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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Block-Structured Operator Inference for Coupled Multiphysics Model Reduction

This work presents a block-structured formulation of Operator Inference as a way to learn structured reduced-order models for multiphysics systems. The approach specifies the governing equation structure for each physics component and the structure of the coupling terms. Once the multiphysics structure is specified, the reduced-order model is learned from snapshot data following the nonintrusive Operator Inference methodology. In addition to preserving physical system structure, which in turn permits preservation of system properties such as stability and second-order structure, the block-structured approach has the advantages of reducing the overall dimensionality of the learning problem and admitting tailored regularization for each physics component. The numerical advantages of the block-structured formulation over a monolithic Operator Inference formulation are demonstrated for aeroelastic analysis, which couples aerodynamic and structural models. For the benchmark test case of the AGARD 445.6 wing, block-structured Operator Inference provides an average 20% online prediction speedup over monolithic Operator Inference across subsonic and supersonic flow conditions in both the stable and fluttering parameter regimes while preserving the accuracy achieved with monolithic Operator Inference.

42 ENGINEERING↗
DOE OSTI2026

Algebraic Multigrid with Filtering: An Efficient Preconditioner for Interior Point Methods in Large-Scale Contact Mechanics Optimization

Large-scale contact mechanics simulations are crucial in many engineering fields such as structural design and manufacturing. In the frictionless case, contact can be modeled by minimizing an energy functional; however, these problems are often nonlinear, nonconvex, and increasingly difficult to solve as mesh resolution increases. In this work, we employ a Newton-based interior-point (IP) filter line-search method, an effective approach for large-scale constrained optimization. While this method converges rapidly, each iteration requires solving a large saddle-point linear system that becomes ill-conditioned as the optimization process converges, largely due to IP treatment of the contact constraints. Such ill-conditioning can hinder solver scalability and increase iteration counts with mesh refinement. Here, to address this, we introduce a novel preconditioner, algebraic multigrid with filtering (AMGF), tailored to the Schur complement of the saddle-point system. Building on the classical AMG solver, commonly used for elasticity, we augment it with a specialized subspace correction that filters near null space components introduced by contact interface constraints. Through theoretical analysis and numerical experiments on a range of linear and nonlinear contact problems, we demonstrate that the proposed solver achieves mesh independent convergence and maintains robustness against the ill-conditioning that notoriously plagues IP methods. These results indicate that AMGF makes contact mechanics simulations more tractable and broadens the applicability of Newton-based IP methods in challenging engineering scenarios. More broadly, AMGF is well suited for problems, optimization or otherwise, where solver performance is limited by a low-dimensional subspace, such as those arising from localized constraints, interface conditions, or model heterogeneities. This makes the method widely applicable beyond contact mechanics and constrained optimization.

Mathematics and Computing↗
DOE OSTI2024

Towards interpretable Cryo-EM: disentangling latent spaces of molecular conformations

Molecules are essential building blocks of life and their different conformations (i.e., shapes) crucially determine the functional role that they play in living organisms. Cryogenic Electron Microscopy (cryo-EM) allows for acquisition of large image datasets of individual molecules. Recent advances in computational cryo-EM have made it possible to learn latent variable models of conformation landscapes. However, interpreting these latent spaces remains a challenge as their individual dimensions are often arbitrary. The key message of our work is that this interpretation challenge can be viewed as an Independent Component Analysis (ICA) problem where we seek models that have the property of identifiability. That means, they have an essentially unique solution, representing a conformational latent space that separates the different degrees of freedom a molecule is equipped with in nature. Thus, we aim to advance the computational field of cryo-EM beyond visualizations as we connect it with the theoretical framework of (nonlinear) ICA and discuss the need for identifiable models, improved metrics, and benchmarks. Moving forward, we propose future directions for enhancing the disentanglement of latent spaces in cryo-EM, refining evaluation metrics and exploring techniques that leverage physics-based decoders of biomolecular systems. Moreover, we discuss how future technological developments in time-resolved single particle imaging may enable the application of nonlinear ICA models that can discover the true conformation changes of molecules in nature. The pursuit of interpretable conformational latent spaces will empower researchers to unravel complex biological processes and facilitate targeted interventions. This has significant implications for drug discovery and structural biology more broadly. More generally, latent variable models are deployed widely across many scientific disciplines. Thus, the argument we present in this work has much broader applications in AI for science if we want to move from impressive nonlinear neural network models to mathematically grounded methods that can help us learn something new about nature.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Proximal Galerkin: A Structure-Preserving Finite Element Method for Pointwise Bound Constraints

