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At least 577 records · Page 32

Bistable random momentum transfer in a linear on-chip resonator

Optical switches and bifurcation rely on the nonlinear response of materials. Here, we demonstrate linear temporal bifurcation responses in a passive multimode microresonator, with strongly coupled chaotic and whispering gallery modes (WGMs). In microdisks, the chaotic modes exhibit broadband transfer within the deformed cavities, but their transient response is less explored and yields a random output of the analog signal distributed uniformly from “0” to “1.” Here, we build chaotic states by perturbing the multimode microring resonators with densely packed silicon nanocrystals on the waveguide surface. In vivo measurements reveal random and “digitized” output that ONLY populates around 0 and 1 intensity levels. The bus waveguide mode couples first to chaotic modes, then either dissipates or tunnels into stable WGMs. This binary pathway generates high-contrast, digitized outputs. In conclusion, the fully passive device enables real-time conversion of periodic clock signals into binary outputs with contrasts exceeding 12.3 dB, data rates of up to 10 7 · bits per second, and 20 dB dynamic range.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Investigating the impact of intermediate-mode perturbations on diagnosing plasma conditions in DT cryogenic implosions via synthetic x-ray Thomson scattering

The pursuit of inertial confinement fusion ignition target designs requires precise experimental validation of the conditions within imploding capsules, in particular the density and temperature of the compressed shell. Previous work has identified x-ray Thomson scattering (XRTS) as a viable diagnostic tool for inferring the in-flight compressed deuterium-tritium shell conditions during capsule implosions (Poole et al 2022 Phys. Plasmas 29 072703). However, this study focused on one-dimensional simulations, which do not account for the growth of hydrodynamic instabilities. In this work, two-dimensional DRACO simulations incorporating intermediate-mode perturbations up to Legendre mode l = 50 were used to generate synthetic XRTS spectra with the SPECT3D code. The analysis employed Markov-Chain Monte Carlo techniques to infer plasma conditions from these spectra. The results demonstrate that the XRTS diagnostic platform can effectively discern the in-flight compressed shell conditions for targets with varying adiabats, even in the presence of intermediate-mode perturbations. This work underscores the potential of XRTS for realistic inertial confinement fusion experiments, providing a robust method for probing the complex dynamics of fusion implosions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Classical Non-Markovian Noise in Symmetry-Preserving Quantum Dynamics

In quantum dynamics, symmetries are vital for identifying and assessing conserved quantities that govern the evolution of a quantum system. When promoted to the open quantum system setting, dynamical symmetries can be negatively altered by system-environment interactions, thus, complicating their analysis. Previous work on noisy symmetric quantum dynamics has focused on the Markovian setting, despite the ubiquity of non-Markovian noise in a number of widely used quantum technologies. Here, in this Letter, we develop a framework for quantifying the impact of non-Markovian noise on symmetric quantum evolution via root space decompositions and the filter function formalism. We demonstrate analytically that symmetry-preserving noise maintains the symmetric subspace, while nonsymmetric noise leads to highly specific leakage errors that are block diagonal in the symmetry representation. We support our findings with numerical studies of a transverse-field Ising model and a quantum error detecting code subject to spatiotemporally correlated multiaxis noise. Our results are broadly applicable, providing new analytic insights into the control and characterization of open quantum system dynamics.

decoherence↗

Spin-filter tunneling detection of antiferromagnetic resonance with electrically tunable damping

Antiferromagnetic spintronics offers the potential for higher-frequency operations and improved insensitivity to magnetic fields compared to ferromagnetic spintronics. However, previous electrical techniques to detect antiferromagnetic dynamics have utilized large, millimeter-scale bulk crystals. In this work, we demonstrate direct electrical detection of antiferromagnetic resonance in structures on the few-micrometer scale using spin-filter tunneling in platinum ditelluride (PtTe 2 )/bilayer chromium sulfide bromide (CrSBr)/graphite junctions in which the tunnel barrier is the van der Waals antiferromagnet CrSBr. This sample geometry allows not only efficient detection but also electrical control of the antiferromagnetic resonance through spin-orbit torque from the PtTe 2 electrode. The ability to efficiently detect and control antiferromagnetic resonance enables detailed studies of the physics governing these high-frequency dynamics.

