Effect of liquid aerosol temperature on the detection performance of LIBS for analysis of phosphorus element in water

Yang, Xinyan; Wang, Xin; Li, Dongdong; Zhang, Xu; Li, Kuohu; Ren, Hongmei; Zhou, Zhengxian; Qin, Zhengbo; Zheng, Xianfeng

doi:10.1039/d3ja00286a

Cited by 3 publications

(1 citation statement)

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“…47 X. Y. Yang et al employed a liquid aerosol as the sampling method for LIBS to analyze phosphorus element in water. 48 They investigated the influence of liquid aerosol temperature on qualitative and quantitative analysis to enhance the detection performance of liquid aerosol-LIBS. Yang et al compared the spectral differences between the liquid surface and liquid aerosol, as well as the results of quantitative analysis.…”

Section: Case Studiesmentioning

confidence: 99%

In situ online detection of atmospheric particulate matter based on laser induced breakdown spectroscopy: a review

Sun,

Yu,

Feng

et al. 2024

J. Anal. At. Spectrom.

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Section: Case Studiesmentioning

confidence: 99%

In situ online detection of atmospheric particulate matter based on laser induced breakdown spectroscopy: a review

Sun,

Yu,

Feng

et al. 2024

J. Anal. At. Spectrom.

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Detection of defects in composite insulators based on laser‐induced plasma combined with machine learning

Xu,

Che

2024

Micro & Optical Tech Letters

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Due to Prolonged operation and external environmental factors, composite insulators may develop various defects, which can potentially lead to serious accidents in power systems. Detecting and analyzing these defects is critically important. In this study, we combine machine learning with laser‐induced breakdown spectroscopy (LIBS) to identify defects in composite insulators and obtain spectral data from both defective and nondefective samples. The Uniform Manifold Approximation and Projection algorithm was employed to reduce the dimensionality of the data, thereby enhancing detection accuracy and efficiency. Decision tree, random forest (RF), K‐nearest neighbor, and support vector machine algorithms were used to classify the dimensionality‐reduced data. The results indicate that the RF algorithm achieved the best classification performance, with accuracies of 100% and 95.46% for the training and test sets, respectively. Furthermore, the precision, recall, and F1 scores, which reflect model performance, also showed superior results with the RF algorithm. These findings suggest that the combination of LIBS technology and machine learning can rapidly and accurately detect and analyze defects in composite insulators, offering new insights and methods for defect detection in composite insulators.

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Enhancing phosphorus release from sewage sludge via anaerobic treatment: State-of-art progress and future challenges

Yao,

Yang,

et al. 2024

Chemical Engineering Journal

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Effect of liquid aerosol temperature on the detection performance of LIBS for analysis of phosphorus element in water

Abstract: A simple and convenient method has been developed to improve the LIBS detection performance of a liquid aerosol by adjusting the liquid aerosol temperature.

Cited by 3 publications

References 62 publications

In situ online detection of atmospheric particulate matter based on laser induced breakdown spectroscopy: a review

In situ online detection of atmospheric particulate matter based on laser induced breakdown spectroscopy: a review

Detection of defects in composite insulators based on laser‐induced plasma combined with machine learning

Enhancing phosphorus release from sewage sludge via anaerobic treatment: State-of-art progress and future challenges

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