The proximal Galerkin finite element method is a high-order, low iteration complexity, nonlinear numerical method that preserves the geometric and algebraic structure of pointwise bound constraints in infinite-dimensional function spaces. This paper introduces the proximal Galerkin method and applies it to solve free boundary problems, enforce discrete maximum principles, and develop a scalable, mesh-independent algorithm for optimal design with pointwise bound constraints. This paper also introduces the latent variable proximal point (LVPP) algorithm, from which the proximal Galerkin method derives. When analyzing the classical obstacle problem, we discover that the underlying variational inequality can be replaced by a sequence of second-order partial differential equations (PDEs) that are readily discretized and solved with, e.g., the proximal Galerkin method. Throughout this work, we arrive at several contributions that may be of independent interest. These include (1) a semilinear PDE we refer to as the entropic Poisson equation; (2) an algebraic/geometric connection between high-order positivity-preserving discretizations and certain infinite-dimensional Lie groups; and (3) a gradient-based, bound-preserving algorithm for two-field, density-based topology optimization. The complete proximal Galerkin methodology combines ideas from nonlinear programming, functional analysis, tropical algebra, and differential geometry and can potentially lead to new synergies among these areas as well as within variational and numerical analysis. Open-source implementations of our methods accompany this work to facilitate reproduction and broader adoption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Reorganization of Water at Aqueous Aluminum Chloride (AlCl 3 ) Interfaces: Vibrational Sum Frequency Generation and Molecular Dynamics Simulations

AlCl 3 hydration states and complexation are not well understood both in solutions and at the air–aqueous interface despite their potential significance in natural waters and their industrial and energy-related applications. Here, we investigated Al 3+ and Cl – ion behaviors in an AlCl 3 aqueous bulk solution and at the air–aqueous interface using interface-selective vibrational sum frequency generation (SFG), Raman and infrared spectroscopies, molecular dynamics (MD) simulation, as well as molecular-informed reduced modeling. Our reduced modeling reveals relatively long-range effects for Al 3+ as compared to monovalent ions such as Na + indicating that the interfacial depth of trivalent ions can be significantly larger than that of monovalent ions at the air–water interface. MD simulations reveal interfacial stratification and multiple layering of the ions. Compression of the Al 3+ and Cl – distributions with increasing concentrations from 0.5 to 2.5 m is also observed in the subsurface regions. Significant SSP- and PPP-polarized SFG OH spectral intensity increases are observed from 0.5 to 1.5 m and 0.5 to 2.5 m, respectively, indicative of interfacial depth increases and a change in average orientation above 1.5 m. Extensive evaluation of SFG spectra, Fresnel-corrected using several approaches, shows the same trends. The nonmonotonic trend points to a changing structure in surface and subsurface water orientation and hydrogen bonding environment generally consistent with the MD simulation of stratification and water orientation changes. Furthermore, solvent-shared ion pairing is implicated with MD simulation radial distribution analysis and consistent with infrared spectral identification of the hexaaqua aluminum ion in the solution phase. Spectral evidence of a strong Al 3+ hydration shell and the acidic behavior of the Al 3+ ions is obvious in the Raman and infrared spectra of the bulk solution. In conclusion, we show that the MD dipole potential is directly related to the MD second-order susceptibility of the interface, χ SFG–MD (2) , both of which correlate up to ∼35 Å with the spectral observations of increasing and then saturating intensities, suggesting that both ion stratification and interfacial depth determine the water orientations at an air–water interface of 1-3 electrolyte solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Challenges of standard halo models in constraining galaxy properties from cosmic infrared background anisotropies

