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At least 361 records · Page 20

Investigations of water/oxide interfaces by molecular dynamics simulations

Water/oxide interfaces are ubiquitous on earth and show significant influence on many chemical processes. For example, understanding water and solute adsorption as well as catalytic water splitting can help build better fuel cells and solar cells to overcome our looming energy crisis; the interaction between biomolecules and water/oxide interfaces is one hypothesis to explain the origin of life. However, knowledge in this area is still limited due to the difficulty of studying water/solid interfaces. As a result, research using increasingly sophisticated experimental techniques and computational simulations has been carried out in recent years. Although it is difficult for experimental techniques to provide detailed microscopic structural information, molecular dynamics (MD) simulations have satisfactory performance. In this report, we discuss classical and ab initio MD simulations of water/oxide interfaces. Generally, we are interested in the following questions: How do solid surfaces perturb interfacial water structure? How do interfacial water molecules and adsorbed solutes affect solid surfaces and how do interfacial environments affect solvent and solute behavior? Finally, we discuss progress in the application of neural network potential based MD simulations, which offer a promising future because this approach has already enabled ab initio level accuracy for very large systems and long trajectories.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

High Throughput Argon-37 Field System

We report Pacific Northwest National Laboratory (PNNL) has developed a unique fieldable 37 Ar measurement system designed to measure 37 Ar activity concentrations from soil gas samples to detect above ground and underground nuclear explosions. The Argon-37 Field System is modular in design to accommodate both chemical processing and nuclear detection. The system can be packed into shipping crates and shipped to a location near where the sampling is taking place. The system can process six 2-m 3 whole-air samples in 24 hours and can measure the 37 Ar activity in each of the samples using six proportional counters. The proportional counters, designed and built at PNNL, are surrounded with both active and passive shielding to reduce background and can achieve a minimum detection concentration of 10 mBq/m 3 of 37 Ar in whole-air equivalent. The Argon-37 Field System has undergone extensive testing against rigorous requirements to assure the system meets the needs of the noble gas nuclear explosion monitoring community.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Benzene homologues contaminants in a former herbicide factory site: distribution, attenuation, risk, and remediation implication

Benzene homologues often used as organic raw materials or as detergents in chemical industry are prone to accidental release into the environment which can cause serious long-term soil pollutions. In a large former herbicide factory site, we investigated 43 locations for benzene homologues contaminations in soil, soil gas, and groundwater; and studied the hydrogeological conditions. An inverse distance weighted interpolation method was employed to determine the pollutants three-dimensional spatial distribution in the soils. Results showed that benzene homologues residues were mainly originated from the herbicide production workshop; and that the pollution had horizontally expanded at the deeper soil layer. Contaminants had already migrated 15m downward from ground surface. Contaminant phase distribution study showed that NAPL was the primary phase (>99%) for the pollutants accumulated in the unsaturated zone; while it had not migrated to groundwater. The primary mechanism for contaminant transport and attenuation included dissolution of “occluded” NAPL into pore water and pollutant volatilization into soil pore space. Risk assessment revealed that the pollutants brought unacceptable high carcinogenic and non-carcinogenic risks to public health. In order to convert this former chemical processing factory site into a residential area, a remediation to the polluted production workshop sites is urgently required.

Yang, Shuo↗

Enhanced accuracy through ensembling of randomly initialized auto-regressive models for dynamical systems

Computational mechanics simulations using traditional finite element methods (FEM) require prohibitively expensive computational resources for real-time engineering applications, design optimization, and digital twin implementations. While machine learning (ML) surrogate models offer significant computational speedups, autoregressive ML models for time-dependent mechanical systems suffer from error accumulation that compromises long-term prediction reliability - a critical concern for engineering applications where accuracy over extended time horizons is essential for safety and performance assessments. Here, we propose a deep ensemble framework specifically designed to address this challenge in computational mechanics applications, where multiple ML surrogate models with random weight initializations are trained in parallel and their predictions aggregated during inference. This approach leverages statistical diversity to maximize information gain from a fixed set of training data and to mitigate error propagation, while maintaining the computational efficiency that makes ML surrogates attractive for engineering practice. We validate the framework on three representative problems spanning critical areas of computational mechanics: stress field evolution in heterogeneous microstructures under complex loading (relevant to advanced materials design and composite analysis), planetary-scale shallow water dynamics (applicable to environmental and geotechnical engineering), and Gray-Scott reaction-diffusion systems (relevant to mass transport and chemical process engineering). Across all test cases, the ensemble approach demonstrates consistent error reduction of 15-33% compared to individual models. The codes for this work are available on GitHub (https://github.com/Graham-Brady-Research-Group/AutoregressiveEnsemble_SpatioTemporal_Evolution).

