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At least 541 records · Page 30

Dual-Bed Radioiodine Capture from Complex Gas Streams with Zeolites: Regeneration and Reuse of Primary Sorbent Beds for Sustainable Waste Management

Dual-sorbent systems are proposed for radioiodine management with a regenerated primary bed for multiple cycles of use in complex conditions and a secondary bed for disposal with higher waste loadings. Sorbent approaches for the effective capture of gaseous radioiodine (isotopes 129 I and 131 I) produced from a range of nuclear processes have been studied for over half a century. (1−5) Whether or not a sorbent (e.g., molecular sieve) is required to physically screen/trap or chemically bind a radionuclide of interest through chemisorption, the complexity of the gas stream has a large impact on the performance (e.g., loading capacity, selectivity) and active life of a sorbent bed. (3) Silver mordenite (AgZ), the U.S. Department of Energy baseline sorbent for radioiodine capture from nuclear processes, performs well within acidic conditions and at elevated temperatures (6) and can be consolidated into a chemically durable waste form for long-term disposal. (7,8) However, new sorbents are being sought because optimal capture performance of AgZ significantly decreases in dynamic oxidizing environments with competing species, and it is expensive and it contains Ag (a toxic metal). (9) Until a new sorbent is found to replace AgZ, the regeneration and reuse of AgZ is an attractive alternative to a single-use primary sorbent bed. In this regard, a primary sorbent could be designed for enhanced capture in complex gas streams and the ability to be regenerated for reuse. Here, a secondary sorbent could then be tailored for maximum iodine loading in the gas stream and chemical durability within a disposal facility.

chemisorption

Characterizing microscale signatures in uranium ore concentrates using electron probe microanalyzer

Impurities in uranium ore concentrates (UOCs) serve as forensic signatures of processing history and origin. Here, this study utilizes Electron Probe Microanalyzer (EPMA) to characterize microscale compositional and textural features in individual UOC particles from both commercial and bench-scale production. At the particle scale, multiple internal phases with distinct morphologies, chemical signatures, and stoichiometries are documented. Our data shows that chemical impurities are heterogeneously distributed within single particles and among particles within a sample. These microscale heterogeneities correlate with known processing histories, indicating that microscale signatures of early fuel cycle materials can provide valuable information for nuclear forensic material analysis.

organic

Excipient screening by lyophilization provides insights into spray drying formulations for nanoparticle vaccines

Nanoparticles have shown great promise as delivery platforms in the development of tunable and safe vaccines. Nanolipoprotein particles (NLPs), also known as nanodiscs, are discoidal nanoparticles composed of a lipid bilayer stabilized at their periphery by apolipoproteins. Under the right conditions, the NLP self-assembly process is highly customizable in terms of lipids and apolipoprotein constituents, allowing for tunable physical and chemical characteristics. This flexibility allows a wide range of vaccine antigens and adjuvants to be incorporated onto the NLP platform for tailored vaccine design. The stability of NLPs during long term storage is a very important factor in developing a vaccine delivery platform suitable for widespread global use. When stored in a solution for extended periods of time, NLPs dissociate into their corresponding lipids and protein constituents, leading to particle degradation. Proper stabilization of NLPs can often be achieved by lyophilization (i.e. freeze-drying), a method widely used for various applications including pharmaceuticals. This process, however, can be damaging to particles without the presence of lyoprotectants or excipients that help maintain particle stability during lyophilization. Another method used to stabilize vaccines and pharmaceuticals is spray drying, a process that converts liquid formulations into dry powders through controlled heating and airflow. While spray drying is rapid, scalable, and cost-effective, lyophilization is typically a gentler process that better retains biomolecule structure and function. Both processes use excipients for particle stabilization, so lyophilization can be used as a surrogate to test stability of NLPs, to down-select formulations that may withstand the harsher conditions of spray drying. To screen different formulations, NLPs were synthesized and purified to homogeneity by size exclusion chromatography (SEC) and samples were prepared with a wide range of lyoprotectants and/or excipients. To assess the protective effects of excipients on NLPs upon spray drying, both pre- and post-lyophilized samples were analyzed by SEC. To assess the protective effects upon heating (encountered during the spray drying process), NLP samples were incubated at elevated temperatures prior to SEC analysis. The lyoprotectants and excipients evaluated in this study had different efficiencies in protecting NLPs during lyophilization and heating tests. Trehalose, for example, exhibits stabilization on NLPs both upon lyophilization and heating whereas leucine accelerated NLP dissociation. Although some lyoprotectants are effective by themselves, different combinations can decrease the stabilization of NLPs. Shelf-stable vaccines that do not require cold-chain storage are essential for global accessibility and our findings provide fundamental insight into how to advance NLP-based vaccines for these applications.

