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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Comprehensive assessment of deep reinforcement learning approaches for economic dispatch in nuclear-driven microgrids

As the electrical grid integrates more variable renewable energy sources such as wind and solar, the demand for distributed and flexible systems to address this increased variability becomes critical. Nuclear-driven microgrids provide a promising solution by offering stable generation to complement intermittent renewables, ensuring grid reliability and operating efficiency. This paper proposes a recurrent deep reinforcement learning framework for optimal economic dispatch in a nuclear-powered microgrid integrating renewable energy sources, small modular reactors, battery storage systems, and balance-of-plant dynamics. A three-agent control architecture is developed, where demand and renewable energy agents act as forecasters, and a reinforcement learning-based dispatch agent performs real-time energy allocation. A nonlinear programming formulation is first used to generate an optimal baseline for benchmarking. The proposed dispatch controller, based on Proximal Policy Optimization enhanced with Long Short-Term Memory networks, exploits temporal correlations in system dynamics by taking advantage of the time series used as inputs to improve policy robustness under uncertainty. Comparative analysis against established deep reinforcement learning methods, including Proximal Policy Optimization with a feedforward architecture, Soft Actor-Critic, and Twin Delayed Deep Deterministic Policy Gradient, demonstrates superior performance. Numerical results indicate that the proposed controller achieves a 0.39% cost reduction relative to the nonlinear programming benchmark and outperforms other learning-based methods by generating additional revenue of up to 0.35%. All reinforcement learning controllers compute dispatch actions in less than 0.3 s, resulting in a computational speedup of more than three orders of magnitude over the nonlinear programming baseline. The findings of this paper highlight their applicability for real-time operation and control in nuclear-integrated microgrids under volatile operating conditions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Chapter 18: Learning and Tracking Ad Hoc Fiducial Markers in Spatial Augmented Reality

We describe a spatial augmented reality system with a tangible user interface used to control computer simulations of complex systems. In spatial augmented reality, the user’s physical space is augmented with projected imagery, blending real objects with projected information, and a tangible user interface enables users to manipulate physical objects as controllers for interactive visualizations. Our system learns ad hoc objects in the user’s environment as fiducial markers (i.e., objects that are visually recognized and tracked). When combined with simulation and visualization tools, these interfaces allow the user to control simulations or ensembles of simulations via physical objects using apt metaphors. While other research has leveraged the use of depth cameras, our system enables the use of standard cameras in readily available smartphones and webcams and has an implementation that runs completely in JavaScript in the web browser. We discuss the prerequisite object-recognition requirements for such tangible user interfaces and describe computer-vision and machine-learning algorithms meeting those requirements. We conclude by presenting example applications, which are also available online.

energy distribution↗
DOE OSTI2025

pbd-industrial-limit-of-performance (PBD limit of performance) v1.0.0

Software repository that contains models used for a paper about Platform-Based Design with limit of performance analysis for an industrial pilot study. This repository contains process and control models in Modelica and IDAES and scripts to develop an ML based controller that computes the control function that maximizes the techno-economic performance of cost and energy computed by the model. This software is meant to be released to reproduce the work described in a Journal publication that is now drafted with the working title "Energy System Limit of Performance Analysis using an Online Machine Learning Multi-Resolution Optimization Framework".

Amusat, Oluwamayowa [Lawrence Berkeley National La↗
DOE OSTI2022

Upgrade of Core Conduction Cooldown Test Facility Control System

The CCCTF was designed and installed at ORNL in the 1990s for high-temperature gas-cooled reactor accident simulation. It is located within a modular hot cell housed in the IFEL facility. In 2008 and 2009, modifications were made to the CCCTF in preparation for AGR safety testing. A new airlock was designed and installed to better support periodic exchange of the fission product deposition cups while maintaining the fuel at safety test temperature, and modifications were made to add routines to the existing user interface software for computer-controlled automation of the He sweep gas flow and liquid nitrogen supply to the 85Kr cold traps. However, the furnace control hardware and software remained essentially unchanged. At the conclusion of the AGR-1 and AGR-2 PIE campaigns, continued use of the CCCTF was in question because of the gradual obsolescence of the hardware and software used to control the furnace and ancillary systems that make up the CCCTF. Therefore, an effort was undertaken to replace all the obsolete components with new hardware and update the software to increase system reliability and ensure this capability’s maintenance for future research and fuel qualification efforts. The modifications included creating a new National Instruments (NI) LabVIEW-based control system with a graphical user interface (GUI).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Universal dwell time optimization for deterministic optics fabrication

