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At least 1,369 records · Page 76

Diffusion of small anti-Schottky clusters in UO 2

The aggregation of irradiation-induced defect clusters in UO 2 leads to the formation of dislocation loops and cavities which contribute swelling and ultimately produce deleterious effects on the fuel. Despite their fundamental role in fuel evolution, the kinetics of these defect clusters are not currently well understood. In this work, we investigate the diffusive behavior of interstitial clusters via Molecular Dynamics (MD) simulations. In this work, our investigation considers a range of defect cluster sizes, each composed of N UO 2 units or anti-Schottky defects. We report a complex cluster size - mobility relation; increasing cluster size corresponds to an increase in mobility up until a critical size (N=4) where diffusivity reaches a maximum, after which the trend reverts and further increasing size corresponds to a decrease in mobility. The rapid migration observed correlates well with the very low barriers reported in a few historic experimental studies. Further analysis of the cluster shape and orientation reveals a strong structural preference for near-planar configurations oriented normal to the $\langle$100$\rangle$ direction. Rotational energy barriers are found to be similar in both magnitude and trend to the observed diffusion barriers. Lastly, connections are drawn to the behavior of large defect clusters including the formation of (1/3) $\langle$111$\rangle$ Frank dislocation loops.

36 MATERIALS SCIENCE↗

Synthesis of UO 2 nanoparticles via coulometric titration: influence of electrolytes and ligands on synthesized particle properties

Here, this study investigates the controlled synthesis of uranium oxide nanoparticles (UO 2 NPs) via coulometric titration under ambient conditions, focusing on the impact of electrolyte anions, salt concentrations, and strongly complexing ligands on particle formation and properties. Using dilute solutions of mineral acids (HNO 3 , HCl, and HClO 4 ), we demonstrate that the choice of electrolyte anion affects particle size, polydispersity, and surface charge. Particle characterization using dynamic light scattering and transmission electron microscopy shows that particles synthesized in perchlorate are largest and show a high degree of polydispersity. In comparison, particles synthesized in chloride are smaller and more uniform in size. Synthesis in nitrate yields a wide variety of particle sizes, with the major fraction (>65 %) having a smaller size than that obtained in the other two electrolytes. The presence of more strongly coordinating ligands such as sulfate and acetate modulate hydrolysis and condensation reactions, with sulfate suppressing nanoparticle formation across a wide concentration range ([SO 4 2- ] > 5 mM) and acetate enabling stable colloidal suspensions at [HOAc] ≤ 0.1 M. Synthesis in concentrated electrolytes (2 M NaNO 3 , NaCl, or NaClO 4 ) accelerates reaction kinetics but introduces challenges, as particles showed increased polydispersity and aggregation, and were more prone to oxidation. Electrolyte effects on actinide oxide nanoparticle formation are discussed and a short comparison to established nanoparticle syntheses is drawn. This work underscores the importance of tailoring synthesis parameters to achieve desired nanoparticle properties, providing valuable insights for optimizing UO 2 NP production for various applications.

Actinide hydrolysis↗

Understanding the structural and morphological effects of synthesis route on NpO 2

The availability of actinide standard materials for use in nuclear safeguard applications is critical, as is thorough characterization thereof. Although accurate trace element compositions and isotopic considerations are paramount for deployment of reference standards, structural characterization is also essential towards accurately describing the chemical form and potential matrix effects in candidate materials. Here, to this end, samples of NpO 2 were synthesized via a direct denitration (DD) method and probed with powder X-ray diffraction (PXRD), Raman spectroscopy, and scanning electron microscopy (SEM) for structural and morphological characterization and comparison with NpO 2 materials produced via modified direct denitration (MDD). PXRD confirmed the bulk identity of NpO 2 , and no additional phases were identified using this method. Analysis of Raman data collected using a 532 nm excitation wavelength indicates that samples are mostly phase pure; however, some variability in spectral features is observed. Analysis of additional spectroscopic data collected with a 785 nm excitation wavelength revealed variability in the relative intensity of spectral features. Raman spectroscopy indicates that the sample is primarily NpO 2 ; however, additional signals indicate possible structural disorder, oxidized species, or potential contributions from other Np phases. To further investigate the possibility of additional phase contributions within the sample of NpO 2 , Raman spectroscopic mapping was employed to examine the homogeneity of the sample produced via DD. From this analysis, we determined that despite variability in the intensity of Raman-active vibrational modes, consistent spectra are obtained throughout the area of the sample investigated. SEM images show aggregates with variable sizes and shapes, with rounded, primary particles possessing an average diameter of approximately 100 nm. Comparison of the results of these multimodal analyses to the literature indicates that the crystal chemical, spectroscopic, and microstructural properties of NpO 2 vary based on synthesis method, even if X-ray diffraction data indicate that the bulk phase is NpO 2 .

