Extreme Temperature Sample Environment for Materials Research using Neutron Scattering
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47 OTHER INSTRUMENTATION↗
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Lattice Structured Lightweight Structural Materials
The development of lightweight structural materials is crucial for enhancing the performance and deployment feasibility of fission batteries. This study aims to produce lightweight structural materials whose strength-to-weight ratios exceed those of current widely used structural materials. To achieve this, advanced modeling and simulation tools were employed to design lattice structures with different lattice parameters and different lattice types. A process was successfully developed for transforming lattice-structured models into Multiphysics Object Oriented Simulation Environment (MOOSE) inputs. Finite element modeling (FEM) was used to simulate the uniaxial tensile testing of the lattice-structured parts to investigate the stress distribution at a given displacement. The modeling results showed that the lattice-structured sample displayed a lower Young’s modulus in comparison to the solid material; the increase in solid shell thickness and blend radius enhances the mechanical performance; and the effect of unit cell size on macro scale stress is minimal. Tensile testing was conducted on the solid and lattice-structured materials fabricated by laser powder bed fusion (LPBF) additive manufacturing. The experimental results agreed well with the model prediction. The approach of using modeling as a guiding tool for preliminary material design can significantly save time and cost for new material development.
lightweight material↗
LBNF Material Activation Study
This poster presents a study on material activation at the Fermilab Long-Baseline Neutrino Facility (LBNF), focusing on understanding how high-energy beams interact with surrounding materials to produce radioactive isotopes. Using a simplified model of the LBNF-20 bunker and the FLUKA Monte Carlo simulation tool, the project quantifies isotope production at key locations inside and outside the shielding structures. The study employs a two-step simulation process, tracking primary and secondary particles, and explores the impact of different geometric and material configurations on activation rates. Results include preliminary comparisons of simulated activation rates and suggest methods for refining geometry, improving accuracy, and efficiently simulating future configurations. This work informs the design and operation of the LBNF facility, aiding in radiation safety and shielding optimization. Future efforts will focus on sample iteration, automated simulations, and detailed residual isotope analyses to further enhance the understanding of material activation in high-energy physics environments.
Pietraszek, Jacob [Unlisted, US]↗
Discerning the effect of various irradiation modes on the corrosion of Zircaloy-4
Using proton irradiation, this study investigates the individual influence of several factors on the corrosion kinetics of Zircaloy-4 in a hydrogenated water environment simulating a Pressurized Water Reactor (PWR). Using both simultaneous irradiation-corrosion and autoclave corrosion, we separately examine: (i) the effect of pre-irradiation on modifying the structure of the material, (ii) the impact of irradiation on creating defects in the growing oxide layer during corrosion, and (iii) the influence of irradiation on increasing the corrosion potential through radiolysis during corrosion. To replicate neutron-irradiated microstructure, two proton pre-irradiation schedules were employed: Schedule 1 (isothermal irradiation at 350 °C to 5 dpa) to simulate high-temperature PWR conditions, and Schedule 2 (two-step process: irradiation to 2.5 dpa at -10 °C followed by 2.5 dpa at 350 °C) to simulate lower temperature PWR and Boiling Water Reactor (BWR) conditions. Long-term autoclave corrosion testing over 360 days at 320 °C revealed no significant difference between unirradiated samples and those pre-irradiated according to either schedule, with all samples exhibiting sub-cubic kinetics within the pre-transition regime. Irradiated samples underwent Simultaneous Irradiation Corrosion (SIC) tests, corroding in 320 °C water while being irradiated with protons. Corrosion was found to accelerate in all SIC-tested samples relative to autoclave conditions, with the greatest increase observed in non-pre-irradiated samples. Pre-irradiation with either schedule resulted in a slower corrosion rate compared to non-pre-irradiated regions under SIC conditions. The degree of radiolysis observed in the SIC tests surpassed typical PWR conditions, approaching levels found in BWRs. Radiolysis products were identified as the primary contributors to accelerated corrosion, corroborated by radiolysis bar tests. Furthermore, these findings underscore the intricate interactions between irradiation, corrosion, and water chemistry in determining Zircaloy-4 corrosion kinetics within nuclear reactor environments.
