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Highly Tunable Magnetic Phases in Transition-Metal Dichalcogenide Fe 1 / 3 + δ NbS 2
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Valley-Coherent Quantum Anomalous Hall State in AB-Stacked MoTe 2 / W S e 2 Bilayers
Moiré materials provide fertile ground for the correlated and topological quantum phenomena. Among them, the quantum anomalous Hall (QAH) effect, in which the Hall resistance is quantized even under zero magnetic field, is a direct manifestation of the intrinsic topological properties of a material and an appealing attribute for low-power electronics applications. The QAH effect has been observed in both graphene and transition metal dichalcogenide (TMD) moiré materials. It is thought to arise from the interaction-driven valley polarization of the narrow moiré bands. Here, we show that the newly discovered QAH state in AB-stacked MoTe 2 / W S e 2 moiré bilayers is not valley polarized but valley coherent. The layer- and helicity-resolved optical spectroscopy measurement reveals that the QAH ground state possesses spontaneous spin (valley) polarization aligned (antialigned) in two TMD layers. In addition, saturation of the out-of-plane spin polarization in both layers occurs only under high magnetic fields, supporting a canted spin texture. Our results call for a new mechanism for the QAH effect and highlight the potential of TMD moiré materials with strong electronic correlations and spin-orbit interactions for exotic topological states. Published by the American Physical Society 2024
Nature of Excitons and Their Ligand-Mediated Delocalization in Nickel Dihalide Charge-Transfer Insulators
The fundamental optical excitations of correlated transition-metal compounds are typically identified with multielectronic transitions localized at the transition-metal site, such as d d transitions. In this vein, intense interest has surrounded the appearance of sharp, below-band-gap optical transitions, i.e., excitons, within the magnetic phase of correlated Ni 2 + van der Waals magnets. The interplay of magnetic and charge-transfer insulating ground states in Ni 2 + systems raises intriguing questions on the roles of long-range magnetic order and of metal-ligand charge transfer in the exciton nature, which inspired microscopic descriptions beyond typical d d excitations. Here we study the impact of charge transfer and magnetic order on the excitation spectrum of the nickel dihalides ( Ni X 2 , X = Cl , Br, and I) using Ni- L 3 edge resonant inelastic x-ray scattering (RIXS). In all compounds, we detect sharp excitations, analogous to the recently reported excitons, and assign them to spin-singlet multiplets of octahedrally coordinated Ni 2 + stabilized by intra-atomic Hund’s exchange. Additionally, we demonstrate that these excitons are dispersive using momentum-resolved RIXS. Our data evidence a ligand-mediated multiplet dispersion, which is tuned by the charge-transfer gap and independent of the presence of long-range magnetic order. This reveals the mechanisms governing nonlocal interactions of on-site d d excitations with the surrounding crystal or magnetic structure, in analogy to ground-state superexchange. These measurements thus establish the roles of magnetic order, self-doped ligand holes, and intersite-coupling mechanisms for the properties of d d excitations in charge-transfer insulators. Published by the American Physical Society 2024
Quantum confining excitons with an electrostatic moiré superlattice
Quantum confining excitons has been a persistent challenge in the pursuit of strong exciton interactions and quantum light generation. Unlike electrons, which can be readily controlled via electric fields, imposing strong nanoscale potentials on excitons to enable quantum confinement has proven challenging. In this study, we utilize piezoelectric force microscopy to image the domain structures of twisted hexagonal boron nitride (h – BN), revealing evidence of strong in-plane electric fields at the domain boundaries. By placing a monolayer MoSe 2 only one to two nanometers away from the twisted h – BN interface, we observe energy splitting of neutral excitons and Fermi polarons by several millielectronvolts at the moiré domain boundaries. By directly correlating local structural and optical properties, we attribute such observations to excitons confined in a nanoscale one-dimensional electrostatic potential created by the strong in-plane electric fields at the moiré domain boundaries. Intriguingly, this 1D quantum confinement results in pronounced polarization anisotropy in the excitons’ reflection and emission, persistent to temperatures as high as ~80 Kelvins. Furthermore, these findings open new avenues for exploring and controlling strongly interacting excitons for classical and quantum optoelectronics.
Computational analysis of interface-driven spin-orbit coupling in molecular adsorbates on transition metal dichalcogenides
Spin-orbit coupling (SOC) lifts molecular orbital degeneracy, enabling bilevel electronic platforms suitable for next-generation digital devices. However, common light-atom molecular feedstocks exhibit weak SOC due to the absence of heavy elements. To enhance SOC without synthesizing new materials, we leverage interfacial interactions between molecules and transition-element-based solid-state materials. This computational study investigates SOC splitting in metal-phthalocyanine adsorbed on transition metal dichalcogenides (TMDs) using density functional theory (DFT). The enhanced SOC splitting is attributed to strong orbital hybridization at the molecule-substrate interface. Specifically, Zn-phthalocyanine (ZnPC) on monolayer MoS 2 achieves a notable SOC splitting of ∼8 meV. Furthermore, when ZnPC forms self-assembled chains on MoS 2 , the splitting increases to ∼20 meV, driven by the formation of hybrid bands modulated by molecular periodicity. Furthermore, these findings highlight the role of interfacial and intermolecular interactions in inducing and enhancing SOC in surface-adsorbed molecules, providing a new strategy for molecular spintronic materials without complex synthetic efforts.
Theoretical and experimental analysis of the modulated phase grating X-ray interferometer
X-ray grating interferometry allows for the simultaneous acquisition of attenuation, differential-phase contrast, and dark-field images, resulting from X-ray attenuation, refraction, and small-angle scattering, respectively. The modulated phase grating (MPG) interferometer is a recently developed grating interferometry system capable of generating a directly resolvable interference pattern using a relatively large period grating envelope function that is sampled at a pitch that is small enough that X-ray spatial coherence can be achieved by using a microfocus X-ray source or G0 grating. We present the theory of the MPG interferometry system for a 2-dimensional staggered grating, derived using Fourier optics, and we compare the theoretical predictions with experiments we have performed with a microfocus X-ray system at Pennington Biomedical Research Center, LSU. The theoretical and experimental fringe visibility is evaluated as a function of grating-to-detector distance. Additionally, quantitative experiments are performed with porous carbon and alumina compounds, and the mean normalized dark-field signal is compared with independent porosimetry measurements. Qualitative analysis of attenuation and dark-field images of a dried anchovy are shown.
Thermo-Hydro-Chemical Simulation of Mid-Ocean Ridge Hydrothermal Systems: Static 2D Models and Effects of Paleo-Seawater Chemistry
Decades of research have resulted in characterization of the ocean floor manifestations of mid-ocean ridge (MOR) hydrothermal systems, yet numerical models accounting for the connections between heat transfer, hydrology and geochemistry have been slow to develop. The Thermo-hydro-chemical code ToughReact can be used to describe the coupled effects of fluid flow, heat transfer, and fluid-rock chemical interactions that occur in MOR systems. We describe the results of 2-dimensional simulations of steady state flow in fractured diabase with mineral-fluid chemical reactions. Basal heating and specified permeability yield maximum temperature of 400°C. Total fluid flux and high fracture flow velocities are in accord with observations. Fluid chemistry, mineralogical changes and 87 Sr/ 86 Sr ratios can be compared to observations to assess and calibrate models. Simulated high temperature fracture fluids have Mg and SO 4 near zero, elevated Ca and 87 Sr/ 86 Sr of about 0.7040. Total alteration is 10%–50% for simple models of spreading. Anhydrite forms mainly near the base of the upwelling zone and results in substantial local fracture porosity reduction. A calibrated model is used to predict how Sr isotopes and other features of altered oceanic crust would be different in the Cretaceous (95 Ma) early Proterozoic (1,800 Ma) and Archean (3,800 Ma), when seawater may have had high Ca and Sr concentrations, lower pH, higher temperature, and lower Na, Mg, and SO 4 . The simulations are offered as a start on what ultimately may require a longer-term community effort to better understand the role of MOR thermo-hydro-chemical systems in Earth evolution.
Object Detection and Recognition with PointPillars in LiDAR Point Clouds – Comparisions
In the field of autonomous systems, neural networks have been leveraged for object detection and recognition in 2-dimensional images captured by cameras. Other types of sensors are available for sensing surroundings, including LiDAR sensors, and corresponding networks have been developed to perform detection and recognition in the point clouds generated by these sensors. The approaches are similar, both perform convolutions, but have distinct characteristics and challenges. In designing and configuring autonomous systems, a variety of LiDAR sensors are available, along with configurable deep neural networks to leverage their data. This work presents a review of the PointPillars network, an evolution of the seminal PointNet, comparing accuracy and training time relative to different LiDAR sensors, network and training parameters, CPU and GPU hardware, and the criticality of the use of reflective intensity as a feature. The value of using reflectivity as a predictive feature is explored and quantified to determine if it makes a significant difference in accuracy of the PointPillars network. Two separate LiDAR sensors are utilized, a 16-plane and a 32-plane, and corresponding accuracies and training times with the PointPillars network are evaluated.
In-situ thermomechanical measurements during burst testing of Zr claddings using digital image correlation and infrared thermography techniques
Burst testing has long been a relied-on method for assessing fuel cladding materials in light-water reactoraccident scenarios. However, burst testing historically provided minimal in-situ observations beyond a few thermocouple readings, pressure from transducers, and post-test deformation measurements. Acquisition of full cladding thermomechanical data throughout a burst test would be invaluable to fuel system qualification, material model development and fuel performance code validation, such as the BISON fuel performance code. Recently, methodologies for simultaneously applying 2-dimensional digital image correlation and infrared thermography techniques to quantify the relationship between temperature, internal cladding pressure, and deformation during burst testing were developed. This work details results from the application of these techniques to burst testing of Zircaloy-4 cladding segments subjected to loss-of-coolant accident transients in an air environment. Two types of tests were performed with: (a) cladding segments connected to a fixed pressure reservoir as well as (b) closed systems where internal pressure increased during transients. In-situ thermomechanical measurements including strain and thermal gradient data are presented, demonstrating the effectiveness of applying combined surface measurement techniques to burst testing
Extremum seeking control of distributed energy resources with decaying dither and equilibrium-based switching
Embodiments relate to an operating system coupled to and controlling at least one Distributed Energy Resource (DER) in a smart grid and includes an electricity distribution network and a 2-dimensional Extremum Seeking (2D-ES) controller coupled to network. The electricity distribution network has first and second inputs and one output and maps all active power and reactive power inputs to the smart grid providing measurements of an objective function. The 2D-ES controller controls the DER and includes an active power loop and a reactive power loop. The active power loop is in communication with the first input and the output, governs active power contribution, and receives measurements of the objective function which contain static and oscillatory components. The reactive power loop is in communication with the second input and the output, governs reactive power contribution, and receives measurements of the objective function which contain static and oscillatory components.
Generative diffusion model surrogates for mechanistic agent-based biological models
Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.
Adsorption of ethane, ethene, and ethyne on reconstructed Fe 3 O 4 (001)
The adsorption of the C2 hydrocarbons including ethane, ethene, and ethyne are studied on magnetite Fe 3 O 4 (001) by a combination of molecular beam dosing, temperature programmed desorption, and X-ray photoelectron spectroscopy. The ethane desorption profile has a single temperature invariant peak at 100 K while ethene and ethyne exhibit additional peaks at ~120 K and ~135 K. An inversion analysis is used to extract coverage dependent desorption energies as well as coverage-averaged prefactors for each molecule. Ethene and ethyne exhibit moderate coverage dependent desorption energies decreasing from ~80 to ~30 kJ/mol at saturation while ethane shows a relatively coverage invariant desorption energy ~28 kJ/mol. The desorption energies of the C2 hydrocarbons increase in order of increasing bond order. This is likely due to the enhanced interaction of the ethene and ethyne p system to the coordinatively unsaturated octahedral Fe sites (Fe oct ) on the ¬oxide surface. The saturation coverages of each C2 hydrocarbon at 82 K were also determined. Furthermore, these match well with the 2-dimensional spacing determined from their liquid densities.
Resilience of terrestrial and aquatic fauna to historical and future wildfire regimes in western North America
Wildfires in many western North American forests are becoming more frequent, larger, and severe, with changed seasonal patterns. In response, coniferous forest ecosystems will transition toward dominance by fire-adapted hardwoods, shrubs, meadows, and grasslands, which may benefit some faunal communities, but not others. We describe factors that limit and promote faunal resilience to shifting wildfire regimes for terrestrial and aquatic ecosystems. We highlight the potential value of interspersed nonforest patches to terrestrial wildlife. Similarly, we review watershed thresholds and factors that control the resilience of aquatic ecosystems to wildfire, mediated by thermal changes and chemical, debris, and sediment loadings. We present a 2-dimensional life history framework to describe temporal and spatial life history traits that species use to resist wildfire effects or to recover after wildfire disturbance at a metapopulation scale. The role of fire refuge is explored for metapopulations of species. In aquatic systems, recovery of assemblages postfire may be faster for smaller fires where unburned tributary basins or instream structures provide refuge from debris and sediment flows. We envision that more-frequent, lower-severity fires will favor opportunistic species and that less-frequent high-severity fires will favor better competitors. Along the spatial dimension, we hypothesize that fire regimes that are predictable and generate burned patches in close proximity to refuge will favor species that move to refuges and later recolonize, whereas fire regimes that tend to generate less-severely burned patches may favor species that shelter in place. Looking beyond the trees to forest fauna, we consider mitigation options to enhance resilience and buy time for species facing a no-analog future.
Aggregation in bottom-up vulnerability assessments and equity implications: The case of Jordanian households’ water supply
Bottom-up methods for water resources modeling rely on acceptability thresholds to find, through a response surface, which deeply uncertain futures lead to system failure. They commonly treat water users as aggregate actors, which may preclude analysis of the equity impacts of interventions. This paper explores how aggregation choices for large groups of water users lead to different policy recommendations in response surface assessments. Herein, two aggregation methods with varying parameters are considered: percentile satisfaction targets and generalized mean. A 2-dimensional stress-test assessment across groundwater availability and population is applied to household water supply in Jordan. The study compares six different policies covering supply enhancement and rebalancing, using a country-wide multi-agent model that characterizes households across socioeconomic strata. For different aggregation levels, policies are ordered by their associated robustness index. Results show that aggregation choices may modify response surfaces as much as policy changes and strongly determine policy preference. Modifying allocation rules can substantially reduce the disparity in household vulnerability. Preferences defined by aggregation intervals provide a finer understanding of trade-offs among water users and may improve deliberation over equity under deep uncertainty.
Interplay of Kondo physics with incommensurate charge density waves in CeTe 3
CeTe 3 is a 2-dimensional (2D) Van der Waals (VdW) material with incommensurate charge density waves (CDW), extremely high transition temperature (T CDW ), and a large momentum-dependent CDW gap that leaves a significant portion of the Fermi surface intact. It is also considered to be a weak Kondo system, a property unexpected for a material with incommensurate CDW, where each atomic site is slightly different. Here, we study the properties of the CDW state in several RTe3 (R is rare earth) materials and examine the hybridization of itinerant states with the localized Ce 4f multiplet in CeTe 3 by using angle resolved photoemission spectroscopy. We find that the renormalization of the itinerant states originating from the hybridization with the deeper localized 4f states at −260 meV is k−dependent and extends to the Fermi level. As these localized states are far from the Fermi level, the observed hybridization affects the effective masses only marginally and does not lead to heavy fermions. However, since the same renormalizing mechanism normally leads to the heavy fermion physics when the localized 4f states are near the Fermi level, our observation of its strong k−dependence suggests that this could be the reason for discrepancy between the heavy masses in specific heat and light ones in Shubnikov de Haas oscillations, often observed in heavy fermions.
Autonomous direct freeform fabrication strategy for multi-axis additive manufacturing
Multi-axis additive manufacturing (M-AM) enables precise material deposition along both planar and curved layers, eliminating the need for support structures through a continuous material deposition approach. In contrast to conventional 2-dimensional planar layers constrained to a fixed building orientation, the deposition on freeform layers demands the specification of guided curves to determine material deposition directions which are no longer to be fixed to a build direction. There are challenges that arise when fabricating components with multiple “build” directions, necessitating the decomposition of geometries and the specification of guided curves for the resulting volumes. Furthermore, multi-axis systems introduce heightened challenges due to an increased degree of freedom in motion. Consequently, the potential risks of collision between the deposited geometry and the motion platform become a notable concern. This research proposes a freeform layering algorithm to address the challenge of seamless transitions between planar and curved layers in the process planning of M-AM. The algorithm computes 3D “printable” layers by leveraging topological information derived from the geometry to be built and integrates collision-free manufacturability considerations into the computational process. These accumulated volumes serve as a “substrate” and support volumes for subsequent deposition, allowing later layers to be built without the need for additional support material. In conclusion, the proposed method successfully devises a freeform layering approach suitable for intricate models that demand substantial support, thus enabling the fabrication of diverse geometries in a manner previously unachievable.
Online Monitoring System for Concrete Structures Affected by Alkali-Silica Reaction
This report presents a comprehensive study of developing ultrasonic wave and acoustic emission (AE) techniques for long-term monitoring of alkali-silica reaction (ASR) development in concrete. Small, medium, and full-scale concrete specimens were cast, conditioned, and monitored for various periods, from at least one year to 2.4 years. The reactive concrete specimens contain coarse or fine reactive aggregates to study the effects of different types of reactive aggregates. Confinements were also designed to simulate the 2-dimensional confinement effects of reinforcement in the shield building of nuclear power plants. The concrete specimens were stored in a environmental chamber with high humidity and high temperature to accelerate the ASR development. The ultrasonic monitoring data shows high sensitivity to ASR development and could detect cracking initiation well before visible surface cracks occurred. However, the linear ultrasonic analysis based on wave velocity is strongly affected by temperature variation. Therefore, a nonlinear ultrasonic method was proposed to measure thermally induced nonlinear acoustic responses of concrete (thermal modulation of ultrasonic wave). The measured nonlinear acoustic parameter shows a high correlation with ASR expansion across specimens with different reactive aggregates and confinement conditions. The same conclusion was obtained from nonlinear resonance tests on small concrete prisms. Compared to the linear acoustic methods, the nonlinear tests show high sensitivities to ASR damage from internal microcracking initiation at the early stage to visible cracks at the late stage of ASR. The attributes of the thermal modulation of nonlinear ultrasonic method include high sensitivity, immunity to temperature effects, and strong correlation with ASR expansion, which demonstrate great potentials of the nonlinear ultrasonic method for diagnosis of ASR damage and prediction of concrete deterioration process. Acoustic emission is a passive sensing technique for damage assessment, and access to only one surface is needed even for thick and heavily reinforced elements such as the walls utilized for nuclear shield building. Additionally, relatively few sensors are required to monitor the progression of the damage process. Results indicate that the AE data can be related to the damage rating index, which is a petrography-based means of assessing damage due to ASR in reinforced concrete. Furthermore, AE is capable of detecting ASR damage long before surface cracking is visually noticeable. Boundary conditions play in important role in the progression of ASR damage and differences in boundary conditions are reflected in the AE data. Entropy based data assessment methods provide a means to assess the damage state, and convolutional neural network based data assessment provided a means to assess the damage state in real-time. Results indicate that artificial neural network models may be used as a means to predict volumetric expansion based on AE data.