With pharmaceutical and biotechnology R&D witnessing unprecedented growth, molecular spectroscopy methods such as nuclear magnetic resonance (NMR), Fourier transform infrared (FTIR), and Raman ...

For a number of years, feed raw materials have been rapidly tested by near infrared spectroscopy (NIR) for tight control of ...

According to a comprehensive report from The Insight Partners, "Molecular Spectroscopy Market share and Forecasts 2022 - 2030, Global and Regional Share, Trend, and Growth Opportunity Analysis By ...

Agave plants may be best known for their role in tequila production, but they are also remarkably adept at retaining water in ...

Researchers used terahertz spectroscopy and imaging to gain new insights into how agave plants are so remarkably adept at ...

have developed a new machine learning model to improve microplastic identification. This model is based on a k-Nearest Neighbors (kNN) approach and analyzes a library of Raman spectra from various ...

Published as part of the "Spectroscopy Machine Learning" (SpectraML ... through techniques like mass spectrometry (MS), nuclear magnetic resonance (NMR), infrared (IR), Raman, and UV-Vis spectroscopy.

Texas-based Intuitive Machines is "ready to go" with the launch of its second moon landing mission, which is expected to blast off on Wednesday. Reuters reported that a company executive confirmed ...

Abstract: Machine learning methods, particularly deep learning ... One area significantly affected is nuclear spectroscopy, where the lack of annotated datasets is due to the challenges of manually ...

“Accurate estimation of voltage drop (IR drop) in modern Application-Specific Integrated ... To mitigate this challenge, we investigate how Machine Learning (ML) techniques, including Extreme Gradient ...

Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China Hefei National Research ...