Serrano, Litzay J [Lawrence Livermore National Lab

Enhancing the flowability of woody biomass slurries in wet biorefineries

Feeding wet lignocellulosic biomass (e.g., softwood and hardwood) slurries into high-pressure, high-temperature reactors at an industrially relevant scale presents significant challenges, such as process equipment plugging. Here, in this study, we investigate the possibility of improving the flowability of biomass slurries in an industrial scale wet biorefinery by tuning the physical and chemical characteristics of the biomass particles. To understand the effects of the chemical characteristics of woody biomass on flowability, cellulose pulp particles are produced from pulp sheets via knife milling, pelletizing, and crumbling. These cellulose pulp particles are processed for acid hydrolysis dehydration (AHDH) at one –tonne-per day pilot scale. The levulinic acid yield (a product of cellulose AHDH) and the flowability of the biomass particles are compared using market pulp, 1 mm crumbled pine softwood, a blend of 2 mm hammer-milled pine softwood with 10 wt.% bark, and 2 mm hammer-milled hardwood material with 10 wt.% bark (to represent forest residues). The results indicate that the presence of hemicellulose and lignin influences the flowability of lignocellulosic feedstocks. Crumbled wood particles show poor flowability at the pilot scale, while the presence of fine materials (less than 0.7 mm) and bark improves the flowability of biomass slurries without affecting the organic acid yields (based on C6 carbohydrate content).

09 - BIOMASS FUELS

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions

Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.

54 ENVIRONMENTAL SCIENCES

Energy-efficient scientific computing using chemical reservoirs

The rapid growth of computing demands driven by scientific computing, data analytics, and artificial intelligence (AI) advancements has exposed the limitations of traditional digital processing systems. These systems are nearing physical energy barriers, making significant gains in energy efficiency increasingly unattainable. As we advance toward post-exascale computing, disruptive approaches are critical to overcoming these limitations. Among emerging analog solutions, biochemical computing offers a transformative path for achieving orders-of-magnitude improvements in energy efficiency. By leveraging the natural optimization capabilities of chemical reaction networks (CRNs), biochemical systems have the potential to meet high-performance computing needs through natural scalability. However, numerous challenges remain, including theoretical limitations in mapping computational problems to CRNs and practical barriers in implementing biochemical computing devices. In this paper, we present a framework for chemical computation using biochemical systems and introduce key components of our approach for energy-efficient scientific computing. We showcase the feasibility of this framework by solving a system of ordinary differential equations by emulating a chemical reservoir device, demonstrating its potential for addressing modern computing challenges. This work lays a foundational step toward harnessing the computational power of chemistry to design energy-efficient, scalable, high-performance next-generation computing systems.

Johnson, Connah G. M. [Pacific Northwest National

In Situ Characterization of Interface Evolution in Argyrodite‐Based All‐Solid‐State Li Batteries

Interfacial stability is one of the critical challenges in all-solid-state Li metal batteries. Multiple processes such as solid electrolyte (SE) decomposition and lithium dendrite growth take place at the solid interfaces during cycling, leading to the overall cell failure. To deconvolute these complex processes, in situ characterization is of paramount importance to elucidate the interfacial evolution on the SE upon Li plating/stripping. Herein, an all-solid-state asymmetric in situ cell is developed that allows the direct visualization of the highly localized Li plating/stripping processes under the optical microscope. Moreover, this cell configuration enables reliable post-mortem chemical and morphological analysis of the intact SE/Li interface. Using combined scanning electron microscopy and energy-dispersive X-ray spectroscopy, the study reveals that the evolution of the Li argyrodite interface is strongly influenced by the current density, particularly in terms of chemical distribution and Li plating morphology. More specifically, the solid interface is LiCl-rich with the formation of Li cubes at low current densities, while high currents result in more uniform elemental distribution and filament morphology. These findings elucidate the dynamic evolution mechanism at solid interfaces and offer valuable guidance for developing stable solid interfaces in all-solid-state Li metal batteries.