Computer-Controlled Optical Surfacing (CCOS) has been greatly developed and widely used for precision optical fabrication in the past three decades. It relies on robust dwell time solutions to determine how long the polishing tools must dwell at certain points over the surfaces to achieve the expected forms. However, as dwell time calculations are modeled as ill-posed deconvolution, it is always non-trivial to reach a reliable solution that 1) is non-negative, since CCOS systems are not capable of adding materials, 2) minimizes the residual in the clear aperture 3) minimizes the total dwell time to guarantee the stability and efficiency of CCOS processes, 4) can be flexibly adapted to different tool paths, 5) the parameter tuning of the algorithm is simple, and 6) the computational cost is reasonable. In this study, we propose a novel Universal Dwell time Optimization (UDO) model that universally satisfies these criteria. First, the matrix-based discretization of the convolutional polishing model is employed so that dwell time can be flexibly calculated for arbitrary dwell points. Second, UDO simplifies the inverse deconvolution as a forward scalar optimization for the first time, which drastically increases the solution stability and the computational efficiency. Finally, the dwell time solution is improved by a robust iterative refinement and a total dwell time reduction scheme. The superiority and general applicability of the proposed algorithm are verified on the simulations of different CCOS processes. A real application of UDO in improving a synchrotron X-ray mirror using Ion Beam Figuring (IBF) is then demonstrated. The simulation indicates that the estimated residual in the 92.3 mm × 15.7 mm CA can be reduced from 6.32 nm Root Mean Square (RMS) to 0.20 nm RMS in 3.37 min. After one IBF process, the measured residual in the CA converges to 0.19 nm RMS, which coincides with the simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Simultaneously improving accuracy and computational cost under parametric constraints in materials property prediction tasks

Abstract Modern data mining techniques using machine learning (ML) and deep learning (DL) algorithms have been shown to excel in the regression-based task of materials property prediction using various materials representations. In an attempt to improve the predictive performance of the deep neural network model, researchers have tried to add more layers as well as develop new architectural components to create sophisticated and deep neural network models that can aid in the training process and improve the predictive ability of the final model. However, usually, these modifications require a lot of computational resources, thereby further increasing the already large model training time, which is often not feasible, thereby limiting usage for most researchers. In this paper, we study and propose a deep neural network framework for regression-based problems comprising of fully connected layers that can work with any numerical vector-based materials representations as model input. We present a novel deep regression neural network, iBRNet, with branched skip connections and multiple schedulers, which can reduce the number of parameters used to construct the model, improve the accuracy, and decrease the training time of the predictive model. We perform the model training using composition-based numerical vectors representing the elemental fractions of the respective materials and compare their performance against other traditional ML and several known DL architectures. Using multiple datasets with varying data sizes for training and testing, We show that the proposed iBRNet models outperform the state-of-the-art ML and DL models for all data sizes. We also show that the branched structure and usage of multiple schedulers lead to fewer parameters and faster model training time with better convergence than other neural networks. Scientific contribution: The combination of multiple callback functions in deep neural networks minimizes training time and maximizes accuracy in a controlled computational environment with parametric constraints for the task of materials property prediction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Scalable Nano-Scaffold SOFC Anode Architecture Enabling Direct Hydrocarbon Utilization