autoregressive prediction↗

Critical analysis of velocimetry methods for particulate flows from synthetic data

Particle tracking methods that extract high-fidelity particle velocity data from high speed video of particle laden flows is a common experimental technique applied to chemical processes. These measurements are used to better understand the motion of particles and fluids in complex systems and create data against which computational models are validated. However, the methods, codes, and experimental setups all have limitations. It is imperative that practitioners verify the methods and their implementation as well as understand the limitations of experimental setups. This work focuses on quantifying the visible depth of field in a high particle concentration fluidized bed. Following a precedent set by the particle imaging velocimetry community, a particle velocity field is manufactured using a computational fluid dynamics and discrete element method simulation. Photo realistic high-speed videos are rendered based on the simulated data using the three-dimensional creation software Blender. Particle velocities are extracted from the synthetic high-speed videos using three variants of Particle Tracking Velocimetry and Optical Flow Velocimetry methodologies. Here, the tracked results are then compared to the known solution, quantifying the error associated with the assumed visible depth. The results indicate that at depth of one particle diameter, all three particle tracking codes give accurate measurements, largely within 5%. However, the error increases when the full bed video measurements are compared to the known solution at one particle diameter, i.e., mimicking a validation study. Finally, for some statistics the constant depth assumption only increases the error slightly, for others significantly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Microwave-assisted conversion of methane over H-(Fe)-ZSM-5: Evidence for formation of hot metal sites

We report microwave-assisted catalysis offers great promise as an “intensified” technology for chemical processing. The present study investigates microwave-assisted direct conversion of methane with focus on the design and evaluation of microwave-sensitive H-(Fe)ZSM-5 catalysts for the existence of hotspot formation. Isomorphous substituted H-(Fe)ZSM-5 catalysts are tuned to identify key design parameters that control their microwave sensitivity. Increasing the amount of Al and Fe substitution in the zeolite lattice is found to result in higher microwave sensitivity due to improved dielectric properties and hence facilitated microwave heating of the catalyst bed. Comparison between the performance of these catalysts in a conventional thermally-heated (CH) fixed-bed and a microwave (MW) reactor allows identification of the effect of microwave irradiation on the catalyst activity. Methane conversion is drastically enhanced at MW conditions (from 3% at CH conditions to 40% at MW conditions), indicating accelerated methane activation over the metal site and hence suggesting a hot metal site despite much lower catalyst bulk temperature measured in the microwave reactor. The existence of metal hotspot formation is further supported by the reaction product distribution and spent catalyst analysis. At MW conditions, C 2 (ethane and ethylene) and coke (mainly carbon nanotubes and nanofibers) selectivity are much higher at the cost of aromatics make compared to CH conditions. This can be explained by reduced aromatization activity at the Brønsted acid sites of the zeolite due to its lower relative temperature of the zeolite. The much-enhanced metal aggregation observed in the spent catalyst from the MW reactor along with the distribution of coke species further confirms the existence of selective heating (i.e. hotspot formation) occurring at the active metal site in the catalyst at MW conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Rewiring yeast metabolism for producing 2,3-butanediol and two downstream applications: Techno-economic analysis and life cycle assessment of methyl ethyl ketone (MEK) and agricultural biostimulant production