Direct denitration↗

Reassessing early-age strength development of high-volume fly ash concretes for precast buildings

Increasing beneficial use of fresh or landfilled fly ash as a replacement for Portland cement can be more challenging for the construction of precast buildings or similar applications requiring rapid strength development. Therefore, the framework presented in this paper aims to reassess high-volume fly ash concretes but in the context of facilitating more sustainable precast buildings. More specifically, the framework was used to characterize strength development of concrete mixes with a target minimum 24-hour compressive strength of 24.1 MPa (3500 psi), selected as an example strength development metric to demonstrate the framework, and comprised of 40% Class C, Class F, and landfilled (harvested) fly ash – as a high-volume replacement of Type III or Type IL cement. High-early strength was driven by optimized dosages of commercial grade gypsum and accelerating admixtures, in addition to optimal aggregate packing and mix proportioning strategies. Early-age mechanical properties including compressive strength, modulus of rupture, and modulus of elasticity were reevaluated within 24 hours of batching with respect to common precast production demands. Simple data analyses were then used to highlight cases where currently accepted design provisions for the aforementioned properties are either overly-conservative or unconservative with respect to test data. Furthermore, the framework and demonstration of example mixes presented herein aim to promote confidence for using larger fractions of fresh or landfilled fly ashes for precast buildings to further enhance environmental benefits without sacrificing pertinent early-age structural performance.

42 ENGINEERING↗

Durable and Highly Selective Ion Transport of a Sulfonated Diels Alder Poly(phenylene) for Vanadium Redox Flow Batteries

In order to improve the durability and ion selectivity of a hydrocarbon membrane for vanadium redox flow batteries (VRFBs) a polymer was rationally designed with an external hydrophobic shell and internal hydrophilic core. The polymer was designed to prevent hydrophilic polymer chain aggregation by functionalizing the external polymer shell with hydrophobic side chains and attaching acid moieties onto the polymer backbone. The hydrophobic shell is the result of pentafluorobenzoyl group functionalization on the pendent aryl rings in a Diels Alder poly(phenylene) backbone. The internal polymer chain contains sulfonic acid moieties to impart hydrophilic character. The physical and electrochemical characteristics of the membrane were systematically studied and compared with widely used Nafion-212. The synthesized membranes were found to be superior to Nafion-212 not only in terms of lower vanadium ion permeability (4.25 x 10 -8 vs. Nafion-212: 6.0 x 10 -6 cm 2 min -1 ) but also in higher VRFB performance (EE = 89% and vs. Nafion-212: 85.5% at 40 mA/cm 2 ). The membrane also showed over three times higher capacity retention than Nafion-212 (0.075 % vs. 0.25 % capacity loss per cycle respectively) and excellent ex situ and in situ chemical stability.

25 ENERGY STORAGE↗

Ethanol-fueled metal supported solid oxide fuel cells with a high entropy alloy internal reforming catalyst

High-performance metal supported solid oxide fuel cells (MS-SOFC) with an integrated high entropy alloy (HEA) internal reforming catalyst (IRC) are demonstrated for transportation applications using ethanol and methanol as fuels. Addition of the HEA IRC dramatically improves cell performance and stability when using ethanol/water blend fuel. Absence of carbon deposition predicted by thermodynamic calculations is confirmed by Raman spectroscopy analysis of posttest anodes. Optimal catalyst processing (deposition technique, loading, firing temperature) and cell operation conditions (flow rates, temperature, fuel compositions) are explored. Infiltrated HEA reforming catalyst provides a highly porous structure and low catalyst loading (6 mg cm –2 ). The designed structure and catalysts achieve small mass transport resistances in the fuel electrode (26.2 s m –1 ) and oxygen electrode (41.6 s m –1 ). The best ethanol concentration (60:40 v% ethanol: water) provides 0.83 W cm –1 at 700 °C, without carbon deposition. The ethanol-fueled MS-SOFC is operated for 500 h, including five thermal cycles. As a result, cell evolution is similar to that reported previously for hydrogen fuel; nickel aggregation and chromia deposition were the major observed changes, and carbon formation can be avoided even after long-term operation.