Huang, Di

Hierarchically Structured Vitrimer Biocomposites for Sustainable Manufacturing

Polymers containing dynamic covalent bonds (DCBs) exhibit thermoplastic-like flow above their topology freezing temperature (T v ) while maintaining thermoset-like properties below it, making them promising for sustainable manufacturing. However, their large-scale adoption remains limited due to challenges in accurately determining T v and achieving efficient fiber-matrix bonding in composite applications. Here, hierarchically structured epoxy-anhydride-based polyester vitrimer composites reinforced with cellulosic filaments is demonstrated, where hydroxyl groups on fiber surfaces participate directly in transesterification with the matrix. Further, this dynamic interfacial bonding delivers exceptional mechanical properties, including ≈70 MPa shear strength and >10% strain-to-failure, while enabling thermal malleability. Using a combination of nuclear magnetic resonance and nano-infrared spectroscopies, direct evidence is provided that chemical bond exchange begins well below the conventionally measured T v , supporting the hypothesis that rheologically determined T v reflects a combination of chemical exchange and frictional dynamics rather than a discrete transition. The composites demonstrate excellent processability through vacuum-assisted resin transfer molding and maintain >90% of their mechanical properties after multiple thermal reforming cycles. These findings advance both the fundamental understanding of vitrimeric transitions and the practical development of sustainable, high-performance composite materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Science acceleration and accessibility with self-driving labs

In the evolving landscape of scientific research, the complexity of global challenges demands innovative approaches to experimental planning and execution. Self-Driving Laboratories (SDLs) automate experimental tasks in chemical and materials sciences and the design and selection of experiments to optimize research processes and reduce material usage. This perspective explores improving access to SDLs via centralized facilities and distributed networks. We discuss the technical and collaborative challenges in realizing SDLs’ potential to enhance human–machine and human–human collaboration, ultimately fostering a more inclusive research community and facilitating previously untenable research projects.

Canty, Richard B. [North Carolina State University

Electronic origin of reorganization energy in interfacial electron transfer

Electron transfer (ET) reactions underpin energy conversion and chemical transformations in both biological and a biological systems. The efficiency of any ET process relies on achieving a desired ET rate within an optimal driving force range. Marcus theory provides a microscopic framework for understanding the activation free energy—and therefore the rate—of ET in terms of a key parameter: the reorganization energy. For electrified solid–liquid interfaces, it has long been conventionally understood that only factors in the electrolyte phase are responsible for determining the reorganization energy and that the electronic density of states (DOS) of the electrode only serves to dictate the number of thermally accessible channels for ET. Here we show instead that the electrode DOS plays a central role in governing the reorganization energy, far outweighing its conventionally assumed role. Using atomically layered heterostructures, we tune the DOS of graphene and measure outer-sphere ET kinetics. We find the ensuing variation in ET rate arises from strong modulation in a reorganization energy associated with image potential localization in the electrode. Here we redefine the traditional paradigm of heterogeneous ET kinetics, revealing a deeper role of the electrode electronic structure in interfacial reactivity.