Pressurized Water Reactor↗
Structure-property relationships and recyclability of natural fiber insulation
Thermal insulation can significantly decrease the energy required to maintain internal temperatures within buildings, saving money and decreasing environmental impacts. Many industrially available insulation options utilize petroleum-derived materials, such as polyurethane, fiberglass, and polystyrene, which can be detrimental to the environment by emitting greenhouse gases during production. A sustainable alternative to these synthetic insulations is the use of renewable feedstocks to produce comparable insulation products. In this work, natural fibers such as flax and banana were bound with an epoxy system to produce natural-fiber insulation. Key experimental parameters for this study included evaluating the fiber lengths and processing methods while varying the viscosity of the epoxy binder solvent dissolution. Insulation performance was determined by comparing density, thermal conductivity, resilience, and compressive strength; these parameters were tested to further understand and investigate the structure- property relationships within the system and to understand how these composites compare to current commercial materials. Furthermore, from this characterization and optimization, a natural fiber sample with an R/in. >5 was achieved, making these materials competitive with commercial alternatives.
Compressive strength↗
Model Parameter Development for Complex Materials: Species-Specific Diffusion Barriers in 316 Stainless Steel from Systematic DFT Calculations
Vacancy-mediated diffusion barriers in 316 stainless steel have been systematically calculated using density functional theory to provide essential parameters for mesoscale microstructure evolution models. A statistical sampling approach employing 210 nudged elastic band calculations across multiple special quasi-random structures captures the effects of local chemical environments in this concentrated alloy. The computational methodology addresses challenges specific to chemically disordered systems, including proper magnetic treatment throughout multi-step calculations and validation against experimental structural properties. The calculated activation barriers reveal clear species-dependent diffusion behavior with the hierarchy Ni >> Fe ˜ Cr >> Mo. Nickel exhibits the highest barriers (0.74–1.31 eV, mean 1.045 eV), confirming its role as the slowest-diffusing major component. Iron and chromium show similar moderate barriers averaging 0.587 eV and 0.522 eV, respectively. Remarkably, molybdenum demonstrates exceptionally low barriers (0.12–0.28 eV, mean 0.194 eV), suggesting much higher mobility than previously recognized and potentially significant implications for precipitation kinetics and microstructure evolution. The barrier ranges remain consistent across different 316 SS compositions, supporting parameter transferability for modeling applications. The overall mean barrier of 0.64 eV provides a practical approximation for phase field simulations, while species-specific values enable detailed treatments of diffusion-controlled processes. This systematic approach establishes a validated framework for generating diffusion parameters in other concentrated alloys where experimental data are limited, while providing the first systematic set of species-specific barriers for predictive modeling of 316 stainless steel microstructure evolution.
36 MATERIALS SCIENCE↗
Machine learning enables reconstruction of past fire regimes from charcoal-derived fire intensity and fuel composition
Background Fire is a foundational ecological process that shapes ecosystem structure, diversity, and resilience. Quantifying paleofire regime attributes such as frequency, severity, and intensity is essential for understanding the historical range of variability in fire behavior and its ecological effects. While frequency and severity are often reconstructed in paleofire studies, quantitative reconstructions of fire intensity remain limited. Recent work has shown that maximum pyrolysis temperature—a proxy for fire intensity—and plant species type can be inferred from charcoal using transmission Fourier-transform infrared (FTIR) spectroscopy. However, the sample preparation for transmission FTIR is destructive and time-consuming, limiting application and reuse of materials for other analyses. We evaluated reflectance FTIR spectroscopy as a non-destructive alternative for reconstructing combustion temperature and plant species from laboratory-generated