The halo model, combined with halo occupation distribution (HOD) prescriptions, is widely used to interpret cosmic infrared background (CIB) anisotropies and extract physical information about star-forming galaxies and their connection to large-scale structures. Recent CIB-specific implementations of the halo model have adopted more physical parameterizations. However, the extent to which these models can reliably recover meaningful physical parameters remains uncertain. We assessed whether the current parameterization of CIB halo models is sufficient to recover astrophysical quantities, such as star formation efficiency, η(M h , z), and halo mass at which the peak of star formation efficiency occurs, M max , when fit to mock data. We also assessed whether discrepancies arise from assumptions about galaxy emission (the HOD ingredients) or from more fundamental components in the halo model, such as bias and matter clustering. We fit the M21 CIB HOD model, implemented within the halo model framework, to mock CIB power spectra and star formation rate density (SFRD) data generated from the SIDES-Uchuu simulation, and compared the best-fit parameters to the known simulation inputs. We then repeated the analysis using a simplified version of the simulation (SSU), explicitly designed to match the HOD assumptions. A detailed comparison of model and simulation outputs was carried out to trace the origin of observed discrepancies. While the M21 HOD model provides a good fit to the mock data, it failed to recover the intrinsic parameters accurately, particularly the halo mass at which star formation efficiency peaks. This mismatch persists even when fitting data generated with the same model assumptions. We find strong agreement (within 5%) in the emission-related components (SFRD, emissivity), but observe a scale- and redshift-dependent offset exceeding 20% in the two-halo term of the CIB power spectrum. This likely arises from limitations in the treatment of halo bias and matter clustering within the linear approximation. Additionally, incorporating scatter in the SFR–halo mass relation and the spectral energy distribution (SED) templates significantly affects the shot noise (∼50%), but has only a modest impact (less than 10%) on the clustered component. These results suggest that recovering physical parameters from CIB clustering requires improvements to the cosmological ingredients of the halo model framework, such as adopting scale-dependent halo bias and nonlinear matter power spectra in addition to careful modeling of emission physics.

cosmic background radiation↗
DOE OSTI2024

Single crystal growth, chemical defects, magnetic and transport properties of antiferromagnetic topological insulators ( Ge 1 – δ – x Mn x ) 2 Bi 2 Te 5 ( x ≤ 0.47 , 0.11 ≤ δ ≤ 0.20 )

Magnetic topological insulators provide a platform for emergent phenomena arising from the interplay between magnetism and band topology. Here we report the single crystal growth, crystal structure, magnetic and transport properties, as well as the neutron scattering studies of topological insulator series (Ge 1–δ–x Mn x ) 2 ⁢Bi 2 ⁢Te 5 (x ≤ 0.47, 0.11 ≤ δ ≤ 0.20). Upon doping up to x = 0.47, the lattice parameter c decreases by 0.8%, while the lattice parameter a remains nearly unchanged. Significant Ge vacancies and Ge/Bi site mixing are revealed via elemental analysis as well as refinements of the neutron and x-ray diffraction data, resulting in holes dominating the charge transport. At x = 0.47, below 10.8 K, a bilayer A-type antiferromagnetic ordered state emerges, featuring an ordered moment of 3.0(3) μ B /Mn at 5 K, with the c axis as the easy axis. Magnetization data unveils a much stronger effective interlayer antiferromagnetic exchange interaction and a much smaller uniaxial anisotropy compared to MnBi 2 ⁢Te 4 . We attribute the former to the shorter nearest-neighbor Mn-Mn interlayer superexchange path and the latter to the smaller ligand-field splitting in (Ge 1–δ–x ⁢Mn x ) 2 ⁢Bi 2 ⁢Te 5 . Finally, our study demonstrates that this series of materials holds promise for the investigation of the layer Hall effect and quantum metric nonlinear Hall effect.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Thermal Reservoir Networks for Modularly Expandable Thermal Microgrids