Electrochemistry

Uncertainty in determining carbon dioxide removal potential of biochar

A quantitative and systematic assessment of uncertainty in life-cycle assessment is critical to informing sustainable development of carbon dioxide removal (CDR) technologies. Biochar is the most commonly sold form of CDR to date and it can be used in applications ranging from concrete to agricultural soil amendments. Previous analyses of biochar rely on modeled or estimated life-cycle data and suggest a cradle-to-gate range of 0.20–1.3 kg CO 2 net removal per kg of biomass feedstock, with the range reported driven by differences in energy consumption, pyrolysis temperature, and feedstock sourcing. Herein, we quantify the distribution of CDR possible for biochar production with a compositional life-cycle inventory model paired with scenario-aware Monte Carlo simulation in a 'best practice' (incorporating lower transportation distances, high pyrolysis temperatures, high energy efficiency, recapture of energy for drying and pyrolysis energy requirements, and co-generation of heat and electricity) and 'poor practice' (higher transportation distances, lower pyrolysis temperatures, low energy efficiency, natural gas for energy requirements, and no energy recovery) scenarios. In the best-practice scenario, cradle-to-gate CDR (which is representative of the upper limit of removal across the entire life cycle) is highly certain, with a median removal of 1.4 kg of CO 2 e/kg biomass and results in net removal across the entire distribution. In contrast, the poor-practice scenario results in median net emissions of 0.090 kg CO 2 e/kg biomass. Whether this scenario emits (66% likelihood) or removes (34% likelihood) carbon dioxide is highly uncertain. The emission intensity of energy inputs to the pyrolysis process and whether the bio-oil co-product is used as a chemical feedstock or combusted are critical factors impacting the net carbon dioxide emissions of biochar production, together responsible for 98% of the difference between the best- and poor-practice scenarios.

54 ENVIRONMENTAL SCIENCES

Chemical speciation correlated with microstructural heterogeneity of interdicted uranium materials

Two uranium powders seized by law enforcement in Victoria, Australia, have been characterized by established nuclear forensic methods in a previously published study. Here, in this work, the results of further characterization by a scanning transmission x-ray microscope (STXM) operating in the soft x-ray regime are reported. STXM images are used to estimate the elemental distribution in micrometer-scale particles of each powder, and oxygen K-edge absorption spectra are used to determine the chemical state of uranium. The results of the current study are consistent with the previous analysis; the first powder is found to be a potassium-uranium hydrate, while the second powder is determined to be a mixture of uranium oxides primarily consisting of UO 3 .

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA

Assessment of Active Metal Removal from Pyroprocessing Salt by Electrodeposition

Active metals present in used nuclear fuel dissolve into the salt during pyroprocessing, but are not recovered during electrorefining or drawdown operations due to their chemical stability in the salt. The accumulation of 137 Cs and 90 Sr in process salt over time increases the heat load and ionizing radiation level of the salt such that it must be replaced frequently, resulting in a significant amount of salt waste. An effective means of removing active metals from the molten salt to enable recycle of the electrolyte (e.g., LiCl/KCl) would increase the efficiency of pyroprocessing and decrease the volume of salt waste requiring disposal.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS

Thermochemical conversion of waste plastics with coal and biomass to generate value-added products

Co-gasification of waste plastic and waste coal/biomass in steam was investigated to evaluate the effects of operating conditions and low-cost catalyst compositions of coal ash on syngas production and tar mitigation. The results demonstrate the benefits of waste plastic conversion with coal or biomass. A better understanding of these processes will facilitate the development of more accurate kinetic models for industrial-scale chemical recycling.

co-gasification

Let’s Stick Together: Interplay of Moisture, Particle Size, and Anatomical Fraction in the Flowability of Corn Stover Crumbles

Handling of agricultural waste biomass feedstocks (such as corn stover) is a persistent challenge in the production of biofuels and chemicals at integrated biorefineries. Inconsistent flowability and high feedstock variability creates equipment downtime and processing challenges. This work investigates the influence of anatomical fractionation and moisture content on the bulk solids handling behavior of corn stover crumbles. A combination of rotational shear testing, tribological measurements, and surface and sorption characterization techniques (inverse gas chromatography (IGC), dynamic vapor sorption (DVS), and electron microscopy) were used to evaluate the contributions of particle composition, size, and surface properties to flow behavior. Shear and frictional testing revealed that moisture content has the greatest effect on flowability, significantly increasing unconfined yield strength (ƒ c ) and reducing the flow function coefficient (F F C), regardless of anatomical fraction. While differences in surface energy were observed between fractions, particularly in polar contributions (γ AB ), these did not correspond to meaningful differences in flow behavior. Coarser 6 mm particles exhibited poorer flow performance than 4 mm crumbles, likely due to broader particle size distributions and increased particle interlocking. Taken together, these results suggest that anatomical fractionation provides minimal benefit from a bulk handling perspective. Moisture content and particle size heterogeneity dominate handling behavior, reinforcing the utility of unfractionated corn stover in processing environments. These insights can inform the design of more efficient and reliable feedstock processing systems for agricultural biomass in integrated biorefineries.