30 DIRECT ENERGY CONVERSION↗

Impact of fluorination on Li + solvation and dynamics in ionic liquid-hydrofluoroether locally concentrated electrolytes

The thermal, physical, structural, and transport properties of ionic liquid (IL) electrolytes based on n-methyl-n-butylpyrrolidinium bis(trifluoromethanesulfonyl)imide [PYR14][TFSI] and Li-salts of lithium (nonafluorobutane)(trifluoromethanesulfonyl)imide [Li][IM14] and [Li][TFSI] (0 ≤ x Li ≤ 0.3), with addition of 1,1,2,2-tetrafluoroethyl 2,2,3,3-tetrafluoropropyl ether (TTE) (0 ≤ x TTE ≤ 0.45) were studied. While [IM14] increases glass transition, TTE improves conductivity over a wider liquidus range. Li + is solvated primarily by [TFSI] with some contribution by [IM14]. However, both 7 Li- 19 F HOESY NMR and spatial distribution functions (SDFs) derived from molecular dynamics (MD) indicate short contacts between the fluorines of TTE and Li + . This is a result of tighter anion-solvated Li + without aggregation of the Li + solvates, consistent with Raman measurements, thus confirming the existence of fluorous domains in the bulk which leads to improved fluidity and a Li + diffusivity of 1.26 x 10 –12 m 2 /s at 0 °C (x Li = 0.20) with a Li + transference of 0.16. Improved transport properties translated to a higher capacity of the TTE/IL electrolyte in a Li||LiFePO 4 half-cell with 95mAh/g at 0.1C, compared to the IL electrolyte without TTE (60 mAh/g). Furthermore, this study demonstrates the tunability of solvation and transport properties in IL electrolytes by asymmetric fluorinated anions and hydrofluoroether co-solvents for low temperature Li-ion batteries.

25 ENERGY STORAGE↗

Dynamic machine learning-based optimization algorithm to improve boiler efficiency

With decreasing computational costs, improvement in algorithms, and the aggregation of large industrial and commercial datasets, machine learning is becoming a ubiquitous tool for process and business innovations. Machine learning is still lacking applications in the field of dynamic optimization for real-time control. This work presents a novel framework for performing constrained dynamic optimization using a recurrent neural network model combined with a metaheuristic optimizer. The framework is designed to augment an existing control system and is purely data-driven, like most industrial Model Predictive Control applications. Several recurrent neural network models are compared as well as several metaheuristic optimizers. Hyperparameters and optimizer parameters are tuned with parameter sweeps, and the resulting values are reported. Further, the best parameters for each optimizer and model combination are demonstrated in closed-loop control of a dynamic simulation, and several recommendations are made for generalizing this framework to other systems. Up to 0.953% improvement is realized over the non-optimized case for a simulated coal-fired boiler. While this is not a large improvement in percentage, the total economic impact is $991,000 per year, and this study builds a foundation for future machine learning with dynamic optimization.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Combustion synthesis of Eu 2 O 3 nanomaterials with tunable phase composition and morphology

Combustion reactions in europium nitrate – acetylacetone – 2-methoxyethanol solutions and gels were investigated to produce europium (III) oxide (Eu 2 O 3 ) nanocrystalline materials and thin films. Thermal analyses of solutions indicated that the 2-methoxyethanol solvent also acts as a fuel in the absence of acetylacetone. Adding acetylacetone increases the overall heat of the reaction. Thermal analysis results revealed that the slow oxidation of unburned hydrocarbon residues follows the primary combustion reaction. Time-temperature profile measurements of the bulk combustion synthesis process in air and nitrogen atmospheres enabled the extraction of the maximum reaction temperature and the heating and cooling rates in the combustion zone. Several direct correlations exist between measured combustion parameters and the phase composition of the products. Combustion in air results in mixed-phase cubic and monoclinic nanocrystalline Eu 2 O 3 . Increasing the acetylacetone concentration in solutions increases the synthesis temperature and decreases the quantity of cubic Eu 2 O 3 . Here, the reaction of solutions in a nitrogen atmosphere or diluted with Eu 2 O 3 provides control of the product phase composition and reduces the quantity of the monoclinic phase. Transmission electron microscopy imaging shows that the Eu 2 O 3 end products are highly porous aggregates of nanocrystalline particles. Electrospraying of reactive solutions onto different substrates followed by short annealing makes the preparation of Eu 2 O 3 materials with diverse morphologies possible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Shifting temporal dynamics of human mobility in the United States

In this paper we analyze the average hourly temporal dynamics of human mobility in the United States from 2019 to 2020. We discuss how large decreases in human mobility nonuniformly effect the daily temporal dynamics of aggregate human behavior. The data used are weekly activity patterns for POIs from 2019 to 2020 in the United States, provided by SafeGraph and made openly available to academic and research institutions. We use clustering methods to create metrics describing how human activity changes throughout the day/week at the county and national levels. In response to significant mobility reductions starting March 2020, daily temporal patterns of human activity changed nonuniformly. Morning activity started later, and evening activity started earlier in 2020 compared to 2019, and temporal behavioral patterns on weekdays began to look more similar to weekends. The changes in daily temporal behavior persisted throughout the year even as total mobility levels recovered. The results provide insights on the changes in human behavior in response covid-19 policies and illustrate influences on social systems, health, and transportation networks.

