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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Statistical properties of magnetic structures and energy dissipation during turbulent reconnection in the Earth's magnetotail

We present the first statistical study of magnetic structures and associated energy dissipation observed during a single period of turbulent magnetic reconnection, by using the in situ measurements of the Magnetospheric Multiscale mission in the Earth's magnetotail on 26 July 2017. The structures are selected by identifying a bipolar signature in the magnetic field and categorized as plasmoids or current sheets via an automated algorithm which examines current density and plasma flow. The size of the plasmoids forms a decaying exponential distribution ranging from subelectron up to ion scales. The presence of substantial number of current sheets is consistent with a physical picture of dynamic production and merging of plasmoids during turbulent reconnection. The magnetic structures are locations of significant energy dissipation via electric field parallel to the local magnetic field, while dissipation via perpendicular electric field dominates outside of the structures. Significant energy also returns from particles to fields.

Magnetotail↗
DOE OSTI2023

Multimodal Characterization of Crystal Structure and Formation in Rubrene Thin Films Reveals Erasure of Orientational Discontinuities

Abstract Multimodal multiscale characterization provide opportunities to study organic semiconducting thin films with multiple length scales, across multiple platforms, to elucidate crystallization mechanisms of the various microstructures that impact functionality. With polarized scanning transmission X‐ray and 4D‐scanning transmission electron microscopy, hybrid crystalline structures in rubrene thin films in which large crystalline domains surround a common nucleus and transition to a spherulite morphology at larger radii is observed. These high‐resolution techniques reveal how azimuthal orientational discontinuities at smaller radii are erased as spherulite morphology takes hold. In situ crystallization in the films with optical microscopy is also captured, discovering the importance of considering the initial temperature increase of a film during thermal annealing over the crystallization timescale. This kinetic information of the