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.
AI and Dual-Sensor Spectroscopy Supercharge Antibiotic Fermentation
June 30th 2025Researchers from Chinese universities have developed an AI-powered platform that combines near-infrared (NIR) and Raman spectroscopy for real-time monitoring and control of antibiotic production, boosting efficiency by over 30%.
Toward a Generalizable Model of Diffuse Reflectance in Particulate Systems
June 30th 2025This tutorial examines the modeling of diffuse reflectance (DR) in complex particulate samples, such as powders and granular solids. Traditional theoretical frameworks like empirical absorbance, Kubelka-Munk, radiative transfer theory (RTT), and the Hapke model are presented in standard and matrix notation where applicable. Their advantages and limitations are highlighted, particularly for heterogeneous particle size distributions and real-world variations in the optical properties of particulate samples. Hybrid and emerging computational strategies, including Monte Carlo methods, full-wave numerical solvers, and machine learning (ML) models, are evaluated for their potential to produce more generalizable prediction models.
