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Here, the discrimination of two types of lethal brain cancers, i.e., glioblastoma multiforme (GBM) and oligodendroglioma (OG) are investigated under the laser-induced breakdown spectroscopy (LIBS) and the electrical spark-assisted laser-induced breakdown spectroscopy (SA-LIBS) in order to discriminate the human brain glioma lesions against the infiltrated tissues. It is shown there are notable differences between the plasma emissions over the brain gliomas against those of infiltrated tissues. In fact, a notable enhancement appears in the characteristic emissions in favor of SA-LIBS against those of conventional LIB spectra. Moreover, the plasma properties such as temperature, electron density, and degree of ionization are probed through the data processing of the plasma emissions. The corresponding parameters, taken from SA-LIBS data, attest to be lucidly larger than those of LIBS up to one order of magnitude. In addition, the ionic species such as Mg II characteristic line at 279 nm and caII emission at 393 nm are notably enhanced in favor of SA-LIBS. In general, the experimental evidence verifies that SA-LIBS is beneficial in the discrimination and grading of GBM/OG neoplasia against healthy (infiltrate) tissues in the early stages.
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http://dx.doi.org/10.1364/BOE.497234 | DOI Listing |
Rev Sci Instrum
September 2025
National Centre for Physics (NCP), Islamabad, Pakistan.
Time-resolved data acquisition is crucial for compositional analysis using Laser-Induced Breakdown Spectroscopy (LIBS). It can be managed by adjusting the delay time and gate width of the spectrometer. This study describes the compositional analysis of molybdenum (Mo) ore utilizing charge coupled device (CCD) and intensified charge-coupled device (ICCD) based LIBS systems.
View Article and Find Full Text PDFAnal Chim Acta
November 2025
Laser Spectroscopy Lab, Department of Physics, University of Agriculture Faisalabad, 38090, Pakistan. Electronic address:
Background: Classification of rose species and verities is a challenging task. Rose is used worldwide for various applications, including but not restricted to skincare, medicine, cosmetics, and fragrance. This study explores the potential of Laser-Induced Breakdown Spectroscopy (LIBS) for species and variety classification of rose flowers, leveraging its advantages such as minimal sample preparation, real-time analysis, and remote sensing.
View Article and Find Full Text PDFTalanta
August 2025
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China; Key Laboratory of Special Purpose Equipment and Advanced Processing Technology, Ministry of Education, Zhejiang University of Technology, Hangzhou, China; Zhejiang Key Laboratory of High-Precision and Efficiency H
Rapid and accurate quantification of mineral elements in plants facilitates the optimization of cultivation strategies and provides theoretical support for heavy metal pollution control. Compared to traditional chemical detection methods, laser-induced breakdown spectroscopy (LIBS) offers rapid, simultaneous multi-element analysis. However, the quantitative accuracy of LIBS is often hindered by challenges such as sample heterogeneity and the inherent matrix effects arising from the physical and chemical properties of samples.
View Article and Find Full Text PDFACS Omega
August 2025
Radioisotope Science and Technology Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, Tennessee 37830, United States.
A method was developed to sample molten salts by sparging to generate and transport aerosols to an isolated instrument for compositional analysis by laser-induced breakdown spectroscopy (LIBS). Real-time monitoring of molten salt composition is critical to developing molten salt nuclear reactors, which offer enhanced safety and efficiency. In this article, the sparge sampling method is described and compared with sampling using a Collison nebulizer.
View Article and Find Full Text PDFACS Omega
August 2025
School of Energy and Environment, Anhui University of Technology, Maanshan 243002, China.
Accurate assessment of coal quality is essential for optimizing combustion efficiency and reducing pollutant emissions in coal-fired power plants. In this study, we developed a laser-induced breakdown spectroscopy (LIBS)-based framework, combined with advanced machine learning techniques to predict key coal quality parameters, including elemental carbon, ash content, volatile matter, total sulfur, and calorific value. After applying spectral preprocessing methods.
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