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At least 235 records · Page 13

Development of an interatomic potential for the W–Ta system

A physics-inspired, data-driven interatomic potential framework for multi-element system is presented. This potential is based on the generalization of the embedded atom method potential and systematically incorporates two-, three- and many-body effects. Different atomic environments are described by atom-centered Gaussian basis sets which provides sufficient flexibility to capture diverse atomic environments and allows for easy optimization of the free parameters. An interatomic potential model for the tungsten–tantalum (W–Ta) system is developed using this framework by training on data from ab initio density functional theory (DFT) calculations. The potential is thoroughly tested at various compositions by comparing bulk and defect properties from experimental and DFT data. It is shown that the potential predicts the elastic constants, defect properties, such as vacancy and interstitial formation energies, core structures of dislocations, and melting points for both pure and tantalum and tungsten with an accuracy comparable to other single element machine learning based potentials. In conclusion, the potential model is used to investigate the formation energies of ordered alloys and vacancy formation energies for chemically random W–Ta alloys.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Thermodynamic assessment of nonstoichiometric oxides for solar thermochemical fuel production

Two-step solar thermochemical cycling (STC) based on nonstoichiometric oxides is an ideal means of solar fuel (e.g., H 2 , CO) production. Screening of nonstoichiometric oxides with excellent thermodynamic performance is key to achieving high solar-to-fuel efficiency. However, application-driven materials assessment intended for reactor-level solar fuel production performance requires mimicking realistic operating conditions of on-sun tests in a laboratory setting, which makes oxides assessment and screening an onerous task to accomplish experimentally. In this work, a rapid assessment and screening model of nonstoichiometric oxides for two-step solar thermochemical cycling assuming fixed-bed flow pattern and quasi-equilibration of the solid with the flowing gas phase is developed, with solar-to-fuel efficiency being the target function of optimization. The model accounts for the thermodynamic parameters of oxide materials and typical operating conditions of experimental thermochemical cycling. This study employed the model to explore and compare two groups of typical nonstoichiometric oxides (CeO 2 - and (LaSr)MnO 3 -based) for their maximum efficiency under their uniquely optimized cycling conditions. The results show that CeO 2 can reach a maximum efficiency of 12.9% at reduction temperature of 1500 °C, which is superior to other candidate materials, including 20 mol% Zr-doped CeO 2 (10.1%), La 0.6 Sr 0.4 MnO 3 (2.5%) and La 0.8 Sr 0.2 MnO 3 (3.3%). Even when the reduction temperature is lowered to 1350 °C, ceria yields the highest efficiency amongst the candidate STC materials considered. The optimal cycling strategy depends on the inherent thermodynamic properties of the oxides. This approach serves as a framework for assessing the maximum efficiency and optimal conditions of candidate thermochemical materials within a range of constraints rather than comparing materials under arbitrary cycling conditions, which may inherently favor one material over another. For oxidation temperatures below 800–1000 °C, the model could be further improved by considering reaction kinetics.

14 SOLAR ENERGY↗

Local Structural Coherence and Interfacial Charge Transfer in Cu 2 ⁢S/Mo⁢S 2 Heterostructure

Precise control over electronic coupling at nanoscale interfaces is critical for designing materials with tunable charge-transfer behavior and catalytic function. Heterostructures with locally coherent interfaces provide a platform for interrogating interfacial charge redistribution in coupled material systems. Here, we report Cu 2 ⁢S/Mo⁢S 2 heterostructures exhibiting nanoscale crystallographic alignment, which are synthesized through a rapid thermal transformation pathway. We employed electrochemical reduction reactions to probe interfacial charge transfer, revealing shifts in product distribution attributable to modified interfacial energetics, even in the absence of optimized catalytic performance. The observed formate Faradaic efficiency suggests that interfacial electronic modulation in the heterostructure shifts product selectivity towards formate, highlighting how interface-driven electronic modulation can direct reaction pathways and influence product selectivity. Optimizing catalyst loading, architecture, and reactor configuration will be critical for future improvement. Structural and compositional integrity were confirmed through powder x-ray diffraction, x-ray photoelectron spectroscopy, and high-resolution transmission electron microscopy. Electron transfer between Cu 2 ⁢S and Mo⁢S 2 domains was further evaluated by electrochemical impedance spectroscopy, while selected-area electron diffraction revealed local crystallographic alignment consistent with a local epitaxial relationship at the Cu 2 S/Mo⁢S 2 heterointerface. To illustrate the broader applicability of this approach, a Zn⁢S/Mo⁢S 2 heterostructure was also synthesized using the same microwave strategy, confirming the generalizability of interfacial engineering principles across metal sulfide-Mo⁢S 2 systems. Collectively, these findings demonstrate that controlled local epitaxial alignment serves as an effective design principle for tuning interfacial energetics and catalytic reactivity in complex heterostructure materials.

