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At least 199 records · Page 11

Limited Dynamic Earthquake Triggering in Nevada

Dynamic triggering occurs when seismic waves from distant large earthquakes temporarily alter stress conditions along faults, potentially triggering new earthquakes hundreds to thousands of kilometers away from the source. Previous studies have linked triggered seismicity to anthropogenic activities such as geothermal, oil, and gas production. Although these activities are present in Nevada, little work has been conducted to explore dynamically triggered seismicity in Nevada. Here, we analyze a newly published, high-resolution earthquake catalog for Nevada to identify local seismicity dynamically triggered by teleseismic events (Mw≥7) from 2008 to 2023. We identify 94 dynamically triggered earthquakes concentrated in four distinct regions, which qualitatively show a modest positive correlation with geothermal well locations. Triggered seismicity in Nevada is predominantly delayed, with some instantaneously triggered by Rayleigh waves. The prevalence of delayed triggering indicates that pore fluid interactions may play a critical role in controlling dynamic triggering susceptibility in Nevada. Our results demonstrate that dynamic triggering can provide valuable insight to help identify critically stressed regions.

58 GEOSCIENCES↗

Signal amplification in a solid-state sensor through asymmetric many-body echo

Electronic spins of nitrogen–vacancy centres in diamond constitute a promising system for micro- and nanoscale magnetic sensing, because of their operation under ambient conditions, ease of placement in close proximity to sensing targets and biological compatibility. At high densities, the electronic spins interact through dipolar coupling, which typically limits but can also potentially enhance sensing performance. Here we report the experimental demonstration of many-body signal amplification in a solid-state, room-temperature quantum sensor. Our approach uses time-reversed two-axis-twisting interactions, engineered through dynamical control of the quantization axis and Floquet engineering in a two-dimensional ensemble of nitrogen–vacancy centres. We observe that optimal amplification occurs when the backward evolution time equals twice the forward evolution time, in sharp contrast to the conventional Loschmidt echo. These observations can be understood as resulting from an underlying time-reversed mirror symmetry of the microscopic dynamics, providing key insights into signal amplification and opportunities for practical entanglement-enhanced quantum sensing.

quantum information↗

Dynamic metal-linker bonds in metal–organic frameworks

Metal-linker bonds serve as the “glue” that binds metal ions to multitopic organic ligands in the porous materials known as metal–organic frameworks (MOFs). Despite ample evidence of bond lability in molecular and polymeric coordination compounds, the metal-linker bonds of MOFs were long assumed to be rigid and static. Given the importance of ligand fields in determining the behaviour of metal species, labile bonding in MOFs would help explain outstanding questions about MOF behaviour, while providing a design tool for controlling dynamic and stimuli-responsive optoelectronic, magnetic, catalytic, and mechanical phenomena. Here, in this work, we present emerging evidence that MOF metal-linker bonds exist in dynamic equilibria between weakly and tightly bond conformations, and that these equilibria respond to guest–host chemistry, drive phase change behavior, and exhibit size-dependence in MOF nanoparticles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks

This paper presents a secure reinforcement learning (RL) based control method for unknown linear time-invariant cyber-physical systems (CPSs) that are subjected to compositional attacks such as eavesdropping and covert attack. We consider the attack scenario where the attacker learns about the dynamic model during the exploration phase of the learning conducted by the designer to learn a linear quadratic regulator (LQR), and thereafter, use such information to conduct a covert attack on the dynamic system, which we refer to as doubly learning-based control and attack (DLCA) framework. We propose a dynamic camouflaging based attack-resilient reinforcement learning (ARRL) algorithm which can learn the desired optimal controller for the dynamic system, and at the same time, can inject sufficient misinformation in the estimation of system dynamics by the attacker. The algorithm is accompanied by theoretical guarantees and extensive numerical experiments on a consensus multi-agent system and on a benchmark power grid model.

