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At least 1,099 records · Page 61

Comparison of ablators for the polar direct drive exploding pusher platform

We examine the performance of pure boron, boron carbide, high density carbon, and boron nitride ablators in the polar direct drive exploding pusher (PDXP) platform. The platform uses the polar direct drive conguration at the National Ignition Facility to drive high ion temperatures in a room temperature capsule and has potential applications for plasma physics studies and as a neutron source. The higher tensile strength of these materials compared to plastic enables a thinner ablator to support higher gas pressures, which could help optimize its performance for plasma physics experiments, while ablators containing boron enable the possiblity of collecting addtional data to constrain models of the platform. Applying recently developed and experimentally validated equation of state models for the boron materials, we examine the performance of these materials as ablators in 2D simulations, with particular focus on changes to the ablator and gas areal density, as well as the predicted symmetry of the inherently 2D implosion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Bubble Transport through a Porous Lattice with an Applied Inlet Flow

Within gas-evolving electrochemical systems, bubbles negatively impact performance by covering electrode active sites for reactions, blocking electric field lines, and obstructing liquid electrolyte flow causing pressure buildup. Recent additive manufacturing advances have enabled tuned porous electrode microstructures to be created, but producing systems that maximize electrochemical throughput and minimize bubble impact remains challenging. Thus, improved physical understanding of and modeling capabilities for bubble behavior are critical to improve electrolyzer design. To address this need, this study examines rising stage bubbles within a lattice with an applied liquid flow—an underexplored regime that strongly influences an electrochemical bubble’s fate. Notably, theoretical predictions and resolved bubble simulations are complemented by experiments from a 3D-printed visualization cell that matches the simulation geometry. The minimum threshold flow rate to achieve bubble breakthrough is found to be larger for higher porosities and for smaller bubbles. Different-sized bubbles decrease expected electrochemical performance in different ways; smaller bubbles tend to stay stuck but cover less solid surface, while larger bubbles more readily break through but cover more surface while in the lattice. The bubble trajectory, deformation, and contact area provide insight into these different behaviors. These findings provide design guidelines toward creating more effective electrolyzers.

Guo, Jack [Lawrence Livermore National Laboratory ↗

Modification of favorable average curvature effect by changing parallel sound wave behavior in tokamak plasmas

Abstract The favorable average curvature effect, also known as the GGJ effect (Glasser et al 1975 Phys. Fluids 18 875), is intrinsically associated with parallel sound wave propagation in a tokamak plasma. This work investigates how the GGJ effect is modified by changing the parallel sound wave behavior. Two physics models beyond the standard single fluid theory, i.e. an anisotropic thermal transport model and a parallel sound wave damping model, are employed to change parallel sound waves in a toroidal plasma, and the consequence on the GGJ effect is demonstrated for two important classes of problems, i.e. the resistive plasma response to the applied resonant magnetic perturbation and the stability of the tearing mode. Toroidal modeling reveals that the GGJ effect is significantly altered by both of the aforementioned physics effects. Compared to the thermal transport physics, which completely removes the GGJ effect, the sound wave damping effect only offers partial mitigation. The differences between these two models are further illustrated in terms of the radial structure of the shielding current and the eigenfunction of the tearing instability. In particular, a fundamental reason for complete suppression of the GGJ effect by the thermal transport is identified as an extra toroidal coupling of the poloidal harmonics.

Physics↗

Evaluating diffusion and the thermodynamic factor for binary ionic mixtures

Molecular dynamics (MD) simulations are a powerful tool for the calculation of transport properties in mixtures. Not only are MD simulations capable of treating multicomponent systems, they are also applicable over a wide range of temperatures and densities. In plasma physics, this is particularly important for applications such as inertial confinement fusion. While many studies have focused on the effect of plasma coupling on transport properties, here we focus on the effects of mixing. We compute the thermodynamic factor, a measure of ideal/non-ideal mixing, for three binary ionic mixtures. Here, we consider mixtures of hydrogen and carbon, hydrogen and argon, and argon and carbon, each at 500 randomly generated state points in the warm dense matter and plasma regimes. The calculated thermodynamic factors indicate different mixing behavior across phase space, which can significantly affect the corresponding mutual diffusion coefficients. As MD simulations are still computationally expensive, we apply modern data science tools to predict the thermodynamic factor over a large phase space. Further, we propose a more accurate approximation to the mutual diffusion coefficient than the commonly applied Darken relation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Semi-empirical quantum optics for mid-infrared molecular nanophotonics