Cham, Thow Min Jerald [Cornell University, Ithaca,↗

Synoptic and Mesoscale Modulation of Dynamic and Thermodynamic Impacts on Central Arctic Sea Ice During MOSAiC

The primary objectives of this proposed project are to determine 1) to what extent dynamic and thermodynamic air‐ice interactions in the Central Arctic are driven by atmospheric synoptic and mesoscale phenomena, and 2) the key processes involved in these interactions. These phenomena include large‐scale features such as fronts, high‐ and low‐pressure systems, low‐level jets, and cloud bands, but may also include smaller‐scale phenomena such as mesoscale instabilities (e.g., symmetric instability or slantwise convection), transient meso‐lows and gravity waves. Dynamic air‐ice interactions include direct atmospheric stress and atmospheric stress divergence/deformation; thermodynamic air‐ice interactions involve the net surface energy budget, especially in association with non‐stratocumulus clouds generally occurring with stronger dynamical forcing.

58 GEOSCIENCES↗

Studies of particle transport in high-energy-density plasma in the presence of a megagauss magnetic field

Charged particle transport in a magnetic field is among the most fundamental phenomena in plasma physics. Magnetic fields are often introduced in conventional magnetized fusion devices to suppress particle and heat transport, a phenomenon that has been extensively studied. In contrast, particle and heat transport in magnetized high-energy-density (HED) plasmas remain relatively unexplored due to the challenges of generating an external magnetic field strong enough to significantly alter particle dynamics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Existing Methods for Grid Strength Assessment and Role of Hydropower in Future Grids

The power system is undergoing rapid evolution with increasing penetration of inverter-based resources (IBRs), large loads, microgrids, and power electronic devices. Ensuring reliable and stable operation of the modern power grid is a multi-faceted challenge that requires detailed understanding of this complex system. Dynamic stability is a major concern in maintaining the security of power grids as the generation mixes and large loads transitions to include high shares of power electronic devices. The operation of IBRs in regions with low system strength and higher grid impedances has been found to be the main reason behind many of the power system instabilities that manifest themselves in various types of oscillations and interactions that, if not properly addressed and damped, can jeopardize the reliable operation of the power system. Weak grid conditions compound such stability problems, particularly when many IBRs operate in proximity to and connect to weak power grids. Therefore, it is important to assess grid strength for planning, integration and operation of IBRs for a given power system. In this report, we aim to understand and classify existing methodologies for grid strength assessment, along with their limitations and future needs. Further, we aim to utilize the huge untapped potential in utilizing hydro energy resources in addressing some of the pertinent challenges of grid strength and reliable operation of modern power systems. Hydropower, historically valued for its flexibility and dispatchability, now faces new constraints due to reduced share of synchronous machines in the generation mix and seasonal variability of available water resources. Yet, these same plants present untapped potential beyond energy generation - notably, as providers of critical grid services. This report explores how hydropower plants, particularly through operation as synchronous condensers, can play a pivotal role in strengthening the grid amid evolving system dynamics.

13 HYDRO ENERGY↗

Digital Twin User Guide for Chelan County Public Utility District

This user manual offers a comprehensive guide for developing a Digital twin (DT) of a Kaplan turbine at Chelan County Public Utility District (Chelan PUD) using neural networks. As variable renewable generation expands, hydropower units must operate with optimal efficiency and stability. For Kaplan machines, this flexibility is achieved through coordinated control of guide vane (wicket gates) opening and runner blade pitch, which amplifies the plant’s inherent nonlinear behavior and challenges traditional physics-only modeling. The efficiency of the Kaplan turbine varies with different combinations of the guide vans (wicket gate) opening and the blade angle. Each guide van opening and blade angle has a corresponding highest efficiency point, forming a cam relationship that represents the optimal combination.The discharge of a hydraulic turbine is controlled by the opening angle of the guide vans. Therefore, for each value of head, there is a certain guide van opening and blade angle that corresponds to the highest efficiency. For a given head, different combinations of the guide van opening and blade angle have different efficiencies. Therefore, coordinate cam curves are used to describe the relationship between the wicket gate opening and blade angle with different water head. To address these challenges, the manual details a data-driven modeling and learning workflow centered on structured neural networks. The approach is designed to forecast critical operational variables—discharge flow, net head, penstock (or scroll-case) pressure, and generator electrical outputs—by leveraging real-time inputs such as the generator power control setpoint, exciter field current and field voltage, together with hydromechanical commands (e.g., gate position and, when available, runner blade-pitch angle). The neural models are trained and validated on operational data from a Kaplan unit operated by Chelan PUD, demonstrating that the structured NN architecture can learn the coupled gate–blade–electrical dynamics. The result is a robust DT that improves situational awareness and supports data-informed decision-making for Chelan PUD’s Kaplan turbine operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Dynamics and Formation of Antiferromagnetic Textures in MnBi 2 Te 4 Single Crystal