The Department of Defense (DoD) faces the substantial challenge of cost-effectively retrofitting one to two installations per month, each comprising approximately 1,000 buildings, to improve resilience, reduce energy consumption, and enhance energy supply security. Achieving these objectives requires optimal system selection and effective risk mitigation during system integration. To address this need, we introduce Platform-Based Design (PBD), a structured, hierarchical methodology adapted from other industrial sectors to the domain of energy system retrofits. We demonstrate the effectiveness of PBD through a techno-economic feasibility study comparing geothermal-coupled thermal energy networks (TENs) with conventional energy systems for heating, cooling, and powering 17 buildings at Joint Base Andrews (JBA) in Maryland. Our analysis illustrates that the PBD approach enables rigorous, data-driven, sequential decision making, resulting in a family of Pareto-optimal systems, among which the TEN emerged as the most promising solution. The selected TEN design integrates geothermal borefields, heat recovery heat pumps, photovoltaic (PV) arrays, and battery storage. Compared to the baseline system – gas heating combined with air-source chillers – the proposed TEN reduces annual imported energy by 74% and peak electricity demand by 45%, achieves a levelized cost of energy of $\$0.210$/kWh, and substantially enhances resilience. Life-cycle costs increase by approximately 6%, and initial investment costs are about 2.5 times higher than the baseline. However, if central plant infrastructure, district loops, and utility-scale PV and battery systems are privately funded and operated, the initial investment would fall below the baseline system cost. Critical to achieving these significant performance improvements were detailed nonlinear dynamic simulations coupling geothermal heat transfer, energy system operation, and realistic feedback control logic. These simulations identified essential design modifications and control strategy refinements that substantially reduced energy use, peak demand, and compressor shortcycling, thereby improving durability and reliability—issues that would have been significantly more expensive to resolve during operation. Additionally, the verification step highlighted sensitivities to key design parameters that could reduce initial investment by approximately $\$2$ million and reduce annual life-cycle costs more than $\$300,000$. We recommend adopting the PBD methodology for future feasibility studies and TEN pilot projects to gain valuable operational experience. Furthermore, we recommend that DoD invest in transferring and scaling the PBD methodology to other installations. This entails developing standardized computational frameworks and component libraries as well as training industry in conducting PBD. Such investments would enable rapid, robust, reliable, and cost-effective retrofits, supporting DoD’s ambitious energy system modernization goals.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Analysis of the linear and nonlinear stability of Alfven eigenmodes and fish-bones in JET DT discharges: mode identification and shear flows generation

The plasma in future nuclear fusion reactors will be heated by neutral beam injectors (NBIs) and high frequency electromagnetic waves as well as fusion born alpha particles. Energetic particles (EPs), with energies up to two orders of magnitude larger than the thermal plasma, can trigger EP driven modes and induce harmful EP losses, reducing the plasma heating efficiency and the economical viability of the reactor. The present study is dedicated to analyze the Alfven Eigenmode (AE) activity in JET D–T discharges, the closest experiment to reactor-like operation performed until now. There, EP driven modes are induced by the combined effect of tangential NBIs and ion cyclotron resonance heating (ICRH) driven EP. Linear and nonlinear simulations are performed with the gyro-fluid FAR3d code to analyze the AE activity observed in the discharge 99896. The linear simulations reproduce the unstable n = 3 to 5 toroidal AEs (TAE) at the inner plasma region observed in the experiment, triggered by highly energetic passing deuterium populations injected by the tangential NBIs, further accelerated by the effect of the ICRH up to 1 MeV. In addition, fish-bones triggered by energetic trapped hydrogen induced by the ICRH are also reproduced. On the other hand, the alpha particles density is too small to destabilize AEs in the experiment. Nonetheless, increasing artificially the alpha density by one order of magnitude, an n = 1 beta induced AE can be destabilized in the inner plasma region. Nonlinear simulations indicate the generation of zonal structures during the AE/fish-bone saturation phase. TAE and fish-bones causes a rather weak increase of the passing D and trapped H EP (around 2%), respectively. Shear flows and zonal currents are generated during the saturation of TAE and fish-bones. Nonlinear simulations performed for D–T and pure deuterium thermal plasma indicate AE/fish-bone activity is weaker and shear flows are less intense in the pure deuterium case, trends consistent with the experimental observations that also indicates a deterioration of the thermal plasma confinement. Therefore, both numerical studies and experimental evidence indicate the generation of shear flows by AE/fish-bones could be connected with an improvement of the thermal plasma confinement.

