Atomic Spectroscopy

An upcoming poster session at the American Chemical Society (ACS) Fall 2026 Meeting will explore how spectroscopy can be used to identify the origin of lithium in oilfield wastewater.

A rule proposed by the White House Office of Management and Budget (OMB) would give political appointees final authority over discretionary grant decisions ahead of peer reviewers, allow agencies to terminate active grants without a formal right of appeal, and restrict international collaboration and publication funding across federal science agencies. For the optical, molecular, vibrational, and atomic spectroscopy community, the proposal could affect the grants, journal support, and student and postdoctoral positions that sustain the field.

This proof-of-concept study demonstrates that electrothermal vaporization inductively coupled plasma optical emission spectrometry combined with multivariate analysis can accurately classify the sex of individuals from both dyed and undyed hair samples, highlighting its potential as a green, forensic tool for human sex determination.

Spectroscopy is playing a key role in analyzing materials in lithium-ion batteries.

Spectroscopy’s “What’s Nu” newsletter in May highlights the development of lasers in spectroscopy, compensating for repack variation in near-infrared (NIR) spectroscopy, and validity by design.

How is spectroscopy being used to detect methane and contribute to sustainability missions?

This article presents a strategic six-stage product development roadmap for atomic spectroscopy instruments, integrating Strategic Goal Setting with RISE prioritization, Kano analysis, and Three Horizons innovation. Emphasis is placed on beta validation to ensure inductively coupled plasma mass spectrometry( ICP-MS), inductively coupled plasma optical emission spectroscopy (ICP-OES), and atomic absorption spectroscopy (AAS) systems achieve technical excellence, regulatory compliance, market success, and long-term leadership in trace elemental analysis.

This first installment of Research Profiles in Spectroscopy Series features The University of California, Santa Barbara (UCSB) Petrochronology Research Group and its advances in laser ablation ICP-MS, isotope geochemistry, and petrochronology. Led by John Cottle, Andrew Kylander-Clark, and Morgan Adamson, the group has developed innovative spectroscopic methods that combine high-resolution isotopic dating with trace-element analysis to better understand petrochronology processes, including mountain building, crustal evolution, and complex geological processes.

This new feature in Spectroscopy introduces a structured, application-focused series that curates and examines the most influential research papers in molecular and atomic spectroscopy. Each installment presents a focused “Top 10” collection of seminal publications within a specific analytical domain, spanning techniques such as ultraviolet–visible, infrared, Raman, near-infrared, and atomic spectroscopy. Across biomedical, biopharmaceutical, environmental, and forensic applications, the selected papers illustrate how spectroscopic methods are applied to real-world analytical challenges. Emphasis is placed on the integration of spectral data with chemometric approaches to enable robust calibration, accurate prediction, and meaningful interpretation. Together, these curated collections provide practitioners with a concise, application-oriented perspective on impactful developments in spectroscopy. This article brings together the first nine “Top 10” collections in the series, offering a cross-disciplinary view of influential work shaping the field.

How can micro-particle induced X-ray emission (µ-PIXE) and micro-ion beam induced luminescence (µ-IBIL) spectroscopy improve conservation practices?

What does the aluminosilicate and carbonate particles on bitumen-coated bandages of mummies tell us about the burial environment?

Abstract submissions are open through April 15 as conference expands scope to include molecular methods.

This article is derived from an invited talk given at the Pittcon Conference and Expo in San Antonio, Texas on Monday, March 9, exploring how generative artificial intelligence may transform the daily practice of analytical chemistry. It was presented in The James L. Waters Symposium.

The Pittcon (Pittsburgh) Conference and Expo in San Antonio featured a forward-looking symposium exploring how generative artificial intelligence (AI) may transform the daily practice of analytical chemistry. The James L. Waters Symposium, “Generative AI in the Analytical Chemist’s Toolbox for Chemical Measurements”, took place on Monday, March 9, 2026 (2:30–4:40 p.m.) in Room 221A. The session was presided over by Daniel W. Armstrong of The University of Texas at Arlington, who introduced the topic by emphasizing the rapidly expanding knowledge base required of modern analytical chemists. In addition to chemistry, today’s analytical scientist must command elements of physics, advanced mathematics, data science, and, increasingly, AI. The symposium focused on the practical integration of generative AI tools into chemical measurement science. Speakers discussed how AI can assist analytical chemists with tasks such as algorithm generation, signal processing, literature synthesis, and data interpretation. Importantly, the session emphasized responsible implementation, highlighting the need for rigorous validation, high-quality data sets, and integration into existing laboratory workflows.

The 2024-2026 period has been marked by rapid methodological innovation and critical reassessment of established atomic spectrometric techniques in environmental analysis. Advances in inductively coupled plasma–tandem mass spectrometry (ICP-MS/MS) reaction-cell chemistry, matrix-effect correction in X-ray fluorescence (XRF), microwave-sustained plasma sources, and green preconcentration strategies have expanded analytical capabilities for soils, waters, sediments, plants, and atmospheric particulates. Simultaneously, comparative evaluations of inductively coupled plasma–mass spectrometry (ICP-MS), inductively coupled plasma–optical emission spectrometry (ICP-OES), and XRF have sharpened our understanding of detection limits, bias, and field applicability. This brief review highlights 10 of the most influential publications shaping environmental applications of XRF, ICP-MS, and ICP-OES during 2024–2026. Each paper is discussed with emphasis on its technical contributions and broader impact on environmental monitoring, regulatory science, and instrumental development.

Artificial intelligence and machine learning are rapidly reshaping how analytical data are modeled, interpreted, and deployed, but the conceptual foundation is already familiar to practitioners of chemometrics. Latent variables, calibration models, variance–bias tradeoffs, and multivariate optimization did not originate with neural networks; they have been central to spectroscopic data analysis for decades. This expanded glossary provides a rigorous, side-by-side translation between modern artificial intelligence (AI) terminology and established chemometric concepts. This glossary is intended to demystify AI terminology, while preserving statistical clarity. It is designed to help analytical scientists, spectroscopists, and chemometricians engage with modern data-driven methods without abandoning physical interpretability or statistical discipline.

From a chemometric standpoint, artificial intelligence (AI) in spectroscopy is best understood as an extension of established multivariate methods rather than as a replacement. Most AI approaches closely parallel familiar tools such as regression, classification, and principal component analysis, but offer greater flexibility to handle nonlinear behavior, interacting physical and chemical effects, and large, heterogeneous datasets. By learning directly from raw spectra, AI methods can reduce reliance on manual preprocessing while still indicating which spectral regions influence predictions. In this sense, AI represents a developmental layer of chemometrics that enables classical concepts to operate effectively in modern spectroscopic systems. Overall, AI is best viewed as the next developmental layer of chemometrics, not as a competing discipline. As with all current AI programs, domain knowledge of analytical chemistry is essential for AI’s effective application. Knowing the boundaries of what is plausible in any chemical or modeling system allows fine-tuning of the models towards useful and reliable analytical results.

At Pittcon, generative artificial intelligence will be presented at the James L Waters Symposium on Monday, March 9, 2:30 PM to 4:40 PM in Room 221A. Generative artificial intelligence has transitioned from a conceptual novelty to a practical approach for innovation in spectroscopic data analysis. During 2025, a small set of highly influential publications crystallized this transformation by demonstrating how generative models can synthesize realistic spectra, solve inverse spectral problems, accelerate materials discovery, and automate molecular structural elucidation. This article reviews six pivotal contributions published in 2025 that collectively define the state of generative artificial intelligence in spectroscopy. These works establish theoretical foundations, survey emerging methods, introduce physics-informed generative architectures, and demonstrate transformative applications across vibrational, electronic, and magnetic resonance spectroscopies.

In this article, we highlight some of the important talks on atomic spectroscopy that will take place at Pittcon on Sunday March 8th.

How are ICP-MS and ICP-OES revealing heavy-metal trends in pet food? In this short tutorial, we explain how these techniques have been tracking pet food trends, and what owners can do in response to these trends.

This year’s Emerging Leader in Atomic Spectroscopy Award recipient is Sarah Theiner, whose research is focused on the application of atomic spectroscopy techniques—laser ablation inductively coupled plasma–mass spectrometry (LA-ICP-MS) and single-cell ICP-MS—to expand these analytical techniques as tools for biological and clinical imaging and drug-distribution studies.

The primary goal of this study was to evaluate two microwave digestion systems for the acid decomposition of biological tissues: (1) a single reaction chamber (SRC) system and (2) a rotor-based, closed vessel microwave (RBCVM) system.

The 12th Nordic Conference on Plasma Spectrochemistry and Ionization Principles in Mass Spectrometry will take place from June 14–18, 2026, in Loen, Norway. We preview the conference here.

The 2026 James L. Waters Annual Symposium at Pittcon will focus on the integration of generative AI into analytical chemistry, examining how large language models and AI tools can support method development, data analysis, and chemical measurement while maintaining scientific rigor, validation, and interpretability. Continuing its decades-long tradition of connecting historical perspective with emerging technologies, the symposium will feature presentations from leading chemists and spectroscopists, highlighting both the opportunities and challenges of responsibly incorporating AI into chemical measurement science.

In this edition of “Inside the Laboratory,” Martin Resano, a Coordinator of the Rapid Analysis Methods with Spectroscopic Techniques (MARTE) group and as part of the Aragon Institute for Engineering Research (I3A) at the University of Zaragoza, discusses how compressed sensing spectroscopic techniques are used in his laboratory.