NASA Awards Xylem $300K Water Analysis Contract for Instrument to Detect Water on the Moon
The National Aeronautics and Space Administration (NASA) has awarded Xylem Inc., (White Plains, New York) a $300,000 contract to provide special mass spectrometry technology with a goal of detecting water on the moon.
The National Aeronautics and Space Administration (NASA) has awarded Xylem Inc., (White Plains, New York) a $300,000 contract to provide special mass spectrometry technology with a goal of detecting water on the moon.
NASA is currently developing a rover-based system under its Regolith and Environment Science and Oxygen and Lunar Volatiles Extraction, or RESOLVE, program to analyze moon dust for water and other chemical compounds on a possible future mission. Xylem will provide a miniaturized sector-field mass spectrometer that incorporates its technology for the detection of water and other target compounds.
“We are delighted that NASA has awarded Xylem this contract to help them further explore the moon,” said Chris McIntire, president of Xylem Analytics. “We are proud to be part of NASA’s continuing lunar exploration.”
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.