This project is based on WVU’s pending patents, technology and aims to design and modify the internal surfaces of the Ni/YSZ anode from currently commercially viable Solid Oxide Fuel Cells (SOFCs) using the additive manufacturing process of Atomic Layer Deposition (ALD). The surface architecture/scaffold added onto the internal surface of the anode possesses an engineered nanostructure but it features only commonly-used oxide conductors and electro-catalyst materials. The surface layer possesses a minimum thickness of ~2-40 nm and is solely designed to control the surface reforming reactions and to increase catalytic activity. Three-dimensional (3D) nano scaffold architectures with the noble metal nano-catalyst, low-cost bimetallic catalytic alloys, and nano-scale ionic conducting oxide fully compatible with the state-of-the-art Ni/YSZ anode, were applied to the internal surface of the entire porous SOFC anode using ALD. In the present work, the surface scaffold architecture is essentially multi-functional at the nano-scale, facilitated by the multiple heterostructured interfaces. It will significantly enhance the power density and cell durability for direct hydrocarbon utilization by (1) increasing the number of electrochemical reaction sites to enhance the hydrogen/hydrocarbon oxidation reactions; (2) reducing carbon formation; (3) mitigating the coarsening of backbone Ni phase and the oxidation attack of Ni from oxidants (e.g., H2O, CO2); and (4) promoting the internal reforming capabilities, especially for natural gas applications. ALD is employed to generate stable anode surface architectures that are uniform, precisely controllable at the atomic scale, and accurately repeatable for processing. The engineered anode surface nano-scaffold architecture was cataloged and analyzed using High-resolution Transmission Electron Microscopy (TEM), and cell power/durability performance assessed via comprehensive electrochemical performance testing with commercial specimens and relevant environments using hydrocarbon fuels. To the best of our knowledge, this project is the First Report on ALD of Ni/YSZ. The actual achievement of this Project includes (1). Successful demonstration of 7 types of ALD layers on Ni/YSZ anode, including Co, Ni, Mn, Pt, Ru, ZrOx and multi-functional nano-composite. (1). Conformal coating and subsequently spontaneously pinning the discrete nano-catalyst, including the precious metal nano-catalyst and the Ni and Co catalysts, on the YSZ surface upon the electrochemical operation in the reduced atmosphere. Those nano-catalysts on the ionic-conducting YSZ provided excellent sites for promoting internal reforming; (2). Demonstrated ALD coating increased both catalytic activity and conductivity of Ni/YSZ. Conformal coating provided dopants and introduced additional electrical conducting pathways on the YSZ ionic conductor. The doped surface layer of YSZ with mixed conductivity thus further introduces the active triple phase boundaries adjacent to the ALD-coated nano-catalysts such as Pt, Co, and Ni that are pinned on the YSZ surface. The nano-composite ALD coating on Ni/YSZ anode has significantly increased cell durability; and (3). ALD coating of Ni/YSZ anode increased the power density of the entire cell by 300%. For a long time, the SOFC performance, such as the power density, was deemed hindered by the cathode. The sluggish oxygen reduction reaction (ORR) in the cathode was deemed as hindering the power density of the SOFCs. For the anode-supported commercial SOFCs, the cell performance is considered to be limited by the cathode's performance. For the first time in the field of SOFC, this project has demonstrated that (1). the performance of commercial SOFCs can be further increased by the ALD coating on Ni/YSZ anode backbone. (2). ALD coating on Ni/YSZ fuel electrodes results in the enhancement of power density, and increased reliability, robustness, and endurance of SOFCs, for their application using both hydrogen and hydrocarbon fuels over the entire operating temperature range of 650-800ºC for the inherently functional commercial cells. (3). ALD coating provides alternative approaches of exsolutions for introducing the stable catalyst onto the internal surface of the Ni/YSZ electrode. ALD coating could be much more versatile than exsolution in employing the catalysts with various chemistries onto the various backbones. (4). Due to the negligible amount of ALD materials coated onto the internal surface of the porous cathode of the as-fabricated cells, a peak power density increase up to 300 % induced by ALD coating was simultaneously achieved in terms of both power density and specific power. (5). The ALD coating developed through this project was applied to both the SOFC and Solid Oxide Electrolysis Cells (SOEC). SOEC’s face a similar but more demanding need to improve the fuel electrode's performance. It opens further research directions for electrocatalytic surface nanoionics with a wide range of chemistry. It will revolutionize our ability to render the formation of a nanostructured electrode that has been constantly pursued yet barely achieved for practical SOFC/SOEC applications. The research is also immediately transformative since both the preliminary data and the proposed work are on the direct implantation of nanoionics into the state-of-the-art inherently functional SOCs. It represents an immediate impact on the commercial sectors in SOC technology since the applied ALD processing is computer-controlled ALD coating using the commercial ALD systems, and it is scalable to both the single cells and SOC stacks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Homologous Ladder Cyclohexasilanes

