NIST Researchers Develop Frequency Converter for NIR Spectroscopy
Researchers at the National Institute of Standards and Technology (NIST, Gaithersburg, Maryland) have developed a highly sensitive, low-cost NIR spectroscopy technique that can measure the specific wavelengths used in telecommunications as well as single-photon levels of infrared light given off by fragile biomaterials and nanomaterials.
Researchers at the National Institute of Standards and Technology (NIST, Gaithersburg, Maryland) have developed a highly sensitive, low-cost NIR spectroscopy technique that can measure the specific wavelengths used in telecommunications as well as single-photon levels of infrared light given off by fragile biomaterials and nanomaterials. The approach “up converts” infrared photons up to the visible range using a tunable laser. The narrow-band pump laser scans the infrared signal photons and converts only those that have the desired polarization and wavelength to visible light. The visible light is easily detected by commercially available avalanche photodiode detectors. The new system reportedly enables spectra to be measured with a sensitivity that is greater than 1000 times that of current commercial optical spectral instruments.
Multi-Analytical Study Reveals Complex History Behind Ancient Snake Motif in Argentine Rock Art
May 22nd 2025A recent study published in the Journal of Archaeological Science: Reports reveals that a multi-headed snake motif at Argentina's La Candelaria rock shelter was created through multiple painting events over time.
Fluorescence Spectroscopy Emerges as Rapid Screening Tool for Groundwater Contamination in Denmark
May 21st 2025A study published in Chemosphere by researchers at the Technical University of Denmark demonstrates that fluorescence spectroscopy can serve as a rapid, on-site screening tool for detecting pharmaceutical contaminants in groundwater.
New Deep Learning AI Tool Decodes Minerals with Raman Spectroscopy
May 21st 2025Researchers have developed a powerful deep learning model that automates the identification of minerals using Raman spectroscopy, offering faster, more accurate results even in complex geological samples. By integrating attention mechanisms and explainable AI tools, the system boosts trust and performance in field-based mineral analysis.
