Deep Learning Advances Gas Quantification Analysis in Near-Infrared Dual-Comb Spectroscopy
May 15th 2024Researchers from Tsinghua University and Beihang University in Beijing have developed a deep-learning-based data processing framework that significantly improves the accuracy of dual-comb absorption spectroscopy (DCAS) in gas quantification analysis. By using a U-net model for etalon removal and a modified U-net combined with traditional methods for baseline extraction, their framework achieves high-fidelity absorbance spectra, even in challenging conditions with complex baselines and etalon effects.
AI-Based Neural Networks Revolutionize Infrared Spectra Analysis
May 13th 2024A Researcher from Lomonosov Moscow State University has developed a convolutional neural network (CNN) model for Fourier transform infrared (FT-IR) spectra recognition. This AI-based system is capable of classifying 17 functional groups and 72 coupling oscillations with remarkable accuracy, providing a significant boost to material analysis in fields like organic chemistry, materials science, and biology.
Researchers have recently investigated the microdistribution of a variety of trace elements in dirty and washed toenails (and the speciation of arsenic specifically in situ) using synchrotron X-ray fluorescence microscopy (XFM) and laterally resolved X-ray absorption near edge spectroscopy (XANES).
Exum Instruments and Edge Scientific Partner to Distribute Exum’s LALI-TOF-MS Technology in Canada
Exum Industries, producers of the first laser ablation laser ionization time of flight mass spectrometer (LALI-TOF-MS), announced a partnership with Edge Scientific to better promote and distribute their products.
Using logistics regression on laser-induced breakdown spectroscopy (LIBS) spectra of plasma samples collected pre- and post- Covid-19 pandemic from donors known to have developed various levels of antibodies to the SARS-Cov-2 virus, University of Massachusetts physics professor Nourddine Melikechi’s research team has shown that relying on the levels of sodium (Na), potassium (K), and magnesium (Mg) together is more efficient at differentiating the two types of plasma samples than any single blood metal alone. We spoke to Melikechi about this research.