carbon capture & utilization↗

Electrically Conductive Amine Functionalized Reduced Graphite Oxide Foam for CO 2 Removal from the Air

Rapid regeneration of CO 2 adsorbents is critical to improving the productivity of direct air capture (DAC) systems. In this study, we codesigned a material to have appropriate electrical conductivity and CO 2 adsorption properties to enable efficient CO 2 capture from air. Specifically, we present a poly(ethylenimine) (PEI)-impregnated thermally annealed graphite oxide (TAGO900) foam adsorbent tailored for vacuum-assisted electrically driven thermal swing adsorption (V-ETSA). This structured adsorbent leverages the high electrical conductivity of the reduced graphite oxide framework to enable fast and direct heating of the adsorbent material by electrical resistance heating (Joule heating). An optimal sample, 40 wt % PEI (molecular weight 25k) impregnated TAGO900, shows the best balance between adsorption capacity (1.54 mmol g –1 ) and adsorption rates (0.016 mmol g –1 min –1 ) using fixed bed breakthrough experiments at 25 °C and 70% RH using 50 sccm 400 ppm of CO 2 /N 2 flow. Compared to conventional temperature vacuum swing adsorption (TVSA), the V-ETSA approach achieves substantially faster CO 2 desorption, achieving average desorption rates (including cooling time) of 0.09 mmol g –1 min –1 ─approximately 2.5 times faster than TVSA under similar operating conditions. The maximum desorption rate reaches 0.23 mmol/g/min during the desorption stage. These results underscore the importance of the direct heating strategy, such as Joule heating, for fast and highly productive vacuum swing adsorption in DAC systems.

amines↗

Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product Report

Objective analysis (OA) is a method of mapping unstructured data to a structured grid. In the context of scanning radar data, OA is used to interpolate data in antenna coordinates (range, azimuth, and elevation) onto a regularly spaced Cartesian grid (Trapp and Doswell 2000). The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Mapped Moments to a Cartesian Grid (MMCG) Value-Added Product (VAP) uses the Python ARM Radar Toolkit (Py-ART), a data model-driven interactive architecture for working with weather radar data, to map the data to a Cartesian grid (Helmus and Collis 2016). MMCG, with Py-ART built in, has the ability to take radar data in antenna coordinates and map the gates to a Cartesian grid using inverse distance weight functions such as Cressman (square) and Barnes (exponential), but also can filter the data during the interpolation. MMCG also allows arbitrary formulations for the radius of influence, which are matched to particular radar scanning strategies. This creates a complex parameter space for optimizing the retention of storm structure detail while minimizing artifacts. MMCG takes data processed with ARM’s Corrected Precipitation Radar Moments in Antenna Coordinates (CMAC) VAP and maps it to a Cartesian grid as the output product. A variety of fields that have been mapped to the Cartesian grid are then saved to plots to complement each grid file.

54 ENVIRONMENTAL SCIENCES↗

A Nonlocal Feature-Driven Exemplar-Based Approach for Image Inpainting

Here, we present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework. The recovery of geometric structures is achieved by using general convolution operators as a measure of behavior within an image. These are combined with a nonlocal exemplar-based approach to exploit the self-similarity of an image in the selected feature domains and to ensure the inpainting of textures. We also introduce an anisotropic patch distance metric to allow for better control of the feature selection within an image and present a nonlocal energy functional based on this metric. Finally, we derive an optimization algorithm for the proposed variational model and examine its validity experimentally with various test images.

97 MATHEMATICS AND COMPUTING↗

Multigene engineering in plants: Technologies, applications, and future prospects