Rising concerns for sustainability and global climate change have driven the development of sustainable pro- duction pathways for biofuels and chemicals from lignocellulosic biomass via integrated biological and chemical processes. We constructed an engineered Saccharomyces cerevisiae capable of producing 2,3-butanediol (2,3-BDO) from glucose without accumulating ethanol and glycerol, which hinder downstream processing of 2,3-BDO, through extensive metabolic reprogramming. Specifically, we introduced heterologous 2,3-BDO biosynthetic enzymes and deleted the major isozymes of ethanol and glycerol biosynthetic enzymes. In addition, we introduced an NAD + regenerating Pyruvate-Malate (PM) cycle and enhanced the NAD + regenerating capability of the PM cycle to resolve the redox imbalance from the deletion of ethanol and glycerol production pathways. The resulting engineered yeast produced 109.9 g/L of 2,3-BDO with a productivity of 1.0 g/L/h and a yield of 0.36 g/ g glucose in a fed-batch fermentation. We also conducted techno-economic analysis (TEA) and life cycle assessment (LCA) of the production of methyl ethyl ketone (MEK) through catalytic dehydration of 2,3-BDO. A TEA based on the experimental results indicated that the minimum product selling price (MPSP) was estimated to be $1.90/kg. Regarding cradle-to-grave LCA, 100-year global warming potential (GWP100) and fossil energy consumption (FEC) were found to be 0.37 kg CO 2 eq/kg and 3.1 MJ/kg, respectively. These results demonstrated the feasibility of cost-competitive and sustainable bio-based MEK production via yeast fermentation. In addition, we explored the possibility of using the fermentation broth containing 2,3-BDO as a biostimulant inducing drought tolerance in plants. As a result, the yeast 2,3-BDO fermentation broth can induce drought tolerance in Arabidopsis thaliana without a complicated purification process.

2,3-butanediol (2,3-BDO)↗

Bioelectrochemical reactor to manage anthropogenic sulfate pollution for freshwater ecosystems: Mathematical modeling and experimental validation

Anthropogenic sulfate loading into otherwise low-sulfate freshwater systems can cause significant ecological consequences as a biogeochemical stressor. To address this challenge, in situ bioremediation technologies have been developed to leverage naturally occurring microorganisms that transform sulfate into sulfide rather than implementing resource-intensive physio-chemical processes. However, bioremediation technologies often require the supply of electron donors to facilitate biological sulfate reduction. Bioelectrochemical systems (BES) can be an alternative approach for supplying molecular hydrogen as an electron donor for sulfate-reducing bacteria through water electrolysis. Although the fundamental mechanisms behind BESs have been studied, limited research has evaluated the design and operational parameters of treatment systems when developing BESs on a scale relevant to environmental systems. Further, this study aimed to develop an application-based mathematical model to evaluate the performance of BESs across a range of reactor configurations and operational modes. The model was based on sulfate transformation by hydrogenotrophic sulfate-reducing bacteria coupled with the recovery of solid iron sulfide species formed by the oxidative dissolution of dissolved ferrous iron from a stainless steel anode. Sulfate removal closely corresponded to the rate of electrolytic hydrogen production and hydraulic residence time but was less sensitive to specific microbial rate constants. The mathematical model results were compared to experimental data from a pilot-scale BES tested with nonacidic mine drainage as a case study. The close agreement between the mathematical model and the pilot-scale BES experiment highlights the efficacy of using a mathematical model as a tool to develop a conceptual design of a scaled-up treatment system.

54 ENVIRONMENTAL SCIENCES↗

Hydro-mechanical behavior of heated bentonite buffer for geologic disposal of high-level radioactive waste: A bench-scale X-ray computed tomography investigation