charcoal. We also examined the influence of contrasting airflow environments (ambient air versus nitrogen-rich) on pyrolysis temperature and plant species reconstruction prediction accuracies and compared predictive performance between a novel, neural network–based deep learning model with the traditional modern analogue technique (MAT) using k-nearest neighbor functions. As proof of concept, we apply our enhanced methodology to ancient charcoal to demonstrate applicability at improving long-term fire regime reconstructions and the ability to link paleofire records with contemporary fire ecology. Results Our analysis shows that transmission and reflectance FTIR spectra yield comparable spectral profiles. However, sample preparation for reflectance FTIR is minimal and non-destructive, unlike transmission FTIR which is destructive. We demonstrate that oxygen environments improved reconstruction accuracy relative to nitrogen-rich conditions. Finally, our deep learning neural network (DL) achieved testing accuracies of 98.7% for temperature and 96.2% for species identification, outperforming MAT’s k-NN approach (89.8% and 65.9%, respectively). A Shapley importance analysis identified 5 key spectral regions that greatly influenced the model’s temperature or species categorization. When applied to ancient charcoal, our results show historic fires from the most recent past primarily burned at low intensities (400–500 °C), reflective of natural fire regimes in ponderosa pine forests. Our results corroborate charcoal morphology data that suggests all ancient charcoal originated from burned woody plant types. Conclusions By combining reflectance FTIR spectroscopy with a deep learning approach, we provide the first accuracies high enough to confidently identify both species and temperature from laboratory-produced charcoal, improving quantitative reconstructions of fire intensity and fuel composition from paleofire records. This opens a wide range of research into the link between fire and larger drivers (i.e., climate or human) and greater ecological understanding of fire regimes beyond that of burn scars or recent observations. These methodological improvements have direct relevance for fire management by improving interpretation of historical fire behavior, informing fuel–fire relationships, and providing a scalable analytical framework applicable to both long-term ecological studies and contemporary fire science.
54 ENVIRONMENTAL SCIENCES↗
Irradiation Testing of Additively Manufactured Materials for LWR Applications: Alloy 718 and 316L Stainless Steel
The objective of this NSUF Project is to assess the changes in irradiated additively manufactured (AM) material properties as compared to non-irradiated material. Type 316L stainless steel and Alloy 718 samples were produced using Direct Metal Laser Melting (DMLM) fabrication. Materials produced from this fabrication method have several potential applications within the nuclear industry as reactor internal repair parts, fuel debris resistant filters, or fuel spacers within existing light water reactors (LWRs). AM materials have been shown to achieve equivalent mechanical behavior in simulated reactor environments as compared to wrought materials, but have significantly more flexibility when it comes to unique design features. The increased component design flexibility makes these AM materials an attractive choice for both current LWR applications as well as for small modular reactor (SMR) designs. Prior to use of these materials in reactor fleet operation, the industry as a whole must evaluate the effects of irradiation on their material properties. Standard 0.4 inch thick Compact Tension specimens and SSJ3 type tensile bars were neutron irradiated at the Advanced Test Reactor to ~1 dpa for the purpose of performing a variety of mechanical tests in a range of simulated environments applicable to LWRs. For the ductile austenitic Type 316L stainless steel, the irradiated data will be used to confirm that the AM process produces materials with properties that are equivalent to wrought materials under testing conditions applicable to LWR operation. Transmission electron microscopy analysis was also performed in order to understand microstructural and microchemical changes induced in each material in response to neutron irradiation. If possible, data collected from these AM 316L samples will be used to remove fluence limits from specifications of ASME code cases for this alloy, which will give vendors much more flexibility in building future components.
