ANALYTICA CHIMICA ACTA
·
2021
·
IF 6.558
Pengju Xing,Junhang Dong,Peiwen Yu,Hongtao Zheng,Xing Liu,Shenghong Hu,Zhenli Zhu
In this study, a simple and effective method for accurate determination of lithium in brine samples was developed by the combination of laser induced breakdown spectroscopy (LIBS) and convolutional neural network (CNN). Our results clearly demonstrate that the use of CNN could efficiently overcome the complex matrix effects, and thus allows for on-site Li quantitative determination in brine samples by LIBS. Specifically, two CNN models with different input data (M-CNN with matrix emission lines, and DP-CNN with double Li lines) were constructed based on the primary matrix features on spectrum and Boltzmann equation , respectively. It was observed that DP-CNN model could greatly improve the accuracy of Li analysis. We also compared the quantitative analysis capabilities of DP-CNN model with partial least squares regression (PLSR) and principal component analysis-support vector regression (PCA-SVR) model, and the results clearly showed DP-CNN offers the best quantification results (higher accuracy and less matrix interference). Finally, five real brine samples were successfully analyzed by the proposed DP-CNN model, confirming by the average absolute error of the prediction of 0.28 mg L −1 and the average relative error of 3.48%. These results clearly demonstrate that input data plays an important role in the training of CNN model in LIBS analysis, and the proposed DP-CNN provides an effective approach to solve the matrix effects encountered in LIBS for Li measurement in brine samples.
研究领域:
分析化学
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人工智能应用
·
环境科学
✓ 已验证
产品关联
DOI
PubMed
高影响因子
JOURNAL OF ANALYTICAL ATOMIC SPECTROMETRY
·
2022
·Vol. 38
·期 1
·142-155
·
IF 4.351
Jia-Xin She,Weiqiang Li,Shichao An,Yuanfeng Cai
Stable Sn isotopes have the potential to constrain the behavior of Sn in various natural and anthropogenetic processes, however, studies on stable isotopes of Sn for terrestrial rock samples have been limited. Here, we present a high-precision Sn isotope analytical method of geological samples by the double spike technique. Samples were equilibrated with a 117Sn–122Sn double spike during dissolution, then Sn was purified from the rock matrix using TRU spec resin. The purified Sn solutions were analyzed on a Nu 1700 Sapphire MC-ICP-MS, and data reduction was performed using Newton–Raphson iteration. Effects of acid mismatch, concentration mismatch, and matrix elements were systematically tested to evaluate their impact on the accuracy and repeatability of the measurements. We show that the mismatch of acidity exerts negligible influences on the Sn isotope analysis. By contrast, the accuracy of Sn isotopic measurements could be compromised if the Sn concentration of the sample is below 40% of that of the standard. To ensure the accuracy of Sn isotope analysis, the concentration match between the sample and standard should be better than ±20%. When the measured solution has varying mass ratios of [X]/Sn (ng g−1/ng g−1) lower than 0.5 for Mg, Ca, Fe, Ti, U, Ni, Ag, As, Ru, and Mo, the measured Sn isotope composition was not affected. However, Cd in Sn samples could affect the accuracy of Sn isotope analysis by the 117Sn–122Sn double spike through the generation of 116CdH+ in plasma that interferes with 117Sn+. The Cd/Sn ratio needs to be below 0.01 to ensure analytical accuracy by the double spike method. The accuracy of this method was further verified by measuring the pure solution with both double spike and sample standard bracketing methods, as well as processing a synthetic solution through column chemistry. The Sn isotope compositions of geological samples measured using our method are reproducible and consistent with previous data from other laboratories. Additionally, Sn isotope data for geological reference materials BIR-1, RGM-1, and GSR-1 are reported for the first time. Based on repeated analyses of pure and geological reference materials, the long-term intermediate precision is better than ±0.069‰ on δ122/118Sn for a spiked solution of 100 μg g−1.
研究领域:
地球化学
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分析化学
·
地质学
✓ 已验证
DOI
高影响因子
TALANTA
·
2024
·
IF 5.6
Yongsheng Zhang,Jian Yuan,Rui Gao,Yang Zhao,Zefu Ye,Zhujun Zhu,Peihua Zhang,Lei Zhang,Wangbao Yin,Suotang Jia
Ash content, as a crucial indicator of coal quality, its rapid and accurate determination is pivotal to improve the energy utilization of coal and reduce environmental pollution. Traditional spectroscopic methods face significant challenges in acquiring accurate information from coal samples due to the notorious matrix effects arising from their complex composition, vast molecular structure, and diverse coal types. In this study, the feasibility of total reflection X-ray fluorescence (TXRF) combined with partial least squares (PLS) for the determination of coal ash was firstly investigated based on the TXRF being unaffected by matrix effects. Firstly, coal samples were prepared as suspensions, and the effects of sample particle size and different dispersants on the results of TXRF analyses were evaluated. The accuracy and applicability of the chosen sample preparation strategies were further validated using inductively coupled plasma mass spectrometry (ICP-MS) and two certified reference materials (CRMs). Subsequently, based on the analysis of 19 coal samples, the impact of three different predictive variables on the performance of the PLS model was investigated: (a) TXRF full spectrum normalized by the net intensity of the internal standard; (b) net intensity of characteristic peaks for 12 elements (Al, Si, K, Ca, Ti, Fe, Cr, Mn, Cu, Ni, and Sr) normalized by the net intensity of the internal standard; (c) concentrations of the aforementioned 12 elements. The results demonstrate that the PLS model constructed usingthe TXRF full spectrum normalized by the net intensity of the internal standard exhibits the best predictive capabilities, with the determination coefficient of calibration set (R 2 ) and mean square error (MSE) of the prediction set reaching 0.9736 and 0.99 %, respectively. Moreover, the measurement accuracy of this model was six times greater than that obtained with traditional X-ray fluorescence (XRF). Presented analytical results display the possibilities of combining TXRF with PLS for coal quality evaluation.
研究领域:
分析化学
·
环境科学
·
能源资源
✓ 已验证
产品关联
DOI
PubMed
高影响因子
危险品化学品经营许可证（带存储）