Nanoscale infrared (IR) resonators with sub-diffraction limited mode volumes and open geometries have emerged as new platforms for implementing cavity quantum electrodynamics at room temperature. The use of IR nanoantennas and tip nanoprobes to study strong light–matter coupling of molecular vibrations with the vacuum field can be exploited for IR quantum control with nanometer spatial and femtosecond temporal resolution. In order to advance the development of molecule-based quantum nanophotonics in the mid-IR, we propose a generally applicable semi-empirical methodology based on quantum optics to describe light–matter interaction in systems driven by mid-IR femtosecond laser pulses. The theory is shown to reproduce recent experiments on the acceleration of the vibrational relaxation rate in infrared nanostructures. It also provides physical insights on the implementation of coherent phase rotations of the near-field using broadband nanotips. We then apply the quantum framework to develop general tip-design rules for the experimental manipulation of vibrational strong coupling and Fano interference effects in open infrared resonators. We finally propose the possibility of transferring the natural anharmonicity of molecular vibrational levels to the resonator near-field in the weak coupling regime to implement intensity-dependent phase shifts of the coupled system response with strong pulses and develop a vibrational chirping model to understand the effect. The semi-empirical quantum theory is equivalent to first-principles techniques based on Maxwell’s equations, but its lower computational cost suggests its use as a rapid design tool for the development of strongly coupled infrared nanophotonic hardware for applications ranging from quantum control of materials to quantum information processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Comparison of ablators for the polar direct drive exploding pusher platform

In this work, we examine the performance of pure boron, boron carbide, high density carbon, and boron nitride ablators in the polar direct drive exploding pusher (PDXP) platform. The platform uses the polar direct drive configuration at the National Ignition Facility to drive high ion temperatures in a room temperature capsule and has potential applications for plasma physics studies and as a neutron source. The higher tensile strength of these materials compared to plastic enables a thinner ablator to support higher gas pressures, which could help optimize its performance for plasma physics experiments, while ablators containing boron enable the possibility of collecting additional data to constrain models of the platform. Applying recently developed and experimentally validated equation of state models for the boron materials, we examine the performance of these materials as ablators in 2D simulations, with particular focus on changes to the ablator and gas areal density, as well as the predicted symmetry of the inherently 2D implosion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Phonon screening and dissociation of excitons at finite temperatures from first principles

The properties of excitons, or correlated electron–hole pairs, are of paramount importance to optoelectronic applications of materials. A central component of exciton physics is the electron–hole interaction, which is commonly treated as screened solely by electrons within a material. However, nuclear motion can screen this Coulomb interaction as well, with several recent studies developing model approaches for approximating the phonon screening of excitonic properties. While these model approaches tend to improve agreement with experiment, they rely on several approximations that restrict their applicability to a wide range of materials, and thus far they have neglected the effect of finite temperatures. Here, we develop a fully first-principles, parameter-free approach to compute the temperature-dependent effects of phonon screening within the ab initio GW -Bethe–Salpeter equation framework. We recover previously proposed models of phonon screening as well-defined limits of our general framework, and discuss their validity by comparing them against our first-principles results. We develop an efficient computational workflow and apply it to a diverse set of semiconductors, specifically AlN, CdS, GaN, MgO, and SrTiO 3 . We demonstrate under different physical scenarios how excitons may be screened by multiple polar optical or acoustic phonons, how their binding energies can exhibit strong temperature dependence, and the ultrafast timescales on which they dissociate into free electron–hole pairs.

