Jerome Workman, Jr.

In this interview, S. Michael Angel, a consultant on the SuperCam team, explains how the instrument analyzes Martian rocks and what it reveals about Mars’ geology and potential for past life.

A study from Chinese researchers demonstrates how combining satellite imagery, land use data, and machine learning can improve pollution monitoring in fast-changing urban rivers. The study focuses on non-optically active pollutants in the Weihe River Basin and showcases promising results for remote, data-driven water quality assessments.

New research highlights how remote satellite sensing technologies are changing the way scientists monitor inland water quality, offering powerful tools for tracking pollutants, analyzing ecological health, and supporting environmental policies across the globe.

Modern remote sensing technologies have evolved from coarse-resolution multispectral sensors like MODIS and MERIS to high-resolution, multi-band systems such as Sentinel-2 MSI, Landsat OLI, and UAV-mounted spectrometers. These advancements provide greater spectral and spatial detail, enabling precise monitoring of environmental, agricultural, and land-use dynamics.

A new dual-spectroscopy approach reveals real-time pollution threats in indoor workspaces. Chinese researchers have pioneered the use of laser-induced breakdown spectroscopy (LIBS) and aerosol mass spectrometry to uncover and monitor harmful heavy metal and dust emissions from soldering and welding in real-time. These complementary tools offer a fast, accurate means to evaluate air quality threats in industrial and indoor environments—where people spend most of their time.

A global research team has detailed how smart sensors, artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies are transforming the detection and management of environmental pollutants. Their comprehensive review highlights how spectroscopy and sensor networks are now key tools in real-time pollution tracking.

Scientists at Oak Ridge National Laboratory have demonstrated that a fast, laser-based mass spectrometry method—LA-ICP-TOF-MS—can accurately detect and identify airborne environmental particles, including toxic metal particles like ruthenium, without the need for complex sample preparation. The work offers a breakthrough in rapid, high-resolution analysis of environmental pollutants.

In this two-part "Icons of Spectroscopy" column, executive editor Jerome Workman Jr. details how Karl H. Norris has impacted the analysis of food, agricultural products, and pharmaceuticals over six decades. His pioneering work in optical analysis methods including his development and refinement of near-infrared spectroscopy, has transformed analysis technology. In this Part II article of a two-part series, we summarize Norris’ foundational publications in NIR, his patents, achievements, and legacy.

A new deep learning-enhanced spectroscopic platform—SERSome—developed by researchers in China and Finland, identifies medicinal and edible homologs (MEHs) with 98% accuracy. This innovation could revolutionize safety and quality control in the growing MEH market.

Researchers are using AI-enabled Raman spectroscopy to enhance the development, administration, and response prediction of cancer immunotherapies. This innovative, label-free method provides detailed insights into tumor-immune microenvironments, aiming to optimize personalized immunotherapy and other treatment strategies and improve patient outcomes.

In this "Icons of Spectroscopy" column, executive editor Jerome Workman Jr. details how Karl H. Norris has impacted the analysis of food, agricultural products, and pharmaceuticals over six decades. His pioneering work in optical analysis methods including his development and refinement of near-infrared (NIR) spectroscopy has transformed analysis technology. This Part I article of a two-part series introduces Norris’ contributions to NIR.

A leading-edge review led by researchers at Oak Ridge National Laboratory and MIT explores how artificial intelligence is revolutionizing the study of molecular vibrations and phonon dynamics. From infrared and Raman spectroscopy to neutron and X-ray scattering, AI is transforming how scientists interpret vibrational spectra and predict material behaviors.

A new study by researchers from Palo Alto Research Center (PARC, a Xerox Company) and LG Chem Power presents a novel method for real-time battery monitoring using embedded fiber-optic sensors. This approach enhances state-of-charge (SOC) and state-of-health (SOH) estimations, potentially improving the efficiency and lifespan of lithium-ion batteries in electric vehicles (xEVs).

A recent review in Energies explores the latest advancements in sensor applications for electric vehicle (EV) thermal management systems. The study, authored by Anyu Cheng, Yi Xin, Hang Wu, Lixin Yang, and Banghuai Deng from Chongqing University of Posts and Telecommunications, along with industry partners, examines how advanced optical sensors improve the efficiency, safety, and longevity of EVs.

A cutting-edge fiber optic sensing system, developed by researchers at Tongji University, leverages neural networks to classify vehicles with unprecedented accuracy. The system’s innovative design uses spectroscopic and optical sensor technologies to provide critical data for road maintenance and traffic management.

The second part of an in-depth interview exploring the use of flow imaging microscopy (FIM), a new technology used for subvisible particle characterization in biologics.

Using LIBS, infrared, and Raman spectroscopic techniques scientists detect quartz and hydrated silica, hinting at past Martian water activity and potential biosignatures

Scientists demonstrate submicron detection of microbes and minerals in Mars-analogue basalt using O-PTIR spectroscopy

Scientists detail the first 1,000 sols of spectroscopic mineral detections on Mars

The first part of an in-depth interview exploring the use of flow imaging microscopy (FIM), a new technology used for subvisible particle characterization in biologics.

Recent advancements in exoplanet detection, including high-resolution spectroscopy, adaptive optics, and artificial intelligence (AI)-driven data analysis, are significantly improving our ability to identify and study distant planets. These developments mark a turning point in the search for habitable worlds beyond our solar system.

Scientists are using advanced spectroscopic techniques to probe the universe, uncovering vital insights about celestial objects. A new study by Diriba Gonfa Tolasa of Assosa University, Ethiopia, highlights how atomic and molecular physics contribute to astrophysical discoveries, shaping our understanding of stars, galaxies, and even the possibility of extraterrestrial life.

Astronomers have made a significant leap in the study of exoplanet atmospheres with a new ground-based spectroscopic technique that rivals space-based observations in precision. Using the Exoplanet Transmission Spectroscopy Imager (ETSI) at McDonald Observatory in Texas, researchers have analyzed 21 exoplanet atmospheres, demonstrating that ground-based telescopes can now provide cost-effective reconnaissance for future high-precision studies with facilities like the James Webb Space Telescope (JWST) (1-3).

Researchers explore soft optical waveguides as a breakthrough technology for biocompatible sensing and robotics.

In this "Icons of Spectroscopy" column, executive editor Jerome Workman Jr. details how Tomas B. Hirschfeld has made many significant contributions to vibrational spectroscopy and has inspired and mentored many leading scientists of the past several decades.

A recent review published in Sensors explores the dynamic field of continuum robotics, with a particular focus on the advances in optical sensing technologies. The study, led by researchers from the Technical University of Košice and the University of Texas at Austin, highlights the dominance of optical fiber sensors in tracking robotic shape perception and environmental interactions, demonstrating spectroscopic applications and future potential.

Researchers at Oregon State University explore how machine learning, optical sensors, and robotics are transforming food quality assessment and processing, improving efficiency and reducing waste.

Given the importance of this nomenclature in guiding authors and reviewers, we invite members of the spectroscopy community to provide feedback, suggest updates, or participate in future revisions.