PHOTONIS is a multinational high-technology group, with more than 40 years experience in manufacturing, sales, and innovation, specializing in charged particle and photon sensor technology. The group operates internationally in the night vision, industrial, scientific, and medical markets.
Mass spectrometry, nuclear detection, medical instrumentation, pharmaceutical safety, industrial instrumentation, and image intensification, as well as the custom design and manufacture of detectors, sensors, and ion mobility analyzers.
PHOTONIS offers a complete range of high performance scientific and medical detector products. Our market includes electron multipliers, microchannel plates, mass spectrometry fiber optics and resistive glass, advance performance time-of-flight detectors, image intensifiers, ion mobility analyzers, and other related products. Our detection products are found in most of today's technology-based markets, including analytical instrumentation, medical diagnostics, chemistry, drug discovery, high-energy physics, space exploration, and scientific research. PHOTONIS is the largest supplier of standard, retrofit, and custom detectors in the mass spectrometry, residual gas analyzer, and electron microscope markets providing advanced detector designs for the highest sensitivity through superior signal collection and noise reduction.
Sturbridge, Massachusetts.
Lancaster, Pennsylvania.
Frisco, Texas.
Brive, France.
Roden, Netherlands.
US: 150
Elsewhere: 900
Best of the Week: SciX Award Interviews, Tip-Enhanced Raman Scattering
June 13th 2025Top articles published this week include an interview about aromatic–metal interactions, a tutorial article about the recent advancements in tip-enhanced Raman spectroscopy (TERS), and a news article about using shortwave and near-infrared (SWIR/NIR) spectral imaging in cultural heritage applications.
Hyperspectral Imaging for Walnut Quality Assessment and Shelf-Life Classification
June 12th 2025Researchers from Hebei University and Hebei University of Engineering have developed a hyperspectral imaging method combined with data fusion and machine learning to accurately and non-destructively assess walnut quality and classify storage periods.