The emerging bioeconomy presents a promising solution to both economic and environmental challenges. Within the bioeconomy, plants serve as a renewable, sustainable, and cost-effective source of foods, fuels, chemicals, and materials. However, traditional breeding and single-gene engineering approaches fall short in addressing complex traits (e.g., drought tolerance, disease resistance, yield, nutrient use efficiency) which are controlled by multiple genes. The complexity of plant biology often necessitates the use of multigene engineering (MGE), which involves simultaneous ectopic expression, up/down-regulation, or editing of multiple genes, to enhance plant traits relevant to the bioeconomy. These genes may be associated with distinct traits or function as components of specific metabolic and regulatory pathways. This review summarizes current technologies for MGE within the synthetic biology-driven Design-Build-Test-Learn (DBTL) framework, detailing its four key stages: Design – gene construct development; Build – DNA assembly and plant transformation; Test – the molecular, biochemical, and physiological characterization of engineered plants; and Learn – computational modeling to refine, multiplex and iterate the process. Despite good progress in the applications of MGE in biofortification, metabolic engineering, and stress resilience, challenges remain in construct stability, coordinated gene expression, and regulatory predictability. We identified optimization paths and future directions to accelerate MGE deployment in sustainable agriculture, with possible societal benefits including reduced production costs, increased yield, and improved food and nutritional security.

AI-aided plant engineering↗

Human-automated vehicle interactions

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗

HUMAN-AUTOMATED VEHICLE INTERACTIONS

This dissertation is proposed to answer the question: how can the interactions between human and automated vehicles be used to improve the overall performance of automated driving technology? Multiple different modules in automated vehicles such as the perception, motion plan and motion control modules can potentially be benefitted from human-automated vehicle interactions. For perception module, the self-correction of faulty sensors can be achieved using human demonstration data. For motion plan and motion control modules, the performance of the low-level motion controller can be improved with the help of human demonstration, and the behavior of the motion planner can be improved using human intervention data during automated driving. Moreover, a better model for a human driver could improve the overall efficiency and comfort of vehicles in connected mixed traffic. In this dissertation, the technical research toward these goals has been completed and has resulted in several peer-reviewed publications. Optimization methods and model predictive control are used extensively to improve energy efficiency while maintaining safe and comfort driving. An inverse model predictive control (IMPC) method has been developed and it has been proven to be effective in modeling the motion of human driven vehicles. The proposed method has demonstrated its benefits in both connected automated highway driving and the bilateral adaptation of human driver and automated driving controller in human-in-the loop simulations. The proposed future research seeks to broaden the application of IMPC by considering a more comprehensive cost function design and applying it to more complex driving situations.

Guo, Longxiang↗

Learning Local Volt/VAR Controllers Toward Efficient Network Operation with Stability Guarantees: Preprint

This paper considers the problem of voltage regulation in distribution network. The primary motivation is to keep voltages within pre-assigned operating limits by commanding the reactive power output of distributed energy resources (DERs) deployed in the grid. We develop a framework for developing local Volt/Var control that comprises of two main steps. In the first, exploiting historical data and for each DER, we learn a function representing desirable equilibrium points for the power network. These points approximate solutions of an Optimal Power Flow problem. In the second, we propose a control scheme for steering the network towards these favorable configurations. Theoretical conditions are derived to formally guarantee the stability of the developed control scheme and numerical simulations illustrate the effectiveness of the proposed approach.

data-driven control↗

Application of Simulated Annealing and Related Algorithms to TWTA Design

Simulated Annealing (SA) is a stochastic optimization algorithm used to search for global minima in complex design surfaces where exhaustive searches are not computationally feasible. The algorithm is derived by simulating the annealing process, whereby a solid is heated to a liquid state and then cooled slowly to reach thermodynamic equilibrium at each temperature. The idea is that atoms in the solid continually bond and re-bond at various quantum energy levels, and with sufficient cooling time they will rearrange at the minimum energy state to form a perfect crystal. The distribution of energy levels is given by the Boltzmann distribution: as temperature drops, the probability of the presence of high-energy bonds decreases. In searching for an optimal design, local minima and discontinuities are often present in a design surface. SA presents a distinct advantage over other optimization algorithms in its ability to escape from these local minima. Just as high-energy atomic configurations are visited in the actual annealing process in order to eventually reach the minimum energy state, in SA highly non-optimal configurations are visited in order to find otherwise inaccessible global minima. The SA algorithm produces a Markov chain of points in the design space at each temperature, with a monotonically decreasing temperature. A random point is started upon, and the objective function is evaluated at that point. A stochastic perturbation is then made to the parameters of the point to arrive at a proposed new point in the design space, at which the objection function is evaluated as well. If the change in objective function values (Delta)E is negative, the proposed new point is accepted. If (Delta)E is positive, the proposed new point is accepted according to the Metropolis criterion: rho((Delta)f) = exp((-Delta)E/T), where T is the temperature for the current Markov chain. The process then repeats for the remainder of the Markov chain, after which the temperature is decremented and the process repeats. Eventually (and hopefully), a near-globally optimal solution is attained as T approaches zero. Several exciting variants of SA have recently emerged, including Discrete-State Simulated Annealing (DSSA) and Simulated Tempering (ST). The DSSA algorithm takes the thermodynamic analogy one step further by categorizing objective function evaluations into discrete states. In doing so, many of the case-specific problems associated with fine-tuning the SA algorithm can be avoided; for example, theoretical approximations for the initial and final temperature can be derived independently of the case. In this manner, DSSA provides a scheme that is more robust with respect to widely differing design surfaces. ST differs from SA in that the temperature T becomes an additional random variable in the optimization. The system is also kept in equilibrium as the temperature changes, as opposed to the system being driven out of equilibrium as temperature changes in SA. ST is designed to overcome obstacles in design surfaces where numerous local minima are separated by high barriers. These algorithms are incorporated into the optimal design of the traveling-wave tube amplifier (TWTA). The area under scrutiny is the collector, in which it would be ideal to use negative potential to decelerate the spent electron beam to zero kinetic energy just as it reaches the collector surface. In reality this is not plausible due to a number of physical limitations, including repulsion and differing levels of kinetic energy among individual electrons. Instead, the collector is designed with multiple stages depressed below ground potential. The design of this multiple-stage collector is the optimization problem of interest. One remaining problem in SA and DSSA is the difficulty in determining when equilibrium has been reached so that the current Markov chain can be terminated. It has been suggested in recent literature that simulating the thermodynamic properties opecific heat, entropy, and internal energy from the Boltzmann distribution can provide good indicators of having reached equilibrium at a certain temperature. These properties are tested for their efficacy and implemented in SA and DSSA code with respect to TWTA collector optimization.