Science & Technology - Other Topics↗

Anisotropic magneto-resistivity and magnetocaloric effect in DyAl 2

Here, in this study, we experimentally and theoretically investigate the correlation between the anisotropic magnetic resistivity and magnetocaloric effect in DyAl 2 single crystals. DyAl 2 crystallizes in the C15 Laves phase structure with cubic symmetry. While some earlier studies revealed that the isothermal entropy change in dialuminides follows a similar trend as the electrical resistivity change as a function of temperature and magnetic field, the correlation between anisotropic behavior of these two physical parameters is yet to be explored to the best of our knowledge. To address this gap, we measured electrical resistivity of DyAl 2 single crystals along two different directions in zero and applied magnetic fields and compared the experimental results with our theoretical model. For the theoretical analysis, we employed a model Hamiltonian in the mean-field approximation, considering the exchange interaction, Zeeman effect, and crystalline electric field associated with the cubic symmetry. Our findings reveal a significant dependence of DyAl 2 resistivity on the direction of the applied magnetic field, in agreement with our theoretical results. These outcomes emphasize the interplay between magnetocaloric effect and magneto-resistivity, underscoring the potential of the magnetocaloric effect as a valuable tool for gaining deeper insights into basic physical properties including electron transport behavior.

36 MATERIALS SCIENCE↗

The advanced tokamak path to a compact net electric fusion pilot plant

Abstract Physics-based simulations project a compact net electric fusion pilot plant with a nuclear testing mission is possible at modest scale based on the advanced tokamak concept, and identify key parameters for its optimization. These utilize a new integrated 1.5D core-edge approach for whole device modeling to predict performance by self-consistently applying transport, pedestal and current drive models to converge fully non-inductive stationary solutions, predicting profiles and energy confinement for a given density. This physics-based approach leads to new insights and understanding of reactor optimization. In particular, the levering role of high plasma density is identified, which raises fusion performance and self-driven ‘bootstrap currents’, to reduce current drive demands and enable high pressure with net electricity at a compact scale. Solutions at 6–7 T, ∼4 m radius and 200 MW net electricity are identified with margins and trade-offs possible between parameters. Current drive comes from neutral beam and ultra-high harmonic (helicon) fast wave, though other advanced approaches are not ruled out. The resulting low recirculating power in a double null configuration leads to a divertor heat flux challenge that is comparable to ITER, though reactor solutions may require more dissipation. Strong H-mode access (x2 margin over L–H transition scalings) and ITER-like heat fluxes are maintained with ∼20%–60% core radiation, though effects on confinement need further analysis. Neutron wall loadings appear tolerable. The approach would benefit from high temperature superconductors, as higher fields would increase performance margins while potential for demountability may facilitate nuclear testing. However, solutions are possible with conventional superconductors. An advanced load sharing and reactive bucking approach in the device centerpost region provides improved mechanical stress handling. The prospect of an affordable test device which could close the loop on net-electric production and conduct essential nuclear materials and breeding research is compelling, motivating research to validate the techniques and models employed here.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Physics informed neural network can retrieve rate and state friction parameters from acoustic monitoring of laboratory stick-slip experiments

Various machine learning (ML) and deep learning (DL) techniques have been recently applied to the forecasting of laboratory earthquakes from friction experiments. The magnitude and timing of shear failures in stick-slip cycles are predicted using features extracted from the recorded ultrasonic or acoustic emission (AE) signals. In addition, the Rate and State Friction (RSF) constitutive laws are extensively used to model the frictional behavior of faults. In this work, we use data from shear experiments coupled with passive acoustic (variance, kurtosis, and AE rate) interleaved with active source ultrasonic monitoring (transmitted wave amplitude) to develop physics-informed neural network (PINN) models incorporating the RSF law and AE rate generation equation with wave amplitude serving as a proxy for friction state variable. This PINN framework allows learning RSF parameters from stick-slip experiments rather than measuring them through a series of velocity step experiments. We observe that when the stick-slip cycles are irregular, the PINN models outperform the data-driven DL models. Transfer learning (TL) PINN models are also developed by pre-training on data collected at one normal stress level followed by forecasting shear failures and retrieving RSF parameters at other stress levels (i.e., with different recurrence intervals) after retraining on a limited amount of new data. Our findings suggest that TL models perform better compared to standalone models. Both standalone and TL PINN-estimated RSF parameters and their ground truth values show excellent agreements thus demonstrating that RSF parameters can be retrieved from laboratory stick-slip experiments using the corresponding acoustic data and that the transmitted wave amplitude provides a good representation of the evolving frictional state during stick-slips.

