Welcome to AMETEK - Materials Analysis Division
A global leading supplier of superior analytical instrumentation and imaging systems serving broad markets with unmatched support and expertise.
Our analytical systems include elemental analysis systems for bulk, micro and nanoscale applications as well as structural analysis & visualization systems and energy storage and test analyzers.
Our technologies, products, and services are instrumental in materials analysis for radioisotopic content. Key industry segments include nuclear power, nuclear security and materials safeguard, academia and research, environmental management, and health physics.
Our digital imaging technology services provide the ability to create custom sensors, perform diagnostic testing, and do production testing.
Our high-speed digital imaging cameras capture extremely fast moving events in industries such as ballistic studies, crash testing, and TV and film productions for "When it's too fast to see, and too important not to!"
The AMETEK Materials Analysis Division´s goal is to be the recognized market leading provider of world-class analytical systems, nuclear instrumentation and digital imaging solutions.
SPECTRO xSORT Handheld Spectrometer
The Thermo Scientific™ ARL™ EQUINOX 3000 X-ray Diffractometer for research enables accurate measurements.
KLA’s Filmetrics F40 allows you to transform your benchtop microscope into an instrument to measure thickness and refractive index.
This product profile describes the properties and applications of the ProMetric® I-SC Solution Imaging Colorimeter.
Dr. David Dung
We spoke with University of Bonn spin-off Midel Photonics, a start-up company whose laser beam shaping technology is hoping to sharpen up the laser industry.
Matthias Sachsenhauser, Ph.D.
Following Laser World of Photonics 2022, we spoke with Matthias Sachsenhauser from Hamamatsu Photonics about the role of laser-driven light sources in the future of the photonics sector.
Dr. Keith Paulsen
AZoOptics speaks to Dr. Keith Paulsen about the importance of breast cancer detection and the introduction of his team's deep-learning algorithm that associates spatial images of tissue optical properties with optical signal patterns measured during an imaging experiment or patient exam.