Radke, Eric M.↗

Development of a PEMFC Power System with Integrated Balance of Plant

Autonomous Underwater Vehicles (AUV s) have received increasing attention in recent years as military and commercial users look for means to maintain a mobile and persistent presence in the undersea world. Compact, neutrally buoyant power systems are needed for both small and large vehicles. Batteries are usually employed in these applications, but the energy density and therefore the mission duration are limited with current battery technology. At a certain energy or mission duration requirement, other means to get long duration power become feasible. For example, above 10 kW-hrs liquid oxygen and hydrogen have better specific energy than batteries and are preferable for energy storage as long as a compact system of about 100 W/liter is achievable to convert the chemical energy in these reactants into power. Other reactant forms are possible, such as high pressure gas, chemical hydrides or oxygen carriers, but it is essential that the power system be small and light weight. Recent fuel cell work, primarily focused on NASA applications, has developed power systems that can meet this target power density. Passive flow-through systems, using ejector driven reactant (EDR) flow, integrated into a compact balance of plant have been developed. These systems are thermally and functionally integrated in much the same way as are automotive, air breathing fuel cell systems. These systems fit into the small volumes required for AUV and future NASA applications. Designs have been developed for both a 21" diameter and a larger diameter (LD) AUV. These fuel cell systems occupy a very small portion of the overall energy system, allowing most of the system volume to be used for the reactants. The fuel cell systems have been optimized to use reactants efficiently with high stack efficiency and low parasitic losses. The resulting compact, highly efficient fuel cell system provides exceptional reactant utilization and energy density. Key design variables and supporting test data are presented. Future development activities are described.

Wynne, B.↗

Network Slicing for Federated Learning in Operational Technology Environment

Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) environments are essential to modern infrastructure, facing challenges in ensuring low-latency, high-throughput communication while mitigating cyber threats. This paper presents a framework integrating Federated Learning (FL) and network slicing with Quality of Service (QoS) to enable real-time monitoring without disrupting OT operations. Leveraging digital twin technology and Network Function Virtualization (NFV), the architecture supports predictive analytics and Industry 4.0 requirements. FL facilitates decentralized model training, preserving data privacy and scalability, though it introduces potential throughput constraints. Network slicing addresses this by creating dedicated virtualized segments optimized for performance and security. Advanced fault tolerance at the container and instance levels enhances system reliability. The proposed architecture ensures high throughput, low latency, and secure orchestration for real-time anomaly detection in OT networks. Performance evaluations validate its efficiency in throughput, deployment, and learning accuracy, providing a robust foundation for future ICS automation and data-driven decision-making.

