Raman system with remote probes

Researchers have developed an analytical method combining remote near-infrared and Raman spectroscopy with machine learning to noninvasively map moisture and salt damage in historic buildings, offering critical insight into ongoing structural deterioration.

Researchers in Thailand have developed a cost-effective standoff Raman spectroscopy system using a night-vision intensified spectrometer and digital correction algorithms. The system reliably detects chemical compounds at distances up to 60 meters with high spectral resolution.

In this tutorial article, Yukihiro Ozaki explores the recent advancements and broadening applications of tip-enhanced Raman scattering (TERS), a cutting-edge technique that integrates scanning probe microscopy (SPM) with surface-enhanced Raman scattering (SERS). TERS enables highly localized chemical analysis at the nano- to subnano-scale, achieving spatial resolution well beyond the diffraction limit of light. Ozaki highlights the versatility of TERS in various experimental environments—ranging from ambient air to ultrahigh vacuum and electrochemical systems—and its powerful utility in fields such as single-molecule detection, biomolecular mechanism studies, nanomaterial characterization, and high-resolution imaging.

Spanish scientists have combined Raman imaging with powerful chemometric tools to identify and map organic molecules in a Martian meteorite, offering key insights for future Mars sample return missions and the search for past life on the Red Planet.

A team of scientists in Poland has unveiled the first detailed structural and magnetic analysis of the Ribbeck meteorite, a recently recovered space rock classified as an aubrite. Using Raman spectroscopy, X-ray diffraction, and advanced magnetic testing, researchers revealed the meteorite's unique mineralogy and its connection to deep space conditions.

Researchers have developed a powerful deep learning model that automates the identification of minerals using Raman spectroscopy, offering faster, more accurate results even in complex geological samples. By integrating attention mechanisms and explainable AI tools, the system boosts trust and performance in field-based mineral analysis.