Specac is a leading manufacturer of high quality FT-IR accessories and FT-IR/XRF sample preparation products. They regularly bring new and innovative solutions to the market.
Nobody makes more user-friendly or accurate products. ATR, diffuse reflectance, specular reflectance, and transmission are among the techniques served.
Pellets, discs, or films are covered by Specac's Atlas range of automated/manual benchtop pressing and grinding solutions. From small low tonnage manual hydraulic presses to 40-ton fully programmable powered presses with programmable loads, the Atlas range covers FT-IR, XRF, and more.
Specac products serve universities, research and development, forensics, pharmaceutical, and oil industries, and more.
Get in touch with us at Specac.com/contact/quote to receive information and pricing on a specific application or product and to try out the equipment with a free visit from an experienced member of our team.
Specac's range (fit most spectrometers) includes:
Specac has offices in the USA, UK, and China, and has a global network of dealers and distributors.
Specac Inc.
414 Commerce Dr. #175,
Fort Washington, PA 19034
TELEPHONE
+1 (866) 726 1126
FAX
(215) 793-4011
E-MAILsales@specac.com
WEB SITEwww.specac.com
YEAR FOUNDED
1971
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.