99 GENERAL AND MISCELLANEOUS↗

Modeling the effects of carbon payments and forest owner cooperatives on carbon storage and revenue in Pacific Northwest forestlands

The forestlands of the Pacific Northwest store a significant amount of carbon, provide wildlife habitat, generate revenue for forestland owners, and provide for sustainable generation of forest products. Promoting forest carbon storage is increasingly recognized as a key management objective to mitigate climate change. We developed an agent-based model to examine the effectiveness of current land management practices in sustaining forest carbon storage and revenue flows, and the utility of market-based carbon payments in improving carbon storage while also improving revenue generation in the Pacific Northwest. We find that while market based incentives can improve both forest carbon storage and revenue, they are limited in their benefit to small forest owners. Our results highlight the crucial need for additional policy interventions to reduce barriers to market access for small private owners and ensure sustainable forest management in this region. We further show that a forest owners’ cooperative, which aggregates small owners to reduce the costs of participating in a carbon market, can help overcome these barriers and result in both greater carbon storage and a more equitable benefit from carbon payments.

54 ENVIRONMENTAL SCIENCES↗

Factors controlling pore network development of thermally mature Early Palaeozoic mudstones from the Baltic Basin (N Poland)

Understanding the formation of pore space, especially in low porosity shales (as source rocks and as unconventional resources), is critical to the oil and gas industry, since pores control the space available for hydrocarbon and participate in hydrocarbon transport. We examined 87 Ordovician and Silurian mudstone samples collected from four wells located in the Pomeranian part of the Baltic Basin (northern Poland), one of the primary Polish targets for hydrocarbon exploration. These samples represent the Pelplin, the Paslek, the Jantar, the Prabuty, and the Sasino Formations, which still requires more detailed porosity studies. Our study aimed to identify factors controlling porosity development, by applying bulk techniques (organic petrology and TOC analyses, quantitative mineralogy, and porosimetry) as well as nano- to microscale techniques (thin section petrography, electron microscopy). The studied samples are mainly argillaceous mudstones. The results of porosimetry measurements, combined with image analysis, indicate that the pores of all studied rocks are dominated by micropores (pores < 2 nm in diameter), mesopores (2-50 nm in diameter) and small macropores. The SEM images showed three main pore types: a) voids related to clay mineral aggregates, b) pores inside organic matter particles, and c) pores between other mineral grains. In the Jantar and Sasino mudstones, the organic matter content and its thermal maturity control porosity. The occurrence of solid bitumen in the rocks from these formations reduces samples’ mesoporosity because of the pore-clogging effect. In contrast, in the Paslek and Prabuty Formations, there is low organic matter content and specific surface area and the volume of mesopores increase with clay minerals content. In the Pelplin mudstones, there are no prevailing factors controlling porosity. As a result, we suggest that a combination of SEM image analysis and dual liquid porosity (DLP) measurements is a powerful method to assess porosity available for petroleum flow in mudstones.

03 NATURAL GAS↗

Solid additive engineering enables high-efficiency and eco-friendly all-polymer solar cells

Currently, morphology optimization of all-polymer solar cells (all-PSCs) strongly depends on the use of solvent additives, which are usually highly toxic and harmful to the environment and human health. Here, we report a green and volatile solid additive, 2-methoxynaphthalene (2-MN). It was found that the incorporation of 2-MN into a PM6:PY-DT blend can effectively manipulate the aggregations of PM6 and PY-DT during film depositing and thermal annealing processes and results in highly ordered molecular packing and favorable phase-separated morphology. Consequently, a record-high efficiency of 17.32% is achieved for the PM6:PY-DT device. Moreover, 2-MN-processed all-PSCs were fabricated by using non-halogenated solvent. High efficiencies of 17.03% and 16.67% are obtained for all-PSCs fabricated under nitrogen atmosphere and ambient conditions, respectively. Finally, our work shows that the utilization of 2-MN as a green and solid additive is a simple and feasible strategy to optimize the morphology and sheds new light on eco-friendly fabrication and application of all-PSCs.