The behavior of heated bentonite buffer is critical for the security and long-term performance of a geological repository for high-level radioactive waste (HLW). While laboratory column experiments have been conducted to investigate compacted bentonite and coupled THMC (thermal-hydro-mechanical and chemical) processes for a moderate temperature range of up to 100 °C, data for a higher temperature range are limited. Understanding bentonite behavior and coupled THMC processes under higher temperatures (e.g., up to 200 °C) could allow for a more economic repository design and would expand the data and knowledge base for more reliable modeling. In this study, a bench-scale experiment was conducted in a compacted bentonite column experiencing both heating up to 200 °C in the center and hydration from a sand-clay boundary surrounding the column. During the experiment run for 1.5 years, frequent X-ray computed tomography (CT) scanning of bentonite provided insights into the spatiotemporal evolution of (1) hydration/dehydration, (2) clay swelling/shrinkage, (3) displacement, and (4) mineral precipitation. After the experiment, a comprehensive post-dismantling characterization of bentonite samples was conducted. Results showed that the bentonite hydration was axi-symmetrical despite the initial heterogeneity due to packing, confirming the ability of bentonite to seal fast flow/transport paths. Further, compared to a non-heated control experiment, the heated column showed greater CT density variations along the radial distance, indicating that homogenization of bentonite might be more difficult if a temperature gradient is maintained in the repository. Precipitation of an anhydrite layer occurred in the inner hot zone, pointing to potential concerns about salt precipitation causing canister corrosion. Ultimately, the experiments provided a high-resolution window into the strongly dynamic and coupled behavior of bentonite exposed to heating, hydration and swelling, which will be valuable for improving modeling of coupled processes, especially for the early state of a HLW repository.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Applications of the Dulmage–Mendelsohn decomposition for debugging nonlinear optimization problems

Nonlinear modeling and optimization is a valuable tool for aiding decisions by engineering practitioners, but programming an optimization problem based on a complex electrical, mechanical, or chemical process is a time-consuming and error-prone activity. Therefore, there is a need for model analysis and debugging tools that can detect and diagnose modeling errors. One such tool is the Dulmage–Mendelsohn decomposition, which identifies structurally under- and over-determined subsets in systems of equations and variables by partitioning the bipartite graph of the system. This work provides the necessary background to understand the Dulmage–Mendelsohn decomposition and its application to the analysis of nonlinear optimization problems, demonstrates its use in diagnosing a variety of modeling errors, and introduces software implementations for analyzing nonlinear optimization problems in the Pyomo and JuMP algebraic modeling languages.

42 ENGINEERING↗

A data-driven multiscale model for reactive wetting simulations

Here, we describe a data-driven, multiscale technique to model reactive wetting of a silver–aluminum alloy on a Kovar™ (Fe-Ni-Co alloy) surface. We employ molecular dynamics simulations to elucidate the dependence of surface tension and wetting angle on the drop’s composition and temperature. A design of computational experiments is used to efficiently generate training data of surface tension and wetting angle from a limited number of molecular dynamics simulations. The simulation results are used to parameterize models of the material’s wetting properties and compute the uncertainty in the models due to limited data. The data-driven models are incorporated into an engineering-scale (continuum) model of a silver–aluminum sessile drop on a Kovar™ substrate. Model predictions of the wetting angle are compared with experiments of pure silver spreading on Kovar™ to quantify the model-form errors introduced by the limited training data versus the simplifications inherent in the molecular dynamics simulations. The paper presents innovations in the determination of “convergence” of noisy MD simulations before they are used to extract the wetting angle and surface tension, and the construction of their models which approximate physio-chemical processes that are left unresolved by the engineering-scale model. Together, these constitute a multiscale approach that integrates molecular-scale information into continuum scale models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Lignin-based jet fuel and its blending effect with conventional jet fuel

Sustainable aviation fuels (SAFs) must demonstrate specific physical and chemical properties as well as material compatibility (i.e., seal swell) to be used as an aviation turbine fuel. Several alternative jet fuels incorporated in ASTM D7566 are comprised mainly of n/iso-alkanes and can only be blended up to 50 vol% due to material compatibility and density issues. Prior work illustrated the ability of cycloalkanes to replace aromatics’s role in material compatibility. Here, we report the first archival documentation of a feedstock and chemical process to yield a product composition able to complement existing SAF ASTM D7566 annexes. A lignin-based jet fuel (LJF) blend components is generated and composed of mostly C7-C18 mono, di, and tri-cycloalkanes. The neat LJF was blended with conventional jet fuel at 10 vol% (LJF blend) to simulate an ASTM “Fast Track” evaluation process. Fuel properties that are critical to engine operability (ATSM D4054 Tier 3 & 4) were either predicted or experimental tested based on the volume availability. All LJF blend’s critical properties fall within the experience range of conventional jet fuel, with o-ring swelling exceeding the typical range of conventional fuels. Here, these results in total support the potential use of this LJF pathway to complement other SAF pathways and achieve 100% drop-in SAF.