Mukherjee, Sayak↗

Dynamically reconfigurable topological routing in nonlinear photonic systems

The propagation path of topologically protected states is bound to the interface between regions with different topology, and as such, the functionality of linear photonic devices leveraging these states is fixed during fabrication. Here, we propose a mechanism for dynamic control over a driven dissipative system’s local topology, yielding reconfigurable topological interfaces and thus tunable paths for protected routing. We illustrate our approach in non-resonantly pumped polariton lattices, where the nonlinear interaction between the polaritons and the exciton reservoir due to non-resonant pumping can yield picosecond-scale changes in the propagation paths of the chiral edge states. To analytically confirm the numerically observed topological dynamics, we generalize the spectral localizer framework to non-linear non-Hermitian Chern materials and apply this framework to a continuous model of the polariton system based on a driven-dissipative Gross-Pitaevskii equation. In doing so, we show that the local changes in the polariton lattice’s topology are captured by a local Chern marker. Looking forward, we anticipate such reconfigurable topological routing will enable the realization of novel classes of topological photonic devices.

Wong, Stephan [Sandia National Laboratories (SNL-N↗

Adaptive Deep Reinforcement Learning Algorithm for Distribution System Cyber Attack Defense With High Penetration of DERs

With grid modernization, smart inverters are increasingly used to execute advanced controls for distribution network reliability. However, this also increases the cyber-attack space. Here this paper focuses on the defense approaches to restore the system to normal operation circumstances in the presence of cyber-attacks. A unique deep reinforcement learning (DRL) method is developed to minimize voltage violations and reduce power losses for impacted feeders. The defense problem is reformulated as a Markov decision-making process to dynamically control DERs while minimizing load shedding. This is achieved via an improved soft actor-critic (SAC)-based DRL algorithm, which can govern DER set points and load-shedding scenarios in discrete and continuous modes via the auto-tune entropy and Gaussian policy features. Numerical comparison results on the modified IEEE 123-node system with other control approaches, such as Volt-VAR (VV), Volt-Watt (VW), and model predictive control (MPC) show that the proposed method can eliminate voltage violations and provide feasible control actions that perform complete mitigation of cyber-threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Role of ocean coupling in the weakening of the extratropical storm tracks from Arctic sea ice loss

Abstract Within the changing climate, the Northern Hemisphere storm tracks have been projected to shift poleward and expand eastward. Changes in the Northern Hemisphere storm tracks arise from a variety of sometimes competing effects on the region's baroclinicity. Arctic amplification weakens the meridional temperature gradient, hence weakening the storm track, while low-latitude warming has the opposite effect, and surface-amplified warming at high latitudes reduces static stability. To determine the mechanisms driving these competing changes, we use a hierarchy of models with different levels of ocean-atmosphere coupling, using forcings from the Polar Amplification Model Intercomparison Project (PAMIP) to assess the role of Arctic sea ice loss on the Northern Hemisphere storm tracks. We find that ocean-atmosphere coupling enhances and sharpens the weakening of both the North Atlantic and North Pacific storm tracks in response to Arctic sea ice loss, that surface turbulent heat flux modulates the intensity of the weakening, and that local ocean dynamics controls the meridional location of the response. A moist isentropic diagnostic of the atmospheric overturning circulation shows that most of the weakening of the storm tracks and its associated changes in atmospheric heat transport (AHT) arise from a weakening of the transient eddy mass flux. Poleward of the storm tracks, the AHT also decreases, but through weakened effective stratification rather than weakening mass fluxes, an effect which occurs even in the absence of ocean dynamical coupling.

Audette, Alexandre (ORCID:0000000163321088)↗

Adaptive nonlinear wavefront shaping in layered TMDs

Two-dimensional transition metal dichalcogenides are promising candidates for nonlinear photonics applications, offering strong nonlinear susceptibility and compatibility with integrated platforms. Although considerable effort has been devoted to manipulating second harmonic generation in these materials, the dynamic control of nonlinear wavefronts remains largely unexplored. Metasurfaces have enabled significant progress in nonlinear beam engineering, yet their practical implementation is limited by low conversion efficiencies and restricted design flexibility. In this work, we employ feedback-based wavefront shaping using spatial light modulators to enhance SHG from pyramid-like WS 2 multilayer structures by over three orders of magnitude in selected spatial regions. This approach allows us to program nonlinear holograms and dynamically shape the SHG signal through phase-only modulation, opening new possibilities for nonlinear imaging, optical information processing, and data communication.

Mavian, Alex [Rensselaer Polytechnic Inst., Troy, ↗

Floquet control of indirect exchange interaction in periodically driven two-dimensional electron systems

We present a theory for the Ruderman–Kittel–Kasuya–Yosida (RKKY) interaction mediated by a two-dimensional (2D) electron system subjected to periodic driving. This is demonstrated for a 2D metal with two ferromagnetic chains deposited in parallel. Our calculations reveal new non-analytic features in the time-averaged spin susceptibility. For weak light–matter coupling, the RKKY interaction shows oscillations with a period tunable by the light amplitude and frequency. For stronger light–matter coupling, the interaction becomes non-oscillatory and remains purely ferromagnetic. Our findings open a path forward for realizing dynamic control of the indirect exchange interaction in 2D magnetic structures.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Photoinduced frustration modulation in 𝜅-type quantum spin liquid candidates

Geometric frustration is a key parameter controlling electronic and magnetic properties of quantum spin liquid systems, yet remains challenging to tune. Here, we coherently drive molecular vibrations with midinfrared pulses in two organic quantum spin liquid candidates, the insulating 𝜅−(BEDT−TTF) 2⁢ Cu 2 ⁢(CN) 3 and the metallic 𝜅−(BEDT−TTF) 4 ⁢Hg 2.89 ⁢Br 8 , and probe their electronic response through ultrafast reflectivity measurements. We observe a nonlinear coupling between local molecular vibrations and nonlocal phonons, which is expected to directly modulate the geometric frustration of their triangular lattice. Furthermore, our findings establish a promising route to dynamically control frustration in nonbipartite quantum materials.

Frustrated magnetism↗

Population-level control of two manganese oxidases expands the niche for bacterial manganese biomineralization

Abstract The enzymatic oxidation of aqueous divalent manganese (Mn) is a widespread microbial trait that produces reactive Mn(III, IV) oxide minerals. These biominerals drive carbon, nutrient, and trace metal cycles, thus playing important environmental and ecological roles. However, the regulatory mechanisms and physiological functions of Mn biomineralization are unknown. This challenge arises from the common occurrence of multiple Mn oxidases within the same organism and the use of Mn oxides as indicators of combined gene activity. Through the detection of gene activation in individual cells, we discover that expression ofmnxGandmcoA, two Mn oxidase-encoding genes inPseudomonas putidaGB-1, is confined to subsets of cells within the population, with each gene showing distinct spatiotemporal patterns that reflect local microenvironments. These coordinated intra-population dynamics control Mn biomineralization and illuminate the strategies used by microbial communities to dictate the extent, location, and timing of biogeochemical transformations.

Biotechnology & Applied Microbiology↗

Long-lived photoinduced polar states in metal halide perovskites

Ferroic polarization in hybrid perovskites is crucial for enhancing photovoltaic performance and developing potential electronic applications. Controlling ferroic polarization with an optical field enables probing of ferroic polarization without the unwanted interface ionic effects caused by electronic contacts. This study employs ultrafast near-infrared photoexcitation to control dynamic structural transitions in soft single crystalline hybrid Cu (II) halide perovskites, achieving a long-lived polar state (beyond 104 s) at room temperature. We probe reversible long-lived polar domains under near-infrared photoexcitation using in-situ second harmonic generation microscopy. Theoretical calculation informs the polar lattice microstrain likely induced by anisotropic structure deformation in octahedral copper halide under near-infrared photoexcitation. The reversible slow structure deformation is further confirmed by in-situ photo-induced X-ray diffraction measurement. This work provides a material platform for understanding, controlling, and probing polarization under photoexcitation. Our methodology enables the identification of previously undiscovered polar phases in ferroelectric halide perovskites.