09 BIOMASS FUELS

Optimization of Desalination Systems with Detailed Water Chemistry through Integration of Reaktoro in WaterTAP

Chemistry predictions are critical for an accurate estimation of performance and costs in desalination process models, which allows for the estimation of the value of new technologies and the viability of treating new water sources. Herein, we present how an implicit function formulation can be used to integrate the chemical modeling package, Reaktoro, into the techno-economic assessment and modeling platform, WaterTAP. This approach resolves the critical issues of integrating large-scale thermodynamic models and databases into equation-oriented process models while allowing more flexibility relative to previously presented surrogate-based methods. We describe how this integration into Pyomo and WaterTAP models is implemented and used through the open-source package Reaktoro-PSE . We first validate this integration approach by performing optimization on a previously presented desalination treatment train with softening and acid addition as the pretreatment steps. Then, to demonstrate the value of this approach, we extend the cost-optimization problem to include the simultaneous addition of lime and soda ash for softening, and HCl and H 2 SO 4 in the acidification steps. Finally, we were able to confirm the previously established results that were obtained by using surrogate models and demonstrate that the implicit function approach enables exploration of different feedwater compositions and a larger number of chemicals and their combinations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

A GPU-based compressible combustion solver for applications exhibiting disparate space and time scales

High-speed chemically active flows pose significant computational challenges due to their disparate space and time scales, with stiff chemistry often dominating simulation time. While modern scientific computing programs achieve exascale performance by leveraging graphics processing units (GPUs), existing GPU-based compressible combustion solvers face critical limitations in memory management, load balancing, and handling the highly localized nature of chemical reactions. To this end, we present a high-performance compressible reacting flow solver built on the AMReX framework and optimized for multi-GPU settings. Here, our approach addresses three GPU performance bottlenecks: memory access patterns through column-major storage optimization, computational workload variability via a bulk-sparse integration strategy for chemical kinetics, and multi-GPU load distribution for adaptive mesh refinement applications. The solver adapts existing matrix-based chemical kinetics formulations to multi-grid contexts. Using representative combustion applications, including 2D and 3D detonations and a 3D jet-in-crossflow configuration, we demonstrate 1.4–5× performance improvements over initial implementations on an in-house cluster of NVIDIA H100 GPUs, and near-ideal weak scaling on the Frontier supercomputer (Oak Ridge Leadership Computing Facility) with up to 1024 AMD Instinct MI250X GPUs. Roofline analysis reveals substantial improvements in arithmetic intensity for both convection (∼ 10 ×) and chemistry (∼ 4 ×) routines, confirming efficient utilization of GPU memory bandwidth and computational resources.

42 ENGINEERING

Super-resolution imaging reveals resistance to mass transfer in functionalized stationary phases

Chemical separations are costly in terms of energy, time, and money. Separation methods are optimized with inefficient trial-and-error approaches that lack insight into the molecular dynamics that lead to the success or failure of a separation and, hence, ways to improve the process. We perform super-resolution imaging of fluorescent analytes in five different commercial liquid chromatography materials. Unexpectedly, we observe that chemical functionalization can block more than 50% of the material’s porous interior, rendering it inaccessible to small-molecule analytes. Only in situ imaging unveils the inaccessibility when compared to the industry-accepted ex situ characterization methods. Selectively removing some of the functionalization with solvent restores pore access without substantially altering the single-molecule kinetics that underlie the separation and agree with bulk chromatography measurements. Our molecular results determine that commercial “fully porous” stationary phases are over-functionalized and provide an alternative avenue to characterize and direct separation material design from the bottom-up.

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