14 SOLAR ENERGY↗

Pd modified prussian blue frameworks: Multiple electron transfer pathways for improving catalytic activity toward hydrogenation of nitroaromatics

Prussian blue analogs (PBAs) exhibit potential as low-cost and eco-friendly nanocatalysts that can be fabricated with ease. However, the PBA framework structure suffers from poor electronic conductivity, which limits the catalytic efficiency for this class of materials. Noble metals represent an alternative class of materials that display inherent catalytic activity but suffer from aggregation, ultimately reducing the amount of accessible catalytic sites. Herein, we demonstrate a combinatory approach that circumvents the known disadvantages with these classes of catalytic materials in which PBA-supported nanocatalysts were synthesized. These composite materials exhibit excellent catalytic activity for the reduction of nitroaromatics to aminoaromatics while displaying long-term cycling stability, which is attributed to the availability of multiple electron transfer pathways. Overall, this work opens the study on the assembly of PBA-supported heterogeneous nanocatalysts and potentially paves the way toward future applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Differentiating supported platinum single atoms, clusters and nanoparticles by styrene hydrogenation

Supported metal catalysts often consist of metal sites ranging from nanoparticles to subnanometer clusters and single atoms. It remains a necessity to differentiate these sites to guide design of optimal catalysts. Here we report a simple method to assess the distribution of metal active sites in catalyst samples. The method takes the advantage of the structure sensitivity of styrene hydrogenation over titania supported platinum (Pt) catalysts with Pt aggregates varied from single atom to ~1.40 nm nanoparticles. The physicochemical properties were characterized by STEM, XPS, XANES, H2-TPR and CO-chemisorption measurements. The reactivity of Pt sites was quantified by styrene hydrogenation at ambient conditions. The nanometer-sized Pt clusters have significantly higher activity than Pt nanoparticles, sub-nanometer clusters or isolated single atoms. Additionally, the relationship between activity and structural/electronic properties of Pt sites influenced by particle sizes was discussed. Similar relationship was found in the carbon supported Pt catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A systematic analytical framework for multi-source municipal solid waste characterization for energy recovery

Advancing municipal solid waste (MSW) management from disposal-oriented practices toward circular, value-driven systems requires standardized methodologies capable of identifying material composition and resource recoverable potential at the point of generation. Despite extensive research, MSW characterization remains fragmented due to inconsistences in sampling methodologies, waste sorting categories, and temporal coverage across previous studies which limit cross-site comparability, reproducibility, and constrain the reliable evaluation of potential resource recovery pathways. This lack of consistency has hindered the development of a unified framework for MSW characterization and resource assessment. This study introduces a standardized, field-validated protocol for MSW sampling and composition analysis that ensures consistent, traceable data across diverse waste sources. The protocol integrates randomized spatial sampling, systematic material sorting, and controlled subsampling for multi-site and multi-season field campaigns. Validation included MSW collection from residential, grocery, restaurant, and school MSW streams across five U.S. states, including Maryland, Idaho, Virginia, Ohio, and Mississippi, to demonstrate the protocol’s ability to identify source-based composition patterns relevant to resource recovery applications. Grocery and restaurant streams were dominated by food waste and high-moisture organics, while school waste contained higher paper content and residential waste showed greater heterogeneity. Aggregation into energy-relevant fractions highlighted practical recovery pathways via anaerobic digestion or gasification, supporting data-driven planning, policy, and circular economy strategies for sustainable waste management across waste sources.

09 BIOMASS FUELS↗

Data-driven acoustic measurement of moisture content in flowing biomass

Measuring the moisture content in flowing biomass is critical to processes such as liquid biofuel conversion, such as biogasoline, biodiesel, bio jet kerosene, etc. However, biomass tends to flow in aggregates, which results in significant inhomogeneities in the amount of biomass flowing in front of a sensor at a given time, and there can be significant overlap in the material properties of dry vs wet biomass, leading to poor signal-to-noise ratio. We present a technique for identifying biomass moisture content using a series of acoustic pitch-catch measurements to quantify the sound speed and acoustic amplitude through the biomass, in conjunction with classical machine learning techniques, including Naive Bayes, Random Forest, and K-Nearest Neighbors classification. We amplify the differences between the acoustic measurements in different moisture levels by collecting a series of pulse-echo measurements, which we sort in order of ascending sound speed. We test the accuracy of the technique on experimentally-prepared batches of corn stover biomass with specified moisture levels and measure the average error in the estimated moisture level as a function of the number of pitch-catch measurements used. We observe average estimation errors as low as 6.7% by increasing the number of measurements and optimizing the hyperparameters. This work presents a novel method determining moisture content in flowing biomass with inhomogeneous flow. Additionally, this technique has application in optimizing biomass conversion processes, as well as other fields including, paper production, natural fiber processing, and mineral extraction.

09 BIOMASS FUELS↗

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