36 - MATERIALS SCIENCE↗
Molten Salt Corrosion Tests of Additively Manufactured Stainless Steel 316H
Molten salt reactors (MSRs) have drawn considerable interest due to their favorable safety features, high thermal efficiency, and compatibility with different fuel cycles. Yet, the success of MSRs hinges critically on the performance of structural materials to be used in these aggressive molten salt environments, where corrosion and material compatibility remain primary challenges to long-term reliability. Additively manufactured (AM) nuclear structural materials prompt the use of novel geometries and compositions to enhance material performance and reduce costs of constructing MSRs. The rapid solidification conditions inherent to AM processing impart distinctive microstructural features, including cellular sub-structures, dislocation densities, residual stress, and oxide inclusions, which can influence material performance in MSR components. While the mechanical properties of AM stainless steels have been widely studied, their corrosion behavior, particularly in molten salt environments, has received far less attention. Addressing these needs, the Advanced Materials and Manufacturing Technologies (AMMT) program provides a framework for systematically evaluating how unique microstructures produced by AM processes influence the performance of these materials in these demanding environments and for developing reproducible testing workflows that can support future code qualification efforts and standards development. Bridging this knowledge gap is essential for assessing the viability of AM alloys in MSRs and informing qualification strategies. A further challenge is the absence of standardized protocols for molten salt corrosion testing. Accordingly, this report provides an account of the corrosion evaluation of AM 316H stainless steel in NaCl 2 -MgCl 2 molten salt at 550 °C, with exposure times of 100 and 500 hours. It documents the experimental procedures implemented under the AMMT program, including salt preparation, exposure protocols, and post-test characterization methods, to establish reproducibility and transparency. Importantly, the study examines AM 316H samples in the as-fabricated condition, directly reflecting the surface state most relevant to engineering applications, and compares their behavior to machine-cut surfaces. Overall, preliminary evaluations have noted that surface conditions (e.g. morphology, contamination, etc.) have a noticeable impact on the corrosion resiliency. The impact of the corrosion is difficult to detect at 100h, unless, in the case of AM 316H, the specimen surface is decontaminated. After 500 h, as-fabricated surfaces of AM and wrought 316H display evidence of general versus preferential corrosion attack, respectively. Both AM and wrought 316H machine-cut surfaces exhibit a continuous Cr depletion zone, evident of general corrosion. While the estimated extent of corrosion appears within the same order of magnitude regardless of the surface condition, it is apparent that more predictable behavior is observed on machine-cut surfaces. Nonetheless, further investigation is necessary to fully elucidate the corrosion mechanism under these conditions.
36 - MATERIALS SCIENCE↗
Mitigate Stress Corrosion Cracking (SCC) in High-strength Al Castings (CRADA 508)
Development of high strength, corrosion resistant, aluminum alloys is important to a number of industries, including automotive, aerospace, power, etc. Understanding how processing methods such as casting, chemistry, additives and recycle content affect an alloy’s microstructure and performance in real world environments is crucial to their implementation. One major concern is how the repeated exposure to saline solutions can be detrimental to the structural integrity of aluminum alloy components. This study was undertaken as a collaboration between Eck Industries and PNNL to investigate the stress corrosion cracking (SCC) and corrosion behavior of aluminum alloys. This work examines the SCC response of three different materials set. First is cast A206 Al alloy produced by three different casting techniques (i.e., permanent mold, and sand casting with and without chill) and with or without nano forming additives as potential grain refiners. The second materials set comprises Al alloy tubes extruded by PNNL’s ShAPE process and produced using raw material with three different ratios of twitch and pre-consumer Al 6061 scrap. Finally, the third materials set comprises two Al-Si-Mg-Fe alloys with controlled Si% and Fe% to mimic recycle grade Al alloys. The corrosion behavior of these materials was characterized using SCC testing and scanning electrochemical cell microscopy (SECCM) technique; mechanical behavior was characterized using tensile testing, and microstructure was characterized using optical and electron microscopy techniques. Of the A206 Al alloys, the samples with nano forming additives performed poorly compared to those without. The best SCC performance (i.e. least degradation in mechanical properties) was demonstrated by A206 alloy sand cast with chill while the permanent mold sample showed the worst SCC performance. The SECCM was performed on A206 alloy sand cast with chill to understand the effect of microstructure and determine the location-dependent corrosion properties on grain boundaries (GB) and inside grains (IG) on the sample. The corrosion potential and current measured on GB and IG at various points on the sample established that the GB locations are more cathodic and corrosive than the IG locations. Further work needs to be conducted to investigate the mechanisms behind the observed dependence of SCC on casting technique and nano forming additives. Among the ShAPE extruded tubes, SECCM data suggests the decreasing order of tendency to corrode as follows: 100% twitch > 75% twitch > 50% twitch. Finally, both variants of the Al-Si-Mg-Fe alloys showed a large volume fraction of Fe- and Si-containing intermetallics and additional work is needed to discern differences in their respective corrosion responses. In summary, correlating local electrochemical behavior (e.g. via SECCM) with micro/nanostructure, in conjunction with bulk mechanical behavior, can help identify the right fabrication method and alloy chemistry to minimize SCC issues in Al alloys.