58 GEOSCIENCES↗

Electrodermal activity as a proxy for sweat rate monitoring during physical and mental activities

Electrodermal activity has long been used for mental activity monitoring by measuring skin conductance at specific locations, such as fingertips, with high sweat gland density. However, electrodermal activity has not been considered useful for physical activity monitoring, where large sweat volumes are generated, resulting in the accumulation of sweat at the skin–electrode interface and, thus, preventing further dynamic response to sweating events. Here we show that electrodermal activity can be used as a proxy for sweat loss measurement under both low and high physical activity levels. We use wearable sweat sensors that consist of water-permeable electrodes and microfluidic-based sweat analysers, and show that skin conductance is proportional to the instantaneous sweat loss. We demonstrate that sweat loss during exercise can be estimated by integrating skin conductance over time, which can be applied to assess the body hydration status of exercisers. From multisite measurements of skin conductance, we show that the wrist, forearm and upper arm are reflective of physical activity levels, whereas the finger is indicative of mental activity. Simultaneous measurement of two different sites selectively decouples mental and physical activities.

Biomedical engineering↗

Enhanced CO 2 Methanation Activity of Sm 0.25 Ce 0.75 O 2-δ –Ni by Modulating the Chelating Agents-to-Metal Cation Ratio and Tuning Metal–Support Interactions

Highly active and selective CO 2 methanation catalysts are critical to CO 2 upgrading, synthetic natural gas production, and CO 2 emission reduction. Wet impregnation is widely used to synthesize oxide-supported metallic nanoparticles as the catalyst for CO 2 methanation. However, as the reagents cannot be homogeneously mixed at an atomic level, it is challenging to modulate the microstructure, crystal structure, chemical composition, and electronic structure of catalysts via wet impregnation. In this work, a scalable and straightforward catalyst fabrication approach has been designed and validated to produce Sm 0.25 Ce 0.75 O 2-δ -supported Ni (SDC–Ni) as the CO 2 methanation catalyst. By varying the chelating agents-to-total metal cations ratio (C/I ratio) during the catalyst synthesis, we can readily and simultaneously modulate the microstructure, metallic surface area, crystal structure, chemical composition, and electronic structure of SDC–Ni, consequently fine-tuning the oxide–support interactions and CO 2 methanation activity. The optimal C/I ratio (0.1) leads to an SDC–Ni catalyst that facilitates C–O bond cleavage and significantly improves CO 2 conversion at 250 °C. A CO 2 -to-CH 4 yield of >73% has been achieved at 250 °C. Furthermore, a stable operation of >1500 hours has been demonstrated, and no degradation is observed. Extensive characterizations were performed to fundamentally understand how to tune and enhance CO 2 methanation activity of SDC–Ni by modulating the C/I ratio. The correlation of physical, chemical, and catalytic properties of SDC–Ni with the C/I ratio is established and thoroughly elaborated in this work. This study could be applied to tune the oxide–support interactions of various catalysts for enhancing the catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Accounting for connectivity alters the apparent roles of spatial and environmental processes on metacommunity assembly

Understanding the relative contributions of spatial and environmental processes on community assembly is a central question in ecology. Despite this long-standing interest, our understanding of how landscape structure may drive spatial processes of community assembly remains poorly understood in part because of the challenge of tracking community assembly across landscapes and quantifying key aspects of landscapes that may impact assembly processes. Here, we examined the roles of spatial and environmental processes on structuring assemblies of ants in 72 cleared patches embedded within a forested landscape.

