The technique of laser-induced breakdown spectroscopy has been used for the first time to our knowledge for the identification of metals such as palladium and silver that were dispersed in bacterial cellulose membranes. These results for palladium-dispersed films have been correlated to a calibration curve obtained by use of atomic absorption spectroscopy and were found to be in good agreement. The experiments were conducted by use of wet and dry metal-doped membranes. The metal peaks obtained with a dry membrane are greater than five times higher in signal-to-background ratio than when metals are detected by a hydrated membrane. The advantage of this laser-based technique is that minimal sample handling and sample preparation are needed and measurements are completed in real time (a few seconds). Hence this technique can be used for the detection of metals in dry membranes that would be used in the construction of electrode assemblies.
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http://dx.doi.org/10.1364/ao.42.006174 | DOI Listing |
Foods
January 2025
Department of Physics, University of Patras, 26504 Patras, Greece.
The fast detection of Extra Virgin Olive Oil (EVOO) adulteration with poorer quality and lower price vegetable oils is important for the protection of consumers and the market of olive oil from fraudulent activities, the latter exhibiting an increasing trend worldwide during the last few years. In this work, two optical spectroscopic techniques, namely, Laser-Induced Breakdown Spectroscopy (LIBS) and UV-Vis-NIR absorption spectroscopy, are employed and are assessed for EVOO adulteration detection, using the same set of olive oil samples. In total, 184 samples were studied, including 40 EVOOs and 144 binary mixtures with pomace, soybean, corn, and sunflower oils, at various concentrations (ranging from 10 to 90% /).
View Article and Find Full Text PDFJ Hazard Mater
January 2025
College of Engineering and Technology, Southwest University, Chongqing 400716, PR China. Electronic address:
The detection of heavy metals in soil is of great scientific significance for food security and human health. However, traditional detection methods are complicated, time-consuming, and labor-intensive. Herein, we developed a novel method using Au@SiO nanoparticles (NPs) and surface microstructure combined with laser-induced breakdown spectroscopy (Au@SiO NPs-SMS-LIBS) for the rapid detection of lead (Pb), chromium (Cr), and copper (Cu) in soil samples.
View Article and Find Full Text PDFMol Med
January 2025
Research Institute, National Cancer Center, Goyang-Si, Gyeonggi-Do, 10408, Republic of Korea.
Background: Double-strand breaks (DSBs) are primarily repaired through non-homologous end joining (NHEJ) and homologous recombination (HR). Given that DSBs are highly cytotoxic, PARP inhibitors (PARPi), a prominent class of anticancer drugs, are designed to target tumors with HR deficiency (HRD), such as those harboring BRCA mutations. However, many tumor cells acquire resistance to PARPi, often by restoring HR in HRD cells through the inactivation of NHEJ.
View Article and Find Full Text PDFAnal Methods
January 2025
National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing, Beijing, 100029, China.
With the increasing demand for energy, nuclear energy has been developing rapidly. The quantitative detection and qualitative identification of uranium (U) are of great significance for the comprehensive and efficient use of U resources and the control of nuclear and radioactive substances. In this study, the detection of U is divided into liquid sample detection, solid sample detection, gas sample detection, and industrial detection from the perspectives of the sample state and detection environment.
View Article and Find Full Text PDFRSC Adv
January 2025
School of Electrical Engineering and Intelligentization, Dongguan University of Technology Dongguan 523808 China
This work employs the femtosecond laser-ablation spark-induced breakdown spectroscopy (fs-LA-SIBS) technique for the quantitative analysis of magnesium alloy samples. It integrates four machine learning models: Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), and -Nearest Neighbors (KNN) to evaluate their classification performance in identifying magnesium alloys. In regression tasks, the models aim to predict the content of four elements: manganese (Mn), aluminum (Al), zinc (Zn), and nickel (Ni) in the samples.
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