Short Courses
Short Courses
May
29–31 Mass Spectral Interpretation
Pittsburgh, PA
pacslabs.com/course/10massspectralinterpretationcourse.php
June
3–7 Fundamentals of X-ray Powder Diffraction
Newtown Square, PA
www.icdd.com/education/xrd.htm
5–7 Mass Spectral Interpretation
Chicago, IL
pacslabs.com/course/10massspectralinterpretationcourse.php
9 SC189 Photonic Quantum-Enhanced Technologies
San Jose, CA
www.cleoconference.org/home/program/short-courses/sc189/
9 Foundations of Nonlinear Optics
San Jose, CA
www.cleoconference.org/home/program/short-courses/sc149/
9 Nano-Photonics: Physics and Techniques
San Jose, CA
www.cleoconference.org/home/program/short-courses/sc221/
9 High Power Fiber Lasers and Amplifiers
San Jose, CA
www.cleoconference.org/home/program/short-courses/sc270/
9 Frontiers of Guided Wave Nonlinear Optics
San Jose, CA
www.cleoconference.org/home/program/short-courses/sc396/
9 Nano-Cavity Quantum Electrodynamics and Applications
San Jose, CA
www.cleoconference.org/home/program/short-courses/sc403/
10 Introduction to Ultrafast Optics
San Jose, CA
www.cleoconference.org/home/program/short-courses/sc378/
10 Coherent Mid-Infrared Sources and Applications
San Jose, CA
www.cleoconference.org/home/program/short-courses/sc361/
10 Coherent Mid-Infrared Sources and Applications
San Jose, CA
www.cleoconference.org/home/program/short-courses/sc361/
10 Quantum Cascade Lasers: Science, Technology, Applications and Markets
San Jose, CA
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.