Here, we report the synthesis of five new examples of ladder cyclohexasilanes, possessing up to three consecutive fused rings and differing in relative ring fusion configuration and side chain structure. By coupling a 1,4-dipotassiooligosilyl dianion to a cyclohexasilane, we obtained bi- and tricyclic ladder cyclohexasilanes. Combined experimental and theoretical studies suggested that annulation could favor the cis configuration under kinetic control, while the trans configuration predominates under thermodynamic control. Computational studies show that with each additional ring in the trans-diastereomeric series, the predicted onset of light absorption shifts to longer wavelengths.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Metalloborophenes: Structural Diversity and Emerging Properties of Metal–Boron Two‐Dimensional Frameworks

Metalloborophenes, an emerging subclass of 2D materials, have attracted growing attention owing to their exceptional structural diversity and highly tunable electronic and magnetic properties. Constructed from vacancy‐rich borophene frameworks stabilized by electron donation from incorporated metal atoms, metalloborophenes merge the chemical versatility of boron with the functional richness of metal dopants. The first experimental realization of Cu–borophene nanoribbons in 2024 marked a pivotal advance, confirming long‐standing theoretical predictions and revitalizing interest in this new frontier of boron‐based 2D chemistry. Despite this progress, most studies to date remain conceptual and theoretical, constrained by challenges in scalable synthesis, dopant precision, and substrate control. Computational investigations have revealed a broad landscape of stable metalloborophene structures, exhibiting metallic, semiconducting, and magnetic behavior across diverse dopant families, including alkali, alkaline‐earth, transition, and lanthanide elements. These tunable characteristics open promising avenues for applications in spintronics, catalysis, and hydrogen storage. This review provides a comprehensive overview of metalloborophenes, emphasizing the interplay between structure, stability, and functionality, and outlining future directions toward bridging predictive modeling with experimental realization of this rapidly evolving class of 2D materials.

2D materials↗
DOE OSTI2025

Separation of terbium from proton-irradiated gadolinium oxide targets – development of an effective, scalable and automatable process

This work reports an effective and scalable radiochemical separation process for isolating terbium from Gd 2 O 3 . The separation process uses three commercially available extraction chromatography resin columns, has been implemented on a computer-controlled chemistry module, and tested with 100 mg quantities of proton-irradiated nat Gd 2 O 3 . Here, the 4-hour separation procedure isolated radioterbium in 1.3 mL of 0.01 M HCl with 80 ±8% radiochemical yield and a Gd decontamination factor >(1.2 ± 0.3)·10 5 .

Adjacent lanthanide separation↗
DOE OSTI2020

Novel platinum group metal-free catalyst ink deposition system for combinatorial polymer electrolyte fuel cell performance evaluation

Here, a new system for rapid and precise fabrication of polymer electrolyte fuel cell (PEFC) membrane-electrode assemblies (MEAs) is introduced to facilitate rapid testing and characterization of platinum group metal-free (PGM-free) catalysts, with minimal waste of materials, using commercial combinatorial hardware and multi-channel potentiostat. The commercial combinatorial system, designed for high-throughput catalyst activity evaluation (i.e., low overpotential and current densities) using gas-diffusion electrodes (GDEs), has been further developed to enable simultaneous evaluation of the high current density performance on air of twenty-five cathodes prepared by coating catalyst-ionomer inks directly onto the membrane using a newly-developed deposition system. A nanopipette or micropipette coupled to a precise peristaltic pump is being used for precise and even catalyst ink deposition onto the membrane in each of twenty-five circular electrodes with areas of <1 cm 2 and in the precise locations required by the commercial combinatorial MEA hardware. The automated ink deposition system is coupled with a custom-designed heated vacuum table mounted on a computer-controlled XY stage to prevent deformation of the membrane and electrode layers during ink deposition onto the membrane. This system was utilized to evaluate the impact of ionomer to catalyst ratio on the hydrogen-air fuel cell performance of the PGM-free catalyst.

