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.