09 BIOMASS FUELS↗

Meeting sustainable aviation fuel policy targets through first generation corn biorefineries

This paper investigates options to produce low carbon and renewable aviation fuel through existing commercial first generation biorefineries, which could be strategic given the rapid growth of the electric vehicle market for personal mobility. We evaluate the environmental impact of retrofitting a first generation biorefinery to produce aviation fuel through chemical process simulation. The analysis investigates the potential for reducing greenhouse gas (GHG) emissions and displacing fossil fuel (petroleum-based jet kerosene) in the aviation fuel industry through using corn feedstocks in dry grind biorefineries to convert sugars to 1,4-dimethylcyclooctane (DMCO), which qualifies as renewable jet-A fuel blend and is infrastructure compatible. Further, the average life cycle GHG emissions for a baseline case and a scenario with carbon capture and storage (CCS) of fermentative CO 2 are 36 and 5g CO 2 e/MJ DMCO, respectively. Investment in CCS at the biorefinery and adoption of crop best management practices on farms are essential for mitigating the risks of induced land use change GHG emissions for existing corn biorefineries. DMCO as a low-carbon aviation fuel could be a strategic fuel product for retrofitting existing corn dry grind facilities. For aviation fuel supply, corn dry grind facilities could displace about 12% of jet fuel demand in the near term, which would meet sustainable aviation fuel policy targets. We conclude that corn-to-DMCO shows promise for near-term low-carbon fuel markets in the aviation industry and as a bridge to developing biomass-based jet fuel in the future.

09 BIOMASS FUELS↗

Design and requirements of a hydrogen component reliability database (HyCReD)

Hydrogen technologies are expected to play a key role in the decarbonization of several sectors including energy storage and transportation. Rigorous investigation and quantification of the risk and reliability issues associated with hydrogen technologies will be critical to ensuring both their wider adoption and safe, economical operations. Quantitative risk assessment (QRA) is an important tool that has been used to enable the safe deployment of many engineering systems, including hydrogen fueling stations and hydrogen storage systems. However, QRA studies require reliability data which is currently lacking for expanding applications of hydrogen systems. Here, to address this gap, we present a new structure for a hydrogen component reliability database (HyCReD) that can be used to generate reliability data to be used in QRA, reliability, safety studies, maintenance planning, and more. Building on our previous work examining four major hydrogen safety data collection tools (West et al., 2022) [1], our approach in this work was to consult scientific literature on reliability data collection as well as a number of existing reliability engineering databases in the oil & gas, chemical processing, and nuclear power plant sectors. The evaluation of these databases led to identifying best practices to be implemented in a data collection framework for a hydrogen component reliability database. Based on these best practices, a set of 24 requirements for the proposed database are presented, covering its characteristics and the types of data to be collected. We define the structure of the HyCReD database and 25 data elements to be collected, spanning system description, failure, shutdown, or near-miss events, and maintenance events. The data elements are then defined according to international standards used in the safety and reliability practice and potential choice lists are provided for each field. Since this database is being piloted for hydrogen fueling stations, a generic station component hierarchy developed by West (2021) [2] is used to standardize system data. Finally, we demonstrate populating the database with information extracted from five narrative reports on hydrogen fueling station incidents.

08 HYDROGEN↗

Universal correlation for falling film evaporation on a horizontal plain tube

Falling film evaporators are widely used in many industrial sectors, such as desalination, refrigeration & air-conditioning, and chemical processes. The heat transfer correlations suitable for wider operating conditions and various refrigerants are useful for designing falling film evaporators. Although many researchers developed the correlation for their own experimental data, there is a limited universal correlation for falling film evaporators. In addition, the dry-out effects were not considered in the available universal correlations. Here, therefore, this study proposes a new correlation for falling film evaporation on a single horizontal plain tube, which includes nucleate boiling, film evaporation, and dry-out effects. The proposed correlation covers a wider range of operating conditions (Heat flux is of 2.5–168kWm -2 , and film Reynolds number is up to 3500), various refrigerants (R134a, R245fa, R1234ze(E), R123, R32, R410A, R290, and R600a), and the large number of experimental data (1101 data in total). The proposed correlation is validated against the available standard correlation with regard to mean absolute deviation (MAD) and R 2 values. The MAD of the new correlation is about 10%, respectively, which is the lowest among other correlations. Similarly, the R 2 showed a value of 0.92, which is the highest compared to the existing correlations. A simplified correlation is also proposed.