25 ENERGY STORAGE↗
DOE OSTI2020

Photochemical reactions on aerosols at West Antarctica: A molecular case-study of nitrate formation among sea salt aerosols

Environmental implications of climate change are complex and exhibit regional variations both within and between the polar regions. The increase of solar UV radiation flux over Antarctica due to stratospheric ozone depletion creates the optimal conditions for photochemical reactions on the snow. Modeling, laboratory, and indirect field studies suggest that snowpack process release gases to the atmosphere that can react on sea salt particles in remote regions such as Antarctica, modifying aerosol composition and physical properties of aerosols. Here, we present evidence of photochemical processing in West Antarctica aerosols using microscopic and chemical speciation of individual atmospheric particles. Individual aerosol particles collected at the Brazilian module Criosfera 1 were analyzed by scanning transmission X-ray microscopy with near edge X-ray absorption fine structure spectroscopy (STXM/NEXAFS) combined with computer controlled scanning electron microscopy (CCSEM) with energy-dispersive X-ray (EDX) microanalysis. The displacement of chlorine relative to sodium was observed over most of the sea salt particles. Particles with a chemical composition consistent with NaCl-NO3 contributed up to 30% of atmospheric particles investigated. Overall, this study provides evidence that the snowpack and particulate nitrate photolysis should be considered in dynamic partition equilibrium in the troposphere. Finally, these findings may assist in reducing modeling uncertainties and present new insights into the aerosol chemical composition in the polar environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Cloud condensation nuclei activity of internally mixed particle populations at a remote marine free troposphere site in the North Atlantic Ocean

Here, this study reports results from research conducted at the Observatory of Mount Pico (OMP), 2225 m above mean sea level on Pico Island in the Azores archipelago in June and July 2017. We investigated the chemical composition, mixing state, and cloud condensation nuclei (CCN) activities of long-range transported free tropospheric (FT) particles. FLEXible PARTicle Lagrangian particle dispersion model (FLEXPART) simulations reveal that most air masses that arrived at the OMP during the sampling period originated in North America and were highly aged (average plume age > 10 days). We probed size-resolved chemical composition, mixing state, and hygroscopicity parameter ($\mathcal{κ}$) of individual particles using computer-controlled scanning electron microscopy with an energy-dispersive X-ray spectrometer (CCSEM-EDX). Based on the estimated individual particle mass from elemental composition, we calculated the mixing state index, $χ$. During our study, FT particle populations were internally mixed ($χ$ of samples are between 53% and 87%), owing to the long atmospheric aging time. We used data from a miniature Cloud Condensation Nucleus Counter (miniCCNC) to derive the hygroscopicity parameter, $\mathcal{κ}$ CCNC . Combining $\mathcal{κ}$ CCNC and FLEXPART, we found that air masses recirculated above the North Atlantic Ocean with lower mean altitude had higher $\mathcal{κ}$ CCNC due to the higher contribution of sea salt particles. We used CCSEM-EDX and phase state measurements to predict single-particle $\mathcal{κ}$ ($\mathcal{κ}$ CCSEM-EDX ) values, which overlap with the lower range of $\mathcal{κ}$ CCNC measured below 0.15 % SS. Therefore, CCSEM-EDX measurements can be useful in predicting the lower bound of $\mathcal{κ}$, which can be used in climate models to predict CCN activities, especially in remote locations where online CCN measurements are unavailable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Size-Resolved Elemental Composition of Respiratory Particles in Three Healthy Subjects

The chemical composition of respiratory particles is of interest because the viability of any viruses and bacteria in the particles has been shown to depend on this factor. Here, using computer controlled scanning electron microscopy/energy dispersive X-ray spectroscopy (CCSEM/EDX), we analyzed the size-resolved chemical composition of greater than 35,000 individual respiratory particles collected from three healthy human subjects, quantitatively at nanometer-scale spatial resolution. The desiccated particles ranged in size from 0.05 to 4.4 μm, and the mode of the size distribution was approximately 0.1 μm. Particles were heterogeneous in composition, with approximately 42% of them containing a carbon atomic percentage greater than 95% and approximately 53% of them containing a Na + P + K + Cl percentage greater than 5%. Based on the particles’ elemental composition, we classified them into five categories: 48%–56% of the total number were carbonaceous, mostly organic; 40%–50% Na-rich salt; 0.3%–0.5% P-rich salt; 0.1–0.8% K-rich salt; and 1%–2.5% mixed salt. The number ratio of Na-rich salt particles to carbonaceous particles increased with increasing particle size; particles larger than approximately 2 μm were dominated by Na-rich salt. Size-dependent differences in the chemical composition of respiratory particles may have important implications for the efficiency of airborne transmission of respiratory pathogens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Chemical Imaging of Fine Mode Atmospheric Particles Collected from a Research Aircraft over Agricultural Fields