60 APPLIED LIFE SCIENCES↗

Bayesian optimization for chemical products and functional materials

The design of chemical-based products and functional materials is vital to modern technologies, yet remains expensive and slow. Artificial intelligence and machine learning offer new approaches to leverage data to overcome these challenges. Herein this review focuses on recent applications of Bayesian optimization (BO) to chemical products and materials including molecular design, drug discovery, molecular modeling, electrolyte design, and additive manufacturing. Numerous examples show how BO often requires an order of magnitude fewer experiments than Edisonian search. The essential equations for BO are introduced in a self-contained primer specifically written for chemical engineers and others new to the area. Finally, the review discusses four current research directions for BO and their relevance to product and materials design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Extracellular vesicles in β cell biology: Role of lipids in vesicle biogenesis, cargo, and intercellular signaling

Background: Type 1 diabetes (T1D) is a complex autoimmune disorder whose pathogenesis involves an intricate interplay between ß cells of the pancreatic islet, other islet cells, and cells of the immune system. Direct intercellular communication within the islet occurs via cell surface proteins and indirect intercellular communication has traditionally been as occurring via secreted proteins (e.g., endocrine hormones and cytokines). However, recent literature suggests that extracellular vesicles (EVs) secreted by ß cells constitute an additional and biologically important mechanism for transmitting signals to adjacent ß cells or immune cells. Scope of Review: This review summarizes the general mechanisms of EV formation, with a particular focus on how lipids and lipid signaling pathways influence their formation and cargo. We review the implications of EV release from ß cells for T1D pathogenesis, how EVs and their cargo might be leveraged as biomarkers of the early events leading to T1D, and how EVs might be engineered as a therapeutic candidate to counter T1D outcomes. Major Conclusions: Islet ß cells have been viewed as initiators and propagators of the cellular circuit giving rise to autoimmunity in T1D. In this context, emerging literature suggests that EVs may represent a conduit for communication that holds more comprehensive messaging about the ß cells from which they arise. As the field of EV biology advances, it opens the possibility that intervening with EV formation and cargo loading could be a novel disease-modifying approach in T1D.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A review of quantum imaging methods and enabling technologies

The field of quantum imaging has recently seen an explosion of new methods and techniques. At the heart of most quantum imaging approaches are three enabling technologies: the source of the quantum light, the detectors that turn sensing into imaging, and the method by which the quantum nature of light is leveraged to improve the desired measurement. This review addresses the dominant and emerging technologies in each of these components as well as details concerning the implementations of various quantum imaging methods.

36 MATERIALS SCIENCE↗

Understanding parton evolution in matter from renormalization group analysis

We perform a renormalization group (RG) analysis of collinear hadron production in deep inelastic scattering on nuclei. We consider the limit where the parent parton energy E is large, while the medium opacity remains small. We identify the fixed order and leading enhanced medium contributions to the semi-inclusive cross sections and derive RG equations that resum multiple emissions near the endpoints of the splitting functions at first order in opacity. These evolution equations treat the same type of radiation enhancement in matter as the modified Dokshitzer-Gribov-Lipatov-Altarelli-Parisi approach, but differ in the way one regulates the collinear divergences. They provide a unique analytic insight into the problem of resummation and a faster and more efficient path to phenomenology. The new RG evolution framework is applied to study fragmentation in eA reactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Binding Interactions between Receptor-Binding Domain of Spike Protein and Human Angiotensin Converting Enzyme-2 in Omicron Variant

The emergence of new SARS-CoV-2 Omicron variant of concern (OV) has exacerbated the COVID-19 pandemic because of a large number of mutations in the spike protein, particularly in the receptor-binding domain (RBD), resulting in highly contagious and/or vaccine-resistant strains. Herein, we present a systematic analysis based on detailed molecular dynamics (MD) simulations in order to understand how the OV RBD mutations affect the ACE2 binding. We show that the OV RBD binds to ACE2 more efficiently and tightly predominantly because of strong electrostatic interactions, thereby promoting increased infectivity and transmissibility compared to other strains. Some of the OV RBD mutations are predicted to affect the antibody neutralization either through their role in the S-protein conformational changes, such as S371L, S373P, and S375F, or through changing its surface charge distribution, such as G339D, N440K, T478K, and E484A. Other mutations, such as K417N, G446S, and Y505H, decrease the ACE2 binding, whereas S447N, Q493R, G496S, Q498R, and N501Y tend to increase it.

60 APPLIED LIFE SCIENCES↗