AE↗
DOE OSTI2026

Effects of external magnetic fields on the multi-mode Rayleigh–Taylor instability

The magneto-Rayleigh–Taylor instability (mRTI) is a key process in inertial confinement fusion and is thought to be widespread in the interstellar medium, where it can concentrate plasma into discrete structures. We present resistive MHD simulations of the nonlinear evolution of multi-mode mRTI in both two and three dimensions, examining the effects of uniform external magnetic fields oriented either parallel or perpendicular to the initial interface. In both 2D and 3D, weak parallel fields enhance mixing-zone growth, whereas stronger fields suppress it. For perpendicular fields, growth is initially inhibited but becomes enhanced at later times. These behaviors arise from magnetic tension, which modifies flow anisotropy, buoyancy, drag, and vortex dynamics. The interplay of these mechanisms governs the distinct ways in which magnetic fields influence mRTI evolution.

Flow instabilities↗
DOE OSTI2025

Mechanical Roles of Polysaccharide Assembly and Interactions in Plant Cell Walls

Plants synthesize polysaccharide-based primary cell walls that possess unique microstructures and mechanical properties to accommodate plant growth and provide protection. Here, it remains challenging to assess the role of polysaccharide organization and interactions in the mechanical behavior of primary cell walls owing to their complex microstructure and highly nonlinear mechanical responses. Employing a coarse-grained molecular dynamics model developed for onion epidermal walls, this work explores the conditions under which polysaccharide assembly and interactions might play a significant role in primary cell wall mechanics. Cellulose–cellulose adhesion plays a dominant role in the wall load-bearing capacity, but when cellulose–cellulose adhesion was disrupted computationally, cellulose–xyloglucan adhesion could influence the wall load-bearing capacity. Contrary to the common concept that xyloglucans mechanically tether well-separated cellulose microfibrils, xyloglucans functioned in this case as interfibrillar adhesives capable of transmitting tensile forces between cellulose microfibrils. Our findings may inform design criteria of new materials inspired by plant cell walls.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

New systems in MOOSE

The Multiphysics Object-Oriented Simulation Environment (MOOSE) serves as a common library of classes between applications developed for advanced reactor analysis, fusion device engineering, spent fuel cask analysis, geochemistry studies, among other fields. These applications drive the development of the framework to meet their needs. Systems in MOOSE group capabilities that share a common purpose and generally common code. They can be leveraged by all downstream applications, providing extensive code re-use and shared maintenance. They facilitate the discovery by new users of the classes meeting at least partially their needs, and offer the same opportunities for customization as other systems. The addition of a new system to MOOSE opens new ways of solving or discretizing nonlinear problems, of performing distributed postprocessing, and a plethora of other needs. While new systems can be introduced in downstream applications rather than at the framework level, the framework team monitors common needs across the community and often triggers their addition. Documentation, training material, development needs can be centralized, limiting duplicated work across the community. The last three years have seen a large expansion in the capabilities of MOOSE. The supporting role of the framework in the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has created numerous feature requests to support neutronics, thermal hydraulics, computational fluid dynamics and thermo-mechanics simulations in the Griffin, SAM, Pronghorn and Bison applications respectively. Similarly, laboratory-directed research and development (LDRD) projects in additive manufacturing, high-Reynolds flow simulations, structure optimization also necessitate an expansion of the framework capabilities. This summary reports on the new systems created in MOOSE, their design, their capabilities and some of the relevant interfaces.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