42 ENGINEERING↗

Protonation Dynamics of Confined Ethanol–Water Mixtures in H-ZSM-5 from Machine Learning-Driven Metadynamics

Zeolites are indispensable heterogeneous catalysts in industrial chemical processes, valued for their strong Brønsted acidity, well-defined microporous frameworks, and tunable pore structures. Their catalytic activity arises primarily from Brønsted acid sites (BAS), typically present as bridging hydroxyl groups (Si–OH–Al). Under aqueous reaction conditions, these protons interact dynamically with water and alcohol molecules, leading to complex solvation and protonation behavior within confined pores. In this study, we investigate the protonation equilibrium occurring between ethanol and water at the BAS of acidic zeolites under varying hydration levels, i.e., C2H5OH–(H2O)n, n=1–4. Local structure was analyzed through an adaptive-learning global optimization algorithm, while enhanced sampling molecular dynamics simulations with Well-Tempered Metadynamics (WMetaD) and machine learning interatomic potentials (MLPs) provide free-energy surfaces (FES) at variable hydration levels. The results reveal a strong dependence of proton localization on the degree of hydration. At low hydration (1 water molecule), the proton resides predominantly on ethanol; with 2 water molecules, it shifts toward water, and at higher hydration (3 or more water molecules), it becomes extensively delocalized over the water cluster. These findings underscore the critical role of solvation in modulating acid site behavior and suggest that a minimum of three water molecules is necessary to fully stabilize the proton on water within the zeolite framework. This solvation threshold has significant implications for catalytic processes, particularly in biomass conversion reactions where alcohol protonation is a key step in dehydration mechanisms.

machine learning↗

Iron-modified biochar-based bilayer permeable reactive barrier for Cr(VI) removal

In this study, iron (Fe)-modified biochar (FeBC) has been developed to remove hexavalent chromium (Cr(VI)) from groundwater and is suitable for use in permeable reactive barriers (PRBs). However, Cr(VI) removal behavior and chemical processes in FeBC-based PRBs are not fully understood, and the potential for Fe release has not been addressed. In this study, three FeBC-based PRBs were assessed in column experiments for 563 days with respect to their ability to remove Cr(VI). Bilayer column filled with FeBC+limestone and BC+limestone in two separate layers (FeBC_Ca_BC) showed the best performance in terms of Cr(VI) removal with a low treatment cost. The corrosion of FeBC was mainly related to pH and Cr(VI) concentration rather than flow rate. Leached Fe was attenuated by BC and limestone and reutilized in FeBC_Ca_BC. Cr(VI) was reduced to Cr(III) and then adsorbed or precipitated on the biochars. Cr and Fe formed inner-sphere complexes and then transformed from double corner sharing to edge sharing. During the reaction, Cr penetrated from the surface to the interior of the biochars and became a more stable species. This study provides evidence of the effectiveness of a new combination of biochars for Cr(VI) removal and insights into the reaction mechanisms.

42 ENGINEERING↗

Identification of uranium oxidation states using oxygen K-edge scanning transmission X-ray microscopy

The field of nuclear forensics is growing in importance, and the increasing capabilities at synchrotron radiation light sources enable non-destructive characterization of oxide particles with better spatial, compositional, and oxidation state speciation resolution than ever before. Here, uranium oxide particles derived from multiple wet chemical processing methods were examined using a scanning transmission X-ray microscope (STXM), and a weakly-supervised method was developed to automatically analyze the collected data. Multiple uranium oxidation states were observed and quantified within and between samples, yielding information about differences between particles produced via the various processing routes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