We report coherent X-ray imaging of antiferromagnetic (AFM) domains and domain walls in MnBi 2 Te 4 , an intrinsic AFM topological insulator. This technique enables direct visualization of domain morphology without reconstruction algorithms, allowing us to resolve antiphase domain walls as distinct dark lines arising from the A-type AFM structure. The wall width is determined to be 550(30) nm, in good agreement with earlier magnetic force microscopy results. The temperature dependence of the AFM order parameter extracted from our images closely follows previous neutron scattering data. Remarkably, however, we find a pronounced hysteresis in the evolution of domains and domain walls: upon cooling, dynamic reorganizations occur within a narrow ∼1 K interval below 𝑇 𝑁 , whereas upon warming, the domain configuration remains largely unchanged until AFM order disappears. These findings reveal a complex energy landscape in MnBi 2 Te 4 , governed by the interplay of exchange, anisotropy, and domain-wall energies, and underscore the critical role of AFM domain-wall dynamics in shaping its physical properties. These sharply defined and hysteretically evolving walls may provide a controllable AFM texture in MnBi 2 Te 4 , hinting at potential use in low-power spintronic devices based on domain-wall dynamics.

36 MATERIALS SCIENCE↗

Long timescale solvation dynamics and confinement: The case of non-ionic deep eutectic solvents of lauric acid and N-methylacetamide

Microscopic segregation and molecular heterogeneities in complex liquids are the result of the interplay between different intermolecular forces, all of which contribute to the energy landscape of the system. A consequence of the intricate energy landscape is the nontrivial effect on the solvation dynamics. Here, the impact of molecular heterogeneities on the solvation dynamics is studied using infrared spectroscopies and molecular dynamics simulations. In particular, this study focuses on the dynamical effect of nanoscopic heterogeneities present in deep eutectic solvents (DESs) composed of lauric acid (LA) and N-methylacetamide (NMA). To this end, a molecular probe containing a carbon triple bond is used as an infrared reporter. The results show that the vibrational probe is likely to be located in the NMA polar domains. Furthermore, the probe solvation dynamics derived from the 2DIR spectra presents a slowdown of its timescale with increasing LA concentration in the DES. Kubo modeling of the probe solvation dynamics shows a correlation between the amplitude of its long time component and the presence of molecular heterogeneities in the sample. Semiclassical modeling of the vibrational line shape of the triple bond stretch demonstrates that the heterogeneities affect the whole solvation dynamics of the system through the amplitudes of the frequency fluctuations. Molecular dynamics simulations confirm the experimental results and their interpretation by showing a slowdown of the solvation dynamics when the LA heterogeneities are present. Overall, the study presents a molecular framework to explain the effect of confinement created by nanoscopic LA heterogeneities on the solvation dynamics of the system.

Chemical compounds↗

Robust Implicit Adaptive Low Rank Time-Stepping Methods for Matrix Differential Equations

In this work, we develop implicit rank-adaptive schemes for time-dependent matrix differential equations. The dynamic low rank approximation (DLRA) is a well-known technique to capture the dynamic low rank structure based on Dirac–Frenkel time-dependent variational principle. In recent years, it has attracted a lot of attention due to its wide applicability. Our schemes are inspired by the three-step procedure used in the rank adaptive version of the unconventional robust integrator (the so called BUG integrator) (Ceruti et al. in BIT Numer Math 62(4):1149–1174, 2022) for DLRA. First, a prediction (basis update) step is made computing the approximate column and row spaces at the next time level. Second, a Galerkin evolution step is invoked using an implicit solves for the small core matrix. Finally, a truncation is made according to a prescribed error threshold. Since the DLRA is evolving the differential equation projected on to the tangent space of the low rank manifold, the error estimate of the BUG integrator contains the tangent projection (modeling) error which cannot be easily controlled by mesh refinement. This can cause convergence issue for equations with cross terms. To address this issue, we propose a simple modification, consisting of merging the row and column spaces from the explicit step truncation method together with the BUG spaces in the prediction step. In addition, we propose an adaptive strategy where the BUG spaces are only computed if the residual for the solution obtained from the prediction space by explicit step truncation method, is too large. Here, we prove stability and estimate the local truncation error of the schemes under assumptions. We benchmark the schemes in several tests, such as anisotropic diffusion, solid body rotation and the combination of the two, to show robust convergence properties.

