Raman Microscopy as a Valuable Tool for Failure Analysis (PDF)

A combination of surface-enhanced Raman spectroscopy (SERS) and machine learning on microfluidic chips has achieved an impressive 98.6% accuracy in classifying leukemia cell subtypes, offering a fast, highly sensitive tool for clinical diagnosis.

An interview with Paul Pudney, a winner of the William F. Meggers Award.

A recent study examined how Raman spectroscopy, when combined with machine learning (ML), can detect and analyze fertilizer nutrients.

An interview with Charles Wilkins, the winner of the 2013 American Chemical Society Division of Analytical Chemistry Award in Chemical Instrumentation, sponsored by the Dow Chemical Company.

Researchers have developed a rapid, reagent-free method to estimate the saponification value (SV) of edible oils using handheld Raman spectroscopy. This innovative approach simplifies oil quality testing, cutting time and costs while enhancing accuracy and portability.

Because there is a different Raman signature for each of the polymorphs as well as the contaminants, Raman microscopy is an ideal tool for analyzing the structure of these materials as well as identifying possible contaminants that would also interfere with performance.