometry. Our method was applied to seawater (7.1 pg g<small><sup>−1</sup></small> for Re) and solid reference materials (∼0.5–75 ng g<small><sup>−1</sup></small> for Re), which resulted in stable and high recovery with isotopic results consistent with published data. Our method exhibits efficient matrix removal with stable and essentially quantitative Re recovery, which paves the way for wide applications of Re isotopes in the earth and planetary sciences.</p>","PeriodicalId":81,"journal":{"name":"Journal of Analytical Atomic Spectrometry","volume":null,"pages":null},"PeriodicalIF":3.1,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}