36 MATERIALS SCIENCE↗
Investigating the Effect of Water on the Mechanical Properties of Cellulose from Multiscale Molecular Dynamics Simulations
Classical molecular dynamics (MD) simulations provide insight into the structure and physicochemical properties of materials with atomic resolution. However, the length and time scales accessible to atomistic MD are orders of magnitude smaller than many relevant processes such as the response of a bulk material to experimentally accessible strain rates, which presents challenges when comparing models to experimental measurements. Bottom-up coarse-graining provides a means for systematically mapping atomistic information to lower resolution models to increase the length and time scales achievable by simulation. Cellulose is an abundant carbohydrate biopolymer with applications to many fields of research, such as materials science and renewable energy, due to its desirable mechanical properties and viability for conversion into biofuel. The effect of moisture content on the Young's modulus of cellulose is of special interest due to its native environment often being in the hydrated secondary plant cell wall and the grinding energy requirements for biomass feedstock preprocessing. The current work investigates the effects of water solvent on the Young's modulus of cellulose calculated from coarse-grained MD mechanical stress simulations. The coarse-grained model was parametrized from atomistic MD calculations of cellulose-cellulose potentials of mean force using umbrella sampling techniques under vacuum and solvated conditions. The Young's moduli of the coarse-grained cellulose assemblies parametrized from cellulose in vacuum or solvated in water were computed via mechanical stress simulations to highlight the importance of capturing solvent interactions for modeling the mechanical behavior of cellulose.
BASIC BIOLOGICAL SCIENCES,RADIATION PROTECTION AND↗
Single Crystalline Na 0.67 Ni 0.33 Mn 0.67 O 2 Positive Electrode Material via Molten Salt Synthesis for Sodium Ion Batteries
P2-layered Na 0.67 Ni 0.33 Mn 0.67 O 2 (NNMO) has emerged as a promising positive electrode material for sodium ion batteries due to its appealing electrochemical properties. Synthesis of polycrystalline NNMO (PC-NNMO) materials through conventional calcination of solid precursors remains the prevailing method, where heating occurs in a dry environment with air or O 2 . On the other hand, the molten salt method, where precursors are submerged in molten salt medium during calcination, emerged in recent years to be a scalable technique for more controlled crystal growth and uniform morphology in a variety of materials. Here, we utilize the molten salt method to synthesize single crystalline NNMO (SC-NNMO) materials with enhanced electrochemical properties. The SC-NNMO material exhibits an initial specific discharge capacity of 95 mAh g –1 at a 0.1C rate, retaining approximately 88.5% of its capacity after 100 cycles over a wide voltage range of 2.0–4.2 V. Furthermore, SC-NNMO maintains a capacity retention of 83.9% after 300 cycles at a 1C rate compared to 66.6% for PC-NNMO, indicating excellent long-term cycling stability. This stability is further confirmed by the performance of an SC-NNMO//hard carbon full cell, which retains 90.3% of its capacity after 200 cycles at 1C within a voltage window of 1.9–4.1 V. The enhancement in stability of the SC-NNMO sample is attributed to the single crystalline structure suppressing the undesired P2–O2 phase transition at high voltage. This study also presents an easy, efficient, and straightforward molten salt process for SC-NNMO material synthesis, offering valuable insights into the potential application of such methodology for the large-scale, cost-effective production of various sodium-layered transition metal oxide positive electrode materials for SIBs.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Integrated process-structure–property–performance optimization of cold-sprayed zinc coatings on AZ91 magnesium alloy
High-pressure die cast (HPDC) AZ91 magnesium alloy is widely used in automotive components such as transmission housings and brackets for its excellent strength-to-weight ratio. Zinc-based cold spray coatings can be applied selectively to vulnerable areas to enhance corrosion resistance, minimize galvanic coupling with dissimilar metals, and eliminate the need for full-surface oxide coatings, making the process more efficient and targeted. A comprehensive evaluation of 16 combinations of nitrogen carrier gas temperatures and pressures led to the identification of an optimal range of process parameters, yielding Zn coatings with porosity <0.5 % by area, wear rates reduced by a factor of two compared to uncoated AZ91, and adhesion strengths up to 35 MPa. The enhanced mechanical performance of the coating is attributed to the low porosity and the formation of a metallurgical bond at the coating-substrate interface. Corrosion studies using macroscale potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) revealed a significant decrease in corrosion rate and a shift to more noble corrosion potentials (ZCP) for coated substrates. Furthermore, the Zn cold-sprayed samples exhibited significantly lower corrosion-induced evolved hydrogen content compared to the base AZ91 substrate and AZ91 coated with industrial coatings, demonstrating that the Zn layer effectively protects the substrate from the corrosive environment. Overall, cold spray Zn coatings significantly improve the mechanical and corrosion performance of AZ91 Mg alloys, addressing key material challenges and enabling their broader use in automotive applications.