Delgado, Brian G. Rodiles [University of Texas at ↗

A Requirements-Driven Optimization Method for Acoustic Liners Using Analytic Derivatives

More than ever, there is flexibility and freedom in acoustic liner design. Subject to practical considerations, liner design variables may be manipulated to achieve a target attenuation spectrum. But characteristics of the ideal attenuation spectrum can be difficult to know. Many multidisciplinary system effects govern how engine noise sources contribute to community noise. Given a hardwall fan noise source to be suppressed, and using an analytical certification noise model to compute a community noise measure of merit, the optimal attenuation spectrum can be derived using multidisciplinary systems analysis methods. In a previous paper on this subject, a method deriving the ideal target attenuation spectrum that minimizes noise perceived by observers on the ground was described. A simple code-wrapping approach was used to evaluate a community noise objective function for an external optimizer. Gradients were evaluated using a finite difference formula. The subject of this paper is an application of analytic derivatives that supply precise gradients to an optimization process. Analytic derivatives improve the efficiency and accuracy of gradient-based optimization methods and allow consideration of more design variables. In addition, the benefit of variable impedance liners is explored using a multi-objective optimization.

Aircraft Noise↗

Operational resilience of additively manufactured parts to stealthy cyberphysical attacks using geometric and process digital twins

Cyberphysical attacks on the digital backbone of Additive Manufacturing (AM) can compromise the printed part’s functionality. They can alter features in the digital geometry to introduce geometric defects (e.g., missing fillets) or alter process parameters to create local defects (e.g., voids). Addressing the downtime, waste, and quality deterioration associated with existing solutions requires operational resilience, i.e., rapid elimination or disruption of defect formation (to retain part function) without production stoppage or part disposal (to retain yield). This need is unmet due to the inherently unpredictable nature of attack-induced alterations, lack of access to the original geometric model for identification of altered geometric features, and in-process imposition of unknown process dynamics via attack-driven alteration of real-time-uncontrolled (or exogenous) parameters. This work establishes the above-mentioned operational resilience for the first time by creating two Digital Twins (DT). The Geometric DT (Geo-DT) is based on a unique physical-field-driven soft sensor and topology optimization method. The Process Digital Twin (Pro-DT) combines local defect quantification with a novel Reinforcement Learning formulation and training method. The importance of these methodological advances and the scalability of our approach are examined on a real AM testbed. It is shown that Geo-DT can correct geometric defects without access to the original digital geometry or explicit knowledge of attack-altered geometric features. Further, Pro-DT can accelerate real-time disruption of local defects despite attack-driven imposition of unknown process dynamics. We discuss how our framework goes beyond the contemporary focus on pre-attack security and in-attack detection towards resilience for AM and beyond.

Additive Manufacturing↗

Factors affecting the noise from small propeller driven aircraft

The factors affecting noise from small propeller driven airplanes are reviewed to quantify their affects where possible, and to indicate the potential for noise reduction. The main sources of external noise are noted to be the propellers and engines; the airframe being of less importance for both aural detection and community annoyance. Propeller noise is a strong function of tip speed and is affected adversely by nonuniform inflows. Reciprocating engine exhausts are noisier than those of comparably rated turboshaft engines, but their noise can be reduced by the use of flight certified exhaust mufflers. Presently, there are no generally accepted engineering methods for development of optimized propellers and exhaust muffler designs from weight and performance penalty standpoints. Flight demonstration results, however, suggest that required noise reductions for future certification should be possible with potentially small penalties.

Maglieri, D. J.↗

Structural asymmetry governs the assembly and GTPase activity of McrBC restriction complexes

Abstract McrBC complexes are motor-driven nucleases functioning in bacterial self-defense by cleaving foreign DNA. The GTP-specific AAA + protein McrB powers translocation along DNA and its hydrolysis activity is stimulated by its partner nuclease McrC. Here, we report cryo-EM structures of Thermococcus gammatolerans McrB and McrBC, and E. coli McrBC. The McrB hexamers, containing the necessary catalytic machinery for basal GTP hydrolysis, are intrinsically asymmetric. This asymmetry directs McrC binding so that it engages a single active site, where it then uses an arginine/lysine-mediated hydrogen-bonding network to reposition the asparagine in the McrB signature motif for optimal catalytic function. While the two McrBC complexes use different DNA-binding domains, these contribute to the same general GTP-recognition mechanism employed by all G proteins. Asymmetry also induces distinct inter-subunit interactions around the ring, suggesting a coordinated and directional GTP-hydrolysis cycle. Our data provide insights into the conserved molecular mechanisms governing McrB family AAA + motors.

59 BASIC BIOLOGICAL SCIENCES↗

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs a multilayer feedforward artificial neural network with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. Here, we detail the hyperparameters optimization of these models using the grid search algorithm.

42 ENGINEERING↗