A Flexible Quasi-Static Mooring Design Optimization Method for Floating Structures

This paper presents a flexible and efficient design method for optimizing the mooring systems of floating structures. Mooring system optimization is challenging because of the strong nonlinearity of mooring system behavior and the many technical constraints that must be satisfied. Furthermore, different mooring configurations can have very different design spaces. While some successful examples of mooring design optimization exist in the literature, developing an optimization approach that can work across various mooring design problems is a larger challenge. We present such a method based on a flexible parameterization that allows a wide variety of mooring designs to be described by a list of variables, a quasi-static mooring model that provides efficient evaluation of a mooring design without directly considering mooring system dynamics, and an optimization framework that generates, evaluates, and adjusts the mooring design while considering user-specified constraints such as offset limits, strength safety factors, and seabed contact limits. We demonstrate the design optimization framework on four mooring design problems, each for a different type of mooring system. We compare the use of different design modes to simplify the optimization problem, showing that they can reduce the computation time by up to 75%. We also compare different optimization algorithms and find that the resulting computational speed can vary by up to 51 times. We perform a sensitivity study on one design and find that the local sensitivity of anchoring radius to water depth has a positive correlation of 0.29, but the global sensitivity shows large nonlinearities. Lastly, we perform a coupled dynamic analysis on one of the optimized designs and find that the predicted mean platform motions and mooring line tensions are within 1% of dynamic results and the extreme motions and tensions are within 14%. Lastly, we show that a DEA-Chain-Polyester mooring configuration is cost-optimal for the given design problem of the demonstrations, which aligns with general industry practice.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Probing coherent electronic superpositions of singly and doubly excited states of krypton with extreme-ultraviolet four-wave-mixing spectroscopy

Radiative nonlinear four-wave mixing can monitor the evolution of electronic wave packets, providing access to lifetimes and quantifying the light-induced couplings between excited states. In this article, we report the observation of quantum beats in an autoionizing electronic wave packet in krypton, probed using this technique. Analysis of the signal reveals that these beats originate from the contribution of previously unassigned, doubly excited states interacting with singly excited ones. We introduce a minimal theoretical model, based on multichannel quantum-defect theory, which quantitatively reproduces both the wave-packet dynamics and the static spectrum. This work combines a versatile, noncommensurate XUV-IR-based experimental scheme with a tractable model, establishing a powerful approach for the metrology and control of complex, correlated electronic states.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Explaining drivers of housing prices with nonlinear hedonic regressions

Housing markets play a critical role in shaping the spatial and demographic evolution of urban areas. Simulating housing price dynamics can enhance projections of future urban development outcomes. However, traditional hedonic regressions for housing prices, which neglect nonlinear interactions among explanatory variables, often exhibit limited predictive performance. While machine learning (ML) methods can provide a more flexible representation of the relationships between predictors, they are often regarded as “black boxes” due to their complexity and lack of transparency. Interpretable ML techniques provide a promising route by combining the flexibility of ML methods with approaches to analyze the relationships between inputs and outputs. In this study, we employ interpretable ML to analyze the patterns driving the housing market in Baltimore, Maryland, USA. We train an Artificial Neural Network (ANN) to predict Baltimore housing prices based on structural characteristics (e.g., home size, number of stories) and locational attributes (e.g., distance to the city center). We then conduct sensitivity and Partial Dependence Plot (PDP) analyses to interpret the fitted ANN model. We find that the ML model achieves higher predictive accuracy and explains 16 % more of housing price variance than a traditional linear regression model. The interpretable ML model also reveals more nuanced and realistic nonlinear relationships between housing sales price and predictors as well as interactive effects underlying Baltimore home price dynamics. For instance, while the linear model indicates a steady housing price increase over time, our interpretable ML model detects a post-2008 decline, with smaller properties experiencing the sharpest drop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Spatio-spectral optical fission in time-varying subwavelength layers