Corrosion performance↗
Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation
Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.
Bayesian optimization↗
Chemical Imaging of Atmospheric Biomass Burning Particles from North American Wildfires
The effects of biomass burning aerosols (BBA) on radiative forcing and cloud formation depend on chemical composition and the internal structures of individual particles within smoke plumes. To improve our understanding of the chemical and physical properties of BBA emitted at different times of the day and their evolution during atmospheric aging, we conducted a study as a part of the Fire Influence on Regional to Global Environments and Air Quality field campaign. Particle samples were collected onboard a research aircraft from smoke plumes from a wildfire in eastern Oregon during late afternoon and nighttime flights on August 28, 2019. A time-resolved aerosol collector was used to collect samples on substrates for offline spectromicroscopic imaging to investigate the single-particle characteristics of BBA particles. Approximately 20,400 individual particles from 10 selected samples were analyzed using computer-controlled scanning electron microscopy coupled with energy-dispersive X-ray microanalysis, revealing their elemental composition, morphology, and viscosity. Elemental microanalysis indicated that aged potassium is likely found in the form of K 2 SO 4 , KNO 3 , and possible K-organic salts. Further chemical speciation and carbon bonding mapping within individual particles were conducted using synchrotron-based scanning transmission X-ray microscopy (STXM) coupled with near edge X-ray absorption fine structure (NEXAFS) spectroscopy. Real-time, water-soluble light absorption measurements were acquired using a particle-into-liquid sampler instrument coupled to a liquid waveguide capillary cell and total organic analyzer. In the late afternoon samples, 65% of the total particle number population consisted entirely of organic components, compared to 46% in the nighttime particles. These differences were attributed to discrepancies in composition at the time of emission and to the daytime condensation and accumulation of photochemically formed secondary organic material on existing BBA particles, a process that halts at night. Microscopy images indicated that particle viscosity was lower in the nighttime particles (<10 1 Pa·s), likely due to increased relative humidity and a higher contribution from hygroscopic inorganic components. The chemical heterogeneity of individual particles was quantified using STXM-derived mixing state parameters. The nature of carbon bonding within individual particles was inferred from the extent of carbon sp 2 hybridization derived from NEXAFS spectra. Average percentages of sp 2 hybridization range between 40% and 60%, with no noticeable differences between late afternoon and nighttime flights. These findings were compared with the online optical properties of both late afternoon and nighttime smoke plumes, providing valuable insights into the complex relationship between chemical composition and optical properties of BBA particles at different times of the day.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
In situ high-temperature Raman spectroscopy for online EAF slag analysis
Real-time monitoring of slag chemistry is critical for optimizing Electric Arc Furnace (EAF) steelmaking operations, where dynamic variations in slag composition directly influence slag foaming, refractory degradation, and thermal efficiency. Conventional techniques such as X-ray fluorescence (XRF), Fourier-transform infrared (FTIR), and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) are commonly used to analyze slag composition, but their offline nature and equipment constraints limit their applicability for online monitoring in harsh industrial environments. To address this challenge, we present an in situ, high-temperature analytical approach that integrates Raman spectroscopy with a custom designed fiber-optic probe for real-time slag characterization at 1550 °C. The system enables non destructive spectral acquisition from molten slags, providing molecular-level insights into silicate polymerization and iron oxidation states. Eight synthetic slag samples were evaluated, and key Raman features—such as Q n silicate units and FeO₄/FeO₆ coordination environments—were identified and quantitatively correlated with slag basicity and Fe₂O₃ content. The results demonstrate agreement between Raman spectral ratios and bulk slag chemistry, validating the method’s capability to track compositional and structural changes under molten temperature. This work establishes the feasibility of deploying fiber-optic Raman probe for online EAF slag monitoring and highlights their potential to support closed-loop control strategies, thereby enhancing process stability, refractory protection, and steel quality in industrial steelmaking applications.
