Photonis USA and Georgia Tech Sign Sponsored Research Agreement
Photonis USA (Sturbridge, Massachusetts) has signed a sponsored research agreement with the Georgia Tech Research Corporation (Atlanta, Georgia) to design and develop a prototype of a new ion mobility spectrometer (IMS) analyzer, using patented technology from Photonis.
Photonis USA (Sturbridge, Massachusetts) has signed a sponsored research agreement with the Georgia Tech Research Corporation (Atlanta, Georgia) to design and develop a prototype of a new ion mobility spectrometer (IMS) analyzer, using patented technology from Photonis. The new product can be custom-manufactured to interface with a range of mass spectrometers or other sources to reduce the overall complexity of IMS analysis.
A key component in the IMS analyzer is Photonis’ resistive glass, which creates an electric field to guide or direct charged particles. The glass consists of alkali-doped lead silicate glass that has been reduced to make the surface a semiconductor, and can be drawn into custom shapes for use in ion guides, drift tubes, capillary inlet tubes, ion mirrors, collision cells, conversion diodes, or voltage dividers.
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.