Transparent conducting oxides are highly doped semiconductors that exhibit favourable optical features compared with metals, including reduced material losses, tuneable electronic and optical properties, and enhanced damage thresholds. Recently, the photonic community has renewed its attention towards these materials, recognizing their remarkable nonlinear optical properties in the near-infrared spectrum. The exceptionally large and ultrafast change in the refractive index, which can be optically induced in these compounds, extends beyond the boundaries of conventional perturbative analysis and makes this class of materials the closest approximation to a time-varying system. Here we report the spatio-spectral fission of an ultrafast pulse trespassing a thin film of aluminium zinc oxide with a non-stationary refractive index. By applying phase conservation to this time-varying layer, our model can account for both space and time refraction and explain, in quantitative terms, the spatial separation of both spectrum and energy. Our findings represent an example of extreme nonlinear phenomena on subwavelength propagation distances, which provides new insights into transparent conducting oxides’ transient optical properties. This can be critical for the ongoing research on photonic time crystals, on-chip generation of non-classical states of light, integrated optical neural networks, ultrafast beam steering and frequency-division multiplexing.

integrated optics↗
DOE OSTI2025

Peculiar Magnetic and Magneto-Transport Properties in a Noncentrosymmetric Self-Intercalated van der Waals Ferromagnet Cr 5 Te 8

Trigonal Cr 5 Te 8 , a self-intercalated van der Waals ferromagnet with an out-of-plane magnetic anisotropy, has long been known to crystallize in a centrosymmetric structure. However, optical second harmonic generation experiments, together with comprehensive structural analysis, indicate that this compound rather adopts a noncentrosymmetric structure. Lorentz transmission electron microscopy reveals the presence of Néel-type skyrmions, consistent with its noncentrosymmetric structure. A large anomalous Hall conductivity of 102 Ω –1 cm –1 at low temperature stems from intrinsic origin, which is larger than any previously reported values in the bulk Cr–Te system. Notably, spontaneous topological Hall resistivity arising from the skyrmionic phase has been observed. Here, our findings not only elucidate the unique magnetic and magneto-transport properties of noncentrosymmetric trigonal Cr 5 Te 8 , but also open new avenues for investigating the effects of broken inversion symmetry on material properties and their potential applications.

crystal structure↗
DOE OSTI2026

Crystal Structures, Optical Behavior, and Magnetic Properties in Hydrated Lanthanide Iron Sulfates

Single crystals of LnFe(SO 4 ) 3 (H 2 O) 2 (Ln = La, Ce, Pr, Nd, Sm, Eu, Gd, Dy, Ho, Er, Tm; compounds 1–11) and LnFe(SO 4 ) 3 (H 2 O) (Ln = Tm, Yb, Lu; compounds 12–14) were synthesized under hydrothermal conditions. Single-crystal X-ray diffraction (SCXRD) analysis revealed that the dihydrated compounds (1–11) crystallize in centrosymmetric (CS) structures, with the lanthanide ions adopting eight-coordinate geometries. In contrast, the monohydrated compounds (12–14) exhibit noncentrosymmetric (NCS) structures, where the lanthanide ions are seven-coordinated. Vibrating sample magnetometry (VSM) confirmed that compounds 2, 4, and 7–11 are paramagnetic below 400 K, with compound 8 displaying the highest magnetic susceptibility. Compounds 1, 5, 6, 13, and 14 show a sharp increase in magnetic susceptibility at Néel temperatures ( T N ) of approximately 72, 76, 70, 58, and 56 K, respectively, indicating antiferromagnetic ordering. High-temperature magnetic susceptibility measurements further support the presence of antiferromagnetic transitions in these compounds. Second harmonic generation (SHG) measurements showed that the noncentrosymmetric Yb (compound 13) and Lu (compound 14) compounds exhibit SHG intensities of 0.3× and 1.6× that of potassium dihydrogen phosphate (KDP), respectively.

anions↗