47 OTHER INSTRUMENTATION↗
Metrology for femtosecond pulsed x-ray heating in diamond anvil cell experiments at the European XFEL: Revisiting the iron phase diagram up to 150 GPa
The development of pulsed intense x-ray sources, such as free electron laser, offers new avenues for high pressure experiments. Here, we study the feasibility and metrology of x-ray heating in diamond anvil cells at the European x-ray free electron laser. This method enables one to volumetrically heat the sample while inhibiting chemical migration and probing the crystallographic structure of the sample throughout the heating with a high repetition rate. We focus our study on iron, whose phase diagram is well established up to 100 GPa, to explore the possibilities and limitations of this technique. We volumetrically heat iron samples at starting pressures ranging from 10 to 138 GPa, using the x-ray beam pulsed at 4.5 MHz in a serial pump-and-probe experimental design. Experimental challenges arise from temperature gradients within the sample, changes in temperature at the 100 ns timescale, the difficulty of direct temperature estimates, the effect of thermal pressure, and the presence of metastable crystallites due to rapid cycles of heating and cooling. Hence, we develop a multi-crystal-like data processing method that allows us to account for sample heterogeneity in probed conditions. We then calibrate our measurements using known physical properties of iron under pressure. Thermal pressure in our experiments increases from 4% of the isochoric prediction at 10 GPa to 23% at 138 GPa, and we show that our data are in agreement with most previous observations of iron in this pressure range. The method can now be implemented at higher pressures and temperatures and on materials with unknown phase diagrams.
Materials science↗
Evolution of size-selected Pt cluster catalysts on prototypical oxide supports
The current quest for new pathways into sustainable, efficient and durable energy conversion technologies makes the used for a fundamental understanding of the atomic-scale phenomena underlying catalytic processes ever more pressing. In this context, characterizing catalyst particles in situ provides valuable information about the evolution of their composition, structure, oxidation state and charge state during an ongoing process. To disentangle the influence of individual parameters – temperature, pressure, gas composition, cluster size, as well as support acidity, redox state and defect density – it is crucial to control them precisely and separately in experiments. At the example of size-selected Pt n clusters – i.e. sub-nm particles defined to the exact number of atoms – on flat oxide supports, we follow their rich evolution phenomena via (synchrotron-based) X-ray photoelectron spectroscopy (XPS) and scanning tunnelling microscopy (STM) during temperature ramps and in various gas environments. Here, we present our experience with these highly defined, yet complicated-to-create samples in ultra-high vacuum (UHV) and at mbar pressures. We discuss their stability during transport to synchrotrons and under reaction conditions on three prototypical oxide supports and show the various phenomena that can be disentangled. Ptn clusters on the non-reducible silicon dioxide, SiO 2 , remain size-selected and show remarkable stability. Doping strongly influences the size-dependent binding energy shifts and changes the Pt response to oxidative and reaction conditions, which we attribute to different cluster geometries: p-type doping leads to wetting, enhanced sinter resistance and a diminished response to oxidative environments, compared to clusters on n-type doped samples. We compare the system with our previous findings of a similar change in dimensionality for Pt 20 clusters on the reducible ceria, CeO 2 (111), support, induced by modulation of its O vacancy density. The Pt n /CeO 2 system is particularly interesting for strategies to stabilize and redisperse Pt clusters dynamically. Finally, we study the evolution of Pt n clusters on another reducible oxide support, magnetite, Fe 3 O 4 (001), in 0.1 mbar alternating redox conditions at RT and elevated temperature. In analogy to findings previously reported for Pt/TiO 2 (110), the clusters either become encapsulated by a thin oxide film via strong metal–support interaction (SMSI) or deeply buried in the magnetite. Overall, our approach of following the evolution of size-selected clusters on oxide supports leads to fundamental atomic-scale insights on nano-scale catalyst materials, on our path to sustainable, dynamic and self-repairing catalysts.
Falling, Lorenz J. [Technical Univ. of Munich (Ger↗