Vegetation and crops in agricultural areas are a significant source of both biogenic secondary organic aerosols and of directly emitted primary biological atmospheric particles (PBAPs) composed of plant fragments/debris, fungal spores, pollen, and bacteria among others. Seasonal crop harvesting significantly intensifies regional PBAP emissions and aerosolizes soil organic matter and dust particulates. In this paper, the atmospheric contributions and impact of PBAPs have been insufficiently investigated due to limited real-time observations and ambiguity in their detection by existing measurement techniques. We collected ambient particles on-board an instrumented Beechcraft BE76 Duchess research aircraft flying above actively harvested agriculture fields of corn and soybean crops. In this work, particle samples were analyzed using complementary chemical imaging techniques, including computer-controlled scanning electron microscopy coupled with elemental microanalysis to describe particle-type population and scanning transmission X-ray microscopy to provide quantitative metrics on particle internal heterogeneity. We show the particle-class contribution is consistent between corn and soybean harvesting across altitudes of 100–300 m AGL. The contribution of PBAPs in the size range of 0.15–1.25 μm is estimated to be 10–12% of ~39,000 analyzed particles. Determining viscosity with X-ray microanalysis has shown that the fine-mode organic particles collected are viscous/semisolid (10 2 –10 12 Pa s) while the majority of PBAP fragments are solid (>10 12 Pa s). The observation of submicrometer, solid carbonaceous fragments of biogenic origin has implications for the regional cloud condensation nuclei and ice nuclei budget. Therefore, seasonal harvesting of crops may play an important, yet unrecognized, role in regional cloud formation and climate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

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↗
DOE OSTI2023

Stratospheric ozone depletion in the Antarctic region triggers intense changes in sea salt aerosol geochemistry

Abstract Since the early 1980s, the Antarctic environment has served as a natural field laboratory for researchers to investigate the effects of stratospheric ozone depletion, which has resulted in increased surface ultraviolet radiation levels. However, its effective threats still present gaps. We report new pieces of evidence of increased ultraviolet radiation impacting West Antarctica sea salt aerosols. Salt aerosols, particularly in the Southern Ocean Sea, play an important role in the radiative earth balance. To disclose the molecular details of sea salt aerosols, we used a synchrotron-based multi-element microscopic speciation of individual microparticles (Scanning Transmission X-ray Microscopy with Near-Edge X-ray Absorption Fine Structure Spectroscopy combined with Computer-Controlled Scanning Electron Microscopy). Here we identified substantial abundances of chlorine-enriched aerosols in sea salt generated by photolytic products, whereas ice core records revealed increased chlorine depletion from the onset of ozone depletion. Our findings reveal that modern sea salt modification has no Holocene precedent.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Chemical imaging of individual stratospheric particles sampled over North America

The increasing size, severity, and frequency of wildfires have led to dramatic increases in particulate matter concentrations in the troposphere. Severe wildfires can generate intense convective systems capable of transporting large quantities of biomass burning organic aerosols (BBOA) to the upper troposphere and lower stratosphere (UTLS). Chemically complex organic matter and light-absorbing carbonaceous material is introduced into stratospheric regions that were historically isolated from direct surface emissions. In this study, stratospheric particles were sampled over North America during the Dynamics and Chemistry of the Summer Stratosphere (DCOTSS) campaign, an aircraft-based research project designed to characterize convective perturbation in the UTLS. Particle samples collected from six research flights during summer 2022 were analyzed using Computer-Controlled Scanning Electron Microscopy and Scanning Transmission X-ray Microscopy to investigate particle size distributions, morphology, chemical composition, and mixing state of stratospheric particles along transects across the continental United States and adjacent Pacific Ocean airspace. Analysis revealed that all sampled particles contained detectable levels of carbon, with most exhibiting organic volume fractions of 0.37 ± 0.20. Notably, about 5% of the particles also contained soot inclusions, which indicates the presence of refractory black carbon transported to stratospheric altitudes and provides direct evidence of wildfire-derived black carbon reaching the UTLS. Typical particle morphology exhibits organic shells over soot and inorganic cores and suggests secondary processing and aging of BBOA during transport to and within the UTLS. These findings provide compelling evidence that wildfire emissions play a critical role in affecting the long-term composition and radiative properties of stratospheric particles.

Sharpe, Steven [Purdue Univ., West Lafayette, IN (↗