Dou, Yixuan↗

Whitepaper: Optimal Control from a Fluid Dynamics Perspective

An optimal control problem described by the Hamilton-Jacobi-Bellman equation can be developed into a problem that can be solved by general computational fluid dynamics packages. We describe how this formulation would allow a classical problem in optimal control, Zermelo’s problem, to be treated as a multi-fluid problem. This approach has the advantage of allowing optimal navigation problems to be conducted over large areas, as well as to include moderately larger numbers of ships. We draw comparisons between this approach and the field of fluid control for fluid animations in movies.

42 ENGINEERING↗

Control simulations of many-body quantum systems by a synergism of discrete real-time learning and optimal control theory

We present a self-consistent algorithm for optimal control simulations of many-body quantum systems. The algorithm features a two-step synergism that combines discrete real-time machine learning (DRTL) with Quantum Optimal Control Theory (QOCT) using the time-dependent Schrödinger equation. Specifically, in step (1), DRTL is employed to identify a compact working space (i.e., the important portion of the Hilbert space) for the time evolution of the many-body quantum system in the presence of a control field (i.e., the initial or previously updated field), and in step (2), QOCT utilizes the DRTL-determined working space to find a newly updated control field for a chosen objective. Steps 1 and 2 are iterated until a self-consistent control objective value is reached such that the resulting optimal control field yields the same targeted objective value when the corresponding working space is systematically enlarged. Furthermore, to demonstrate this two-step self-consistent DRTL-QOCT synergistic algorithm, we perform optimal control simulations of strongly interacting 1D as well as 2D Heisenberg spin systems. In both scenarios, only a single spin (at the left end site for 1D and the upper left corner site for 2D) is driven by the time-dependent control fields to create an excitation at the opposite site as the target. It is found that, starting from all spin-down zero excitation states, the synergistic method is able to identify working spaces and convergence of the desired controlled dynamics with just a few iterations of the overall algorithm. In the cases studied, the dimensionality of the working space scales only quasi-linearly with the number of spins.

Artificial neural networks↗

Wide-range continuous tuning of the thermal conductivity of La0.5Sr0.5CoO3-δ films via room-temperature ion-gel gating

Abstract Solid-state control of the thermal conductivity of materials is of exceptional interest for novel devices such as thermal diodes and switches. Here, we demonstrate the ability to continuously tune the thermal conductivity of nanoscale films of La 0.5 Sr 0.5 CoO 3- δ (LSCO) by a factor of over 5, via a room-temperature electrolyte-gate-induced non-volatile topotactic phase transformation from perovskite (with δ ≈ 0.1) to an oxygen-vacancy-ordered brownmillerite phase (with δ = 0.5), accompanied by a metal-insulator transition. Combining time-domain thermoreflectance and electronic transport measurements, model analyses based on molecular dynamics and Boltzmann transport equation, and structural characterization by X-ray diffraction, we uncover and deconvolve the effects of these transitions on heat carriers, including electrons and lattice vibrations. The wide-range continuous tunability of LSCO thermal conductivity enabled by low-voltage (below 4 V) room-temperature electrolyte gating opens the door to non-volatile dynamic control of thermal transport in perovskite-based functional materials, for thermal regulation and management in device applications.

42 ENGINEERING↗

Precise Linker Length and Dynamic Bond Exchange Control Penetrant Diffusion in Dense Vitrimers

Polymer networks with dynamic covalent bonds have been investigated for their self-healing ability, recyclability, and potential as more sustainable materials. Recent results have indicated that in some cases, bond exchange can enhance the transport of penetrants in dense networks, pointing to their potential for separations of membranes. Here, imine dynamic bonds in ethylene oxide (EO) networks with precise linker lengths were synthesized to investigate the transport of N,N′-bis(2,5-di-tert-butylphenyl)-3,4,9,10-perylenedicarboximide (BTBP), a large, anisotropic dye molecule. Networks with mesh sizes smaller than, comparable to, and greater than the size of the penetrant axes were investigated to probe the effects of bond exchange and network confinement on transport. Mesh sizes, which ranged from 0.5 to 1.62 nm, were determined from shear rheology, glass transitions by calorimetry, and probe diffusion coefficients by fluorescence recovery after photobleaching. Permanent networks with identical EO chain lengths were prepared as control samples, and up to a 3 orders of magnitude increase in diffusion coefficient is observed in the dynamic systems for short linkers containing 13 backbone atoms. The longest linkers with 71 backbone atoms show no difference between the permanent and dynamic networks. Linkers shorter than 11 backbone atoms, corresponding to a mesh size smaller than the penetrant small axis, diffusion is no longer observable on the experimental time scale, indicating a sharp cutoff attributed to the precise linkers and narrow mesh size distribution. The dynamic imine exchange time scales were compared to the diffusive hopping times of penetrants and indicate that exchange can occur during a diffusive displacement. Furthermore, these findings provide insights into the factors affecting penetrant transport in dense polymers and inspire the development of next-generation selective polymer membranes.

Diffusion↗

A real-time distributed solid oxide electrolysis cell (SOEC) model for cyber-physical simulation

System integration and dynamic operability between SOEC and balance-of-plant (BoP) components are major technical challenges before realizing rapid load following of SOEC systems. Cyber-physical simulation (CPS) is a leading-edge digital engineering approach and is regarded as the next step beyond Digital Twins. CPS approach can be used to research SOEC system integration and develop dynamic controls prior to actual pilot testing without using a real SOEC. To seamlessly couple with BoP hardware and access non-observable operational parameters (e.g., local temperature gradient) during transients, a distributed one-dimensional (1D) real-time SOEC model was developed. Its real-time execution was demonstrated for 20 to 640 nodes at the fixed time step of 5 ms. A higher excess air ratio enabled smaller local temperature gradients on SOEC solid materials and faster transients upon current density step change from 0.15 to 0.55 A cm -2 . During the transients, the magnitude of the peak temperature gradient nearly doubled in 10 s from -3.5 to -5.9 °C cm -1 . This represents a significant operating risk that can impact the dynamic operability of SOEC systems. In addition, the local temperature gradient was found to change directions on all nodes in SOEC solid materials, with the greatest impact on the upstream nodes. The SOEC model was also tested at the thermal neutral voltage using actual process air flow parameters as variable model inputs. Variable process air temperatures were found to induce alternating local temperature gradients on SOEC solid materials. These are new operational mechanisms for SOEC degradation relevant for load following operational modes yet distinct from previous reports. To mitigate these unfavorable features, the SOEC can be operated at voltages that are slightly (±20 mV) deviated from the thermal neutral voltage. Here, the corresponding net thermal energy change was less than 1.6% of the electric power consumption. This 1D real-time SOEC model established the basis of cyber-physical simulation of SOEC hybrid systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Long-term super-resolution inner mitochondrial membrane imaging with a lipid probe

The inner mitochondrial membrane (IMM) generates power to drive cell function, and its dynamics control mitochondrial health and cellular homeostasis. Here, we describe the cell-permeant, lipid-like small molecule MAO-N 3 and use it to assemble high-density environmentally sensitive (HIDE) probes that selectively label and image the IMM in live cells and multiple cell states. MAO-N 3 pairs with strain-promoted azide–alkyne click chemistry-reactive fluorophores to support HIDE imaging using confocal, structured illumination, single-molecule localization and stimulated emission depletion microscopy, all with significantly improved resistance to photobleaching. These probes generate images with excellent spatial and temporal resolution, require no genetic manipulations, are non-toxic in model cell lines and primary cardiomyocytes (even under conditions that amplify the effects of mitochondrial toxins) and can visualize mitochondrial dynamics for 12.5 h. This probe will enable comprehensive studies of IMM dynamics with high temporal and spatial resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