97 MATHEMATICS AND COMPUTING↗

Forecasting high-dimensional spatio-temporal systems from sparse measurements

This paper introduces a new neural network architecture designed to forecast high-dimensional spatio-temporal data using only sparse measurements. The architecture uses a two-stage end-to-end framework that combines neural ordinary differential equations (NODEs) with vision transformers. Initially, our approach models the underlying dynamics of complex systems within a low-dimensional space; and then it reconstructs the corresponding high-dimensional spatial fields. Many traditional methods involve decoding high-dimensional spatial fields before modeling the dynamics, while some other methods use an encoder to transition from high-dimensional observations to a latent space for dynamic modeling. In contrast, our approach directly uses sparse measurements to model the dynamics, bypassing the need for an encoder. This direct approach simplifies the modeling process, reduces computational complexity, and enhances the efficiency and scalability of the method for large datasets. We demonstrate the effectiveness of our framework through applications to various spatio-temporal systems, including fluid flows and global weather patterns. Although sparse measurements have limitations, our experiments reveal that they are sufficient to forecast system dynamics accurately over long time horizons. Our results also indicate that the performance of our proposed method remains robust across different sensor placement strategies, with further improvements as the number of sensors increases. This robustness underscores the flexibility of our architecture, particularly in real-world scenarios where sensor data is often sparse and unevenly distributed.

97 MATHEMATICS AND COMPUTING↗

Topical Report on Water Balance Model Development and Initial Application

This topical report summarizes results from developing a generic dynamic water balance tool which can be used by coal-fired power plant operators to evaluate FGD water management strategies. EPRI is using a statistical software model created on the GoldSim platform and data from coal-fired energy plants to determine the fraction of power plant water needs that can be satisfied by electrodialysis reversal treatment and, correspondingly, the discharge volume and/or byproducts that still need to be managed. The generic model is designed to accept a number of easily obtainable process variables and generate probabilities of resultant water characteristics. The model is also constructed as a universal process flow; one that can be adjusted to accommodate most major components of an FGD blowdown handling system. The generic model is dynamic and utilizes user-inputted operational variation in water quality and quantity to best provide a probabilistic representation of potential performance outcomes. In order to account for complexities of water composition and treatment, a separate chemical modeling software is used to evaluate water chemistry and unit operation performance. The thermodynamic modeling software, OLI Studio, is employed to convert ion data from laboratory analyses into balanced molecular concentrations that are classified as dissolved and suspended solids. The dynamic water model is designed to import a total of 11 species commonly found in FGD blowdown water as molecular concentrations.Water management unit operations are used in the model and categorized by either physical unit operation blocks or chemical unit operations blocks. As the name implies, the physical water treatment blocks are unit operations that are exclusively physical changes to the system (e.g., separations and volume changes). The chemical water treatment blocks are unit operations where chemical changes occur and require the use of OLI Studio to characterize subsequent streams. Water and material balances from three unique coal-fired power plants were obtained and preliminary modeling on the dynamic water balance tool was performed using the extracted data from these power plants. These three power plants were chosen due to their unique configurations and water profiles that represented a variety of operational changes. Results from using the dynamic tool with the three plant configurations are presented and discussed in the context of the existing tool and planned future capabilities.

01 COAL, LIGNITE, AND PEAT↗

Experimental measurements of gas-particle flows in large-scale strippers

Measurements of gas–solid particle flow in a large-scale stripper unit are reported. The experiments target industrial (pilot) scale measurements of gas-particle flow that can be used to validate numerical methods for modeling multiphase flows, such as computational fluid dynamics coupled to the discrete element method (CFD-DEM). Specifically, experiments were performed with Geldart Group B (533 µm) glass beads in a stripper unit. In general, previous CFD-DEM validation datasets were performed in benchtop experimental systems and limited to O(10 5 ) particles. Here, in this work, the number of particles in the isolated section of the stripper is O(10 7 ) for all tests. Measurements are reported for a ~2 m tall section of the ~1 m diameter stripper. Radial mass flux profiles measured at five axial positions and three axial pressure drop measurements are reported for six operating conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Photoinduced electronic excitations drive polymerization of carbon monoxide: A first-principles study

Under pressure, carbon monoxide (CO) transforms into a polymer that can be recovered to ambient conditions. While this transformation can occur without additional stimuli, experimental observations have shown that laser irradiation can induce a similar transformation at reduced pressure. The resulting polymeric phase, which is metastable under ambient conditions, releases energy through decomposition into more stable configurations. Using time-dependent density functional theory and Born–Oppenheimer molecular dynamics simulations, we investigate the mechanism by which electronic excitation facilitates CO polymerization. Our calculations reveal that electronic excitation enhances carbon–carbon bonding, enabling polymerization at pressures significantly lower than those required by conventional compression methods. In conclusion, these findings suggest that a photo-assisted approach could be employed to synthesize novel, potentially energetic materials under less demanding pressure conditions.

Born-Oppenheimer molecular dynamics↗

Collective Modes in Multilayer Graphene/𝛼−RuCl 3 Heterostructures

Collective modes in multilayer graphene, such as plasmons and phonons, exhibit sensitivity to displacement fields and interlayer coupling, distinguishing them from their counterparts in single-layer graphene. Here, we engineer collective modes in charge-transfer heterostructures composed of multilayer graphene and 𝛼−RuCl 3 . In heterostructures with a single 𝛼−RuCl 3 interface, the charge transfer generates displacement fields up to 7 V/nm at the interface between 𝛼−RuCl 3 and the adjacent graphene layer—the highest value achieved through charge-transfer methods. As a result of the broken inversion symmetry, we discover enhanced nonlinear optical response and modified phonon selection rules. Conversely, we find that multilayer graphene sandwiched between two 𝛼−RuCl 3 flakes causes displacement fields to cancel. There, we achieve carrier densities as high as 8 ×10 13 cm −2 in multilayer graphene and restore the phonon selection rules to their unperturbed state. Meanwhile, we demonstrate that plasmonic properties derive from the depletion of multiple valence bands. As a result of the quasilinear band dispersion, these “Dirac multiband plasmons” are relatively unaffected by displacement fields. On the other hand, the inverted heterostructure sequence—two multilayer graphene sheets encapsulating 𝛼−RuCl 3 —activates significant alteration of the plasmons via interlayer plasmon-plasmon coupling. Hence, multilayer graphene and 𝛼−RuCl 3 heterostructures offer a gate-free platform for engineering collective modes derived from inversion symmetry and interlayer coupling.

Moore, Samuel L. [Columbia University, New York, N↗

Dynamic Windows Using Reversible Metal Electrodeposition

The objective of the project is to design, fabricate, and optimize Reversible Metal Electrodeposition (RME) windows that simultaneously achieve color-neutral, fast, uniform switching over a wide dynamic range using low-cost, high-throughput materials and processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Agile Allocation in the Tundra: A Single Growing Season of Warming Increases Nutrient Availability While Decreasing Fine-Root Length

The majority of plant biomass is located belowground in Arctic ecosystems and plant roots are responsible for the uptake of the nutrients that constrain plant growth in these infertile ecosystems. Despite performing a crucial role connecting primary producers to the soil, roots are relatively understudied in the Arctic and their functional response to a rapidly warming and increasingly variable climate is unknown. Here, we assessed whether one growing season with elevated temperatures would have an impact on nutrient uptake and allocation by applying a warming technique that increased daily air temperatures by 3.2 °C. Destructive sampling was performed at the peak of the growing season to quantify biomass pools of carbon (C) and nitrogen (N), root traits, and uptake of a 15 N tracer ( 15 NH 4 + ) for the dominant plant species, Arctagrostis latifolia. We found that soil nutrient availability increased with short-term warming, but A. latifolia NH 4 + uptake remained unchanged. Fine-root length density and root biomass within the soil profile, however, were both reduced by warming. N allocation patterns across plant tissues were also altered by warming. NH 4 + uptake was best fit with a logistic model that captured the spatial relationship between roots and soil (NH 4 + uptake expressed per length fine root and NH 4 + availability expressed per unit soil volume) rather than a traditional Michaelis–Menten model. Our results indicate that short-term experimental warming can shift plant–soil interactions, suggesting that the tundra’s belowground response to elevated temperatures may be more dynamic than previously recognized.

15N tracer↗