Application of laser-induced breakdown spectroscopy with a generalized regression neural network and LASSO-type methods for estimation of arsenic and chromium in soil

Harefa, Edward; Li, Na; Zhou, Weidong

doi:10.1039/d2ja00060a

Cited by 3 publications

(3 citation statements)

References 50 publications

(57 reference statements)

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“…The development of agnostic or automated chemometric calibration tools would be ideal for chemometric models to be efficiently used in the HTP electrochemistry space. A possible path forward in this regard is using generalized regressor neural networks (GRNN) and least absolute shrinkage and selection operator (LASSO) techniques: Harefa et al have demonstrated an improvement in the quantitation accuracy of dissolved cations in aqueous media over the performance of conventional univariate regression of one emission spectral line by using active learning techniques to determine the most important lines from among all the emission lines for the analytes of interest . Their work demonstrates that active learning techniques exhibit potential to overcome the need for a priori parametric tuning of multivariate linear regressors presented by chemometric methods to fit CV data.…”

Section: Successes and Opportunities In High-throughput Electrochemic...mentioning

confidence: 99%

High-Throughput Electrochemistry to Study Materials Degradation in Extreme Environments

et al. 2022

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Electrochemistry has been used for decades to study materials’ degradation in situ in corrosive environments, whether it is in room-temperature chemically aggressive solutions containing halide ions or in high-temperature oxidizing media such as pressurized water, liquid metals, or molten salts. Thus, following the recent surge in high-throughput techniques in materials science, it seems quite natural that high-throughput electrochemistry is being considered to study materials’ degradation in extreme environments, with the objective to reduce corrosion resistant alloy development time by orders of magnitude and identify complex degradation mechanisms. However, while there has been considerable interest in the development of high-throughput methods for accelerating the discovery of corrosion resistant materials in different environments, these extreme environments propose formidable and exciting challenges for high-throughput electrochemical instrumentation, characterization, and data analysis. It is the objective of this paper to highlight those challenges, to present relatively new efforts to tackle them, and to develop research perspectives on the future of this exciting field. This Perspective is articulated around four main interconnected topics, which must be conjointly considered to enable corrosion resistant alloy design using high-throughput electrochemical methods: (1) high-throughput processing methods to develop material libraries, (2) high-throughput electrochemical methods for corrosion testing and evaluation, (3) high-throughput machine-learning augmented electrochemical data analysis, and (4) high-throughput autonomous electrochemistry representing the future of accelerated electrochemistry research.

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Section: Successes and Opportunities In High-throughput Electrochemic...mentioning

confidence: 99%

High-Throughput Electrochemistry to Study Materials Degradation in Extreme Environments

et al. 2022

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“…Weinhandl et al (2022) mentions visual aids can bridge abstract mathematical concepts with concrete situations. (Abbas & Zakaria, 2018;Alshatri et al, 2019;Harefa et al, 2022) which define props as objects that function to demonstrate a meaning or understanding of the object. Demonstrating is an activity to make/visualize an understanding so that verbalism does not occur.…”

Section: Introductionmentioning

confidence: 99%

Improving Mathematics Learning Outcomes Using Concrete Props in Grade V Elementary School Students

Tran

Duong

2023

iraono

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Topics in learning mathematics are generally abstract in nature, while elementary school-age children think concretely. This makes a teacher must be able to manage learning mathematics well. The purpose of this study is to describe through concrete props in learning the material of the nets of simple space buildings can improve student learning outcomes. The method used is descriptive method with the form of research, namely classroom action research with 2 cycles. The research was conducted in class V at a elementary school in Hanoi City, Vietnam. The number of respondents consisted of 129 students consisting of 58 male students and 71 female students. Based on the first cycle, student learning completeness was only 37.21%. This achievement is classified as low. Based on reflection, cycle II made improvements in planning and in implementing learning. In cycle II there was an increase, where classical learning completeness became 96.89%. This shows that the use of concrete teaching aids in math learning can improve students' learning outcomes

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“…LIBS has been used to the analysis of three chromium-doped soils by principal components analysis (PCA) and neural networks analysis (NNA) on the paper of Sirven J B et al [29]. The result was best with a prediction accuracy and precision of about 5% in the determination of chromium concentration and a significant reduction of the data.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis of Cr in soil using laser induced breakdown spectroscopy combined with relevance vector machine method

Lu¹,

Zhou²

2023

First International Conference on Spatial Atmospheric Marine Environmental Optics (SAME 2023)

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The laser induced breakdown spectroscopy (LIBS) technology coupled with a principal component analysis and relevance vector machine (PCA-RVM) was used for rapidly detect the concentration of heavy metal Cr in soil. The PCA-RVM was obtained by optimizing the relevance vector machine (RVM) with PCA.LIBS spectrum of 14 soil samples with different concentration of elements were collected and used for the model training and prediction. 10 of those were selected as training sample sets to build the PCA-RVM model, and the others as test sample sets for model evaluation. Comparing with the prediction results of PCA-RVM model with that obtained using RVM and support vector machines (SVM) model, the analytical accuracy improved by 84.17% to RVM and 92.62% to SVM at the maximum, respectively. Indicating that the PCA-RVM model can effectively improve the detection accuracy and repeatability of LIBS analysis of elemental concentration in soil.

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Application of laser-induced breakdown spectroscopy with a generalized regression neural network and LASSO-type methods for estimation of arsenic and chromium in soil

Abstract: The identification of heavy metals in soil, specifically arsenic (As) and chromium (Cr), is critical for evaluating the preservation and quality of the soil. Laser-induced breakdown spectroscopy has become a...

Cited by 3 publications

References 50 publications

High-Throughput Electrochemistry to Study Materials Degradation in Extreme Environments

High-Throughput Electrochemistry to Study Materials Degradation in Extreme Environments

Improving Mathematics Learning Outcomes Using Concrete Props in Grade V Elementary School Students

Quantitative analysis of Cr in soil using laser induced breakdown spectroscopy combined with relevance vector machine method

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