X-ray analytical techniques, spanning X-ray fluorescence (XRF), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS), are becoming faster, more portable, and more energy-efficient, with microbeam XRF spot sizes once exclusive to synchrotrons now available on laboratory benchtops.
X-ray analytical techniques, spanning X-ray fluorescence (XRF), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS), are becoming faster, more portable, and more energy-efficient, with microbeam XRF spot sizes once exclusive to synchrotrons now available on laboratory benchtops.
AI-assisted spectral interpretation is reaching field-portable EDXRF analyzers, while the 2023 Nobel Prize in Physics for attosecond pulse generation underscores how deeply X-ray and extreme ultraviolet techniques are woven into fundamental electron-dynamics research.
This article surveys recent X-ray instrumentation, patents, and applications across food safety, forensics, planetary science, and battery research, with perspectives from the scientists and instrument developers advancing the field.
X-ray analytical techniques occupy a distinct niche among spectroscopic methods: rather than probing valence electron or vibrational transitions, XRF measures characteristic core-electron emission to identify elemental composition, XRD measures diffraction patterns from crystal lattices to determine phase and structure, and XPS measures photoelectron binding energies to characterize surface chemistry and oxidation state. Together, these techniques span applications from food safety screening to forensic explosives analysis to battery electrode characterization.
The field's foundational importance to physics was reaffirmed in 2023, when Pierre Agostini, Ferenc Krausz, and Anne L'Huillier received the Nobel Prize in Physics for experimental methods that generate attosecond pulses of light for the study of electron dynamics in matter, tools capable of probing electron behavior on the timescale of electron motion itself.1 As the American Physical Society noted in recognizing the achievement, these techniques produce extremely short pulses of light that can be used to “probe the minute dynamics of electrons in atoms and molecules,” allowing researchers to examine ultrafast electron behaviors that could not be studied before.2 While attosecond science spans the extreme ultraviolet and soft X-ray regimes rather than the hard X-ray wavelengths used in conventional XRF and XRD, it illustrates the same underlying principle that drives the more applied instrumentation discussed in this article: X-ray and X-ray-adjacent techniques remain among the most direct experimental windows into atomic and electronic structure available to science.
This article surveys recent developments across XRF, XRD, and XPS instrumentation, drawing on recent peer-reviewed literature, patents, and vendor and researcher commentary.
One of the most significant recent shifts in X-ray fluorescence has been the migration of microbeam capability, with spot sizes once achievable only at synchrotron beamlines, onto commercial laboratory instruments. Researchers including Wright, Zierden, Kolomyjec, and Southwell have documented emerging laboratory microbeam XRF (μXRF) systems that use polycapillary or doubly curved crystal optics to achieve spot sizes of approximately 3 to 30 μm, enabling applications such as elemental mapping of intact plant tissue, food adulteration screening, packaging migration studies, and biofortification tracking that previously required synchrotron facility access.3
Portable XRF instrumentation has undergone a parallel transformation, moving from a purely qualitative screening role toward regulatory-grade quantitative analysis. Patrick J. Parsons of the New York State Department of Health's Wadsworth Center and colleagues evaluated two second-generation XRF analyzers equipped with doubly curved crystal (DCC) optics, measuring certified reference materials and Food Emergency Response Network (FERN) 2022 proficiency test samples. The analyzers showed strong agreement for lead, cadmium, arsenic, and mercury in food matrices, supporting portable DCC-optic XRF as a viable frontline screening platform for heavy-metal contamination, although matrix complexity, moisture, and the need for matrix-matched calibration remain challenges.3 Handheld instrument manufacturers, including Thermo Fisher Scientific with its Niton product line, continue to prioritize more powerful miniaturized X-ray tubes, faster silicon drift detectors, and improved ergonomics.3
Instrument manufacturers are also targeting the middle ground between handheld and traditional floor-standing wavelength-dispersive XRF (WDXRF) systems. Malvern Panalytical's Revontium compact XRF spectrometer, launched in April 2024, is reported by the company to deliver results comparable to those of larger floor-standing systems while consuming approximately 250 W, compared with roughly 2,000 W for traditional WDXRF instruments, at a cost of ownership more than 25% lower than that of WDXRF, atomic absorption spectroscopy (AAS), and inductively coupled plasma (ICP) instruments.4
“Revontium provides our customers with powerful elemental analysis in a compact format.”
— Lieven Kempenaers, Product Manager XRF, Malvern Panalytical
“Revontium expands our comprehensive offering in the XRF instrument market.”
— Mark Fleiner, President, Malvern Panalytical
Speed, not just sensitivity, has become a key differentiator in portable XRF technology, particularly for industrial alloy sorting and scrap metal recycling applications where throughput directly determines economic viability. A patent held by SciAps Inc., invented by Donald W. Sackett, describes a handheld XRF analyzer with a dedicated aluminum alloy algorithm that operates at optimized tube voltage and current settings, roughly 10 to 11 kV and 300 to 500 μA, specifically tuned for detecting magnesium, silicon, and the transition metal alloying elements common in aluminum alloys.5 Rather than following the industry-standard dual-beam measurement approach, in which sequential high- and low-voltage tests typically require 10 to 60 seconds per sample, this optimized single-beam protocol delivers results in approximately 1 to 4 seconds for nearly 90% of aluminum alloys, automatically switching to a higher-voltage, lower-current measurement only when the initial fast scan fails to detect the expected alloying elements or cannot definitively distinguish between similar alloy compositions.5 This kind of adaptive, application-specific measurement protocol illustrates how portable XRF performance gains increasingly come from smarter measurement algorithms rather than purely from detector or X-ray tube hardware improvements.
As with other spectroscopic techniques covered in this series, artificial intelligence is increasingly embedded directly into X-ray instrumentation rather than applied only in postacquisition data analysis. The XOS portable energy-dispersive XRF (EDXRF) system is described as integrating real-time, AI-assisted spectral interpretation, together with sulfur analysis per ASTM D4294 and measurement of up to 12 elements from phosphorus through zinc at sub-ppm levels, reducing the need for operators to manually interpret overlapping elemental peaks during field deployments.3 Complementing this trend, Andric and colleagues used an autoencoder neural network to compress raw EDXRF spectra collected from canvas paintings into a small set of informative features, enabling efficient classification of the paintings by period of creation while reducing processing time and manual expert work compared with conventional multivariate data reduction.6
X-ray diffraction continues to serve as the definitive technique for crystalline phase identification across remarkably disparate applications. In forensic science, Estevanes, Jernigan, Zall, and Monjardez combined confocal Raman microscopy with single-crystal XRD to characterize ammonium nitrate residues in ammonium nitrate fuel oil (ANFO) recovered from postblast substrates after the controlled detonation of two improvised explosive devices. The lattice-vibration spectra of the postblast residues differed from those of intact ammonium nitrate, a change the authors attributed to thermally driven phase transitions, and single-crystal XRD independently confirmed the structural changes. Although the authors describe the work as a fundamental study of ammonium nitrate phase behavior rather than a forensic application per se, it has clear implications for reconstructing explosive device composition and detonation conditions in postblast investigations.3
In planetary science, powder XRD confirmed the characteristic aubrite mineral assemblage (enstatite, olivine, plagioclase feldspar, metal sulfides, and iron oxides) in the Tiglit meteorite, which fell in Morocco in 2021, while complementary Raman spectroscopy and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM–EDS) also revealed unexpected phases, including calcite (attributed to terrestrial weathering), the high-temperature silica polymorphs quartz and cristobalite, and carbon phases. Together, these findings broaden the recognized compositional range of aubrite-class meteorites and point to a more complex formation and alteration history than previously attributed to this meteorite class.3
X-ray photoelectron spectroscopy (XPS) continues to advance as the definitive surface-sensitive elemental and chemical-state characterization technique. Wang, Mueller, and Crumlin proposed three more reliable alternatives to the common practice of assigning the O 1s feature near 531–532 eV directly to oxygen vacancies in XPS spectra of metal oxides: monitoring changes in cation valence state, evaluating normalized surface oxygen-to-cation stoichiometry, and tracking binding-energy shifts attributable to electron doping, thereby addressing a long-standing ambiguity in the interpretation of oxide surface chemistry.3
In battery research, XPS provides surface-sensitive characterization of the solid electrolyte interphase (SEI) in lithium-ion batteries, including composition, oxidation-state mapping, and degradation product identification, complementing the bulk elemental and structural information that XRF and XRD provide and giving battery researchers a more complete, multitechnique picture of electrode degradation mechanisms over repeated charge–discharge cycling.3
X-ray analytical techniques are advancing along several parallel tracks simultaneously: microbeam XRF is democratizing spatial resolution once exclusive to synchrotron facilities, compact and portable instruments are extending quantitative, regulatory-grade analysis into field and point-of-need settings, and artificial intelligence is beginning to automate spectral interpretation directly at the point of acquisition rather than requiring specialized postprocessing expertise. Across XRD forensic and planetary applications and XPS battery and surface-chemistry research, these techniques continue to provide structural and chemical-state information unobtainable by vibrational or electronic spectroscopy alone.
The 2023 Nobel Prize in Physics for attosecond pulse generation, while methodologically distinct from routine XRF, XRD, and XPS analysis, underscores the continued fundamental importance of X-ray and X-ray-adjacent electromagnetic radiation to probing atomic and electronic structure, connecting applied analytical instrumentation to the frontiers of basic physics research.
From attosecond-scale fundamental physics to seconds-scale alloy sorting on a factory floor, the X-ray techniques surveyed here share a common thread: each advance trades some dimension of measurement, whether spatial resolution, acquisition speed, instrument footprint, or required operator expertise, for broader practical deployability without sacrificing the elemental and structural specificity that has always been X-ray analysis's defining strength.
Expect continued miniaturization and cost reduction in both XRF and XRD instrumentation, following the trajectory demonstrated by Malvern Panalytical's Revontium system, as vendors compete to bring floor-standing-equivalent performance to compact, lower-power benchtop and portable form factors. AI-assisted spectral interpretation, already deployed in field-portable EDXRF systems, is likely to expand further into XRD phase identification and XPS peak deconvolution, reducing the specialized expertise historically required to interpret complex, overlapping X-ray spectral data.
Microbeam XRF's continued commercialization should further expand access to spatially resolved elemental mapping for agricultural, food safety, and packaging applications, narrowing the capability gap between synchrotron facilities and laboratory benchtop instrumentation. Meanwhile, as battery and semiconductor research continues to demand ever more precise surface and interfacial characterization, expect deeper integration between XPS, XRF, and complementary techniques such as Raman and infrared microspectroscopy, discussed elsewhere in this series, within unified multimodal characterization platforms rather than as separate, sequentially operated instruments.
As Nobel-recognized attosecond techniques continue to mature within fundamental physics research, expect gradual translation of some underlying ultrafast X-ray methodologies toward specialized time-resolved materials characterization applications, further narrowing the historical divide between basic physics instrumentation and applied analytical chemistry.
(1) The Royal Swedish Academy of Sciences. Electrons in Pulses of Light: Popular Science Background, The Nobel Prize in Physics 2023. NobelPrize.org, October 2023.
(2) American Physical Society. Nobel Prize in Physics Awarded for Techniques to Produce Ultrashort Pulses of Light. APS News, October 3, 2023.
(3) Workman, J., Jr.; Wetzel, W. X-Ray Spectroscopy Analysis: Techniques and Applications Across Science and Industry. Spectroscopy Online, July 6, 2026.
(4) Malvern Panalytical Launches Compact XRF Spectrometer. Metal AM, April 10, 2024.
(5) Sackett, D. W. Portable, Hand Held Aluminum Alloy XRF Analyzer and Method. US 11 169 100 B2, 2021.
(6) Andric, V.; Kvascev, G.; Cvetanovic, M.; et al. Deep Learning Assisted XRF Spectra Classification. Sci. Rep. 2024, 14 (1), 3666. DOI:
