Moxtek Inc.
Company Description
Moxtek is a leading supplier of advanced nano-optical and X-ray components used in display electronics, imaging, and analytical instrumentation. Moxtek provides innovative, solution-based products and services focused on performance, quality, and value. Moxtek products enable many new scientific discoveries and improve the quality of everyday life.
Moxtek optical polarizers and polarizing beamsplitters enable advancements in projection display and analytical instrumentation including: 2-D and 3-D projection display, near-eye display, and optical analysis instrumentation.
Moxtek X-ray products empower compact handheld and benchtop elemental analysis for positive material identification. Moxtek products are used in various EDXRF, WDXRF, and XRD systems for environmental screening, hazardous substance analysis, and sorting and recycling.
Markets Served
- EDXRF and WDXRF
- XRD
- Microanalysis
- Radiographic Inspection
- Medical Imaging
- X-ray Imaging
- Security
- Non-destructive testing (NDT)
- 2D and 3D projection display
- HMD/HUD
- Polarimetric imaging
- UV, visible, and IR spectroscopy
Moxtek
452 West 1260 North
Orem, UT 84057
TELEPHONE
(801) 225-0930
FAX
(802) 221-1121
E-MAILinfo@moxtek.com
WEB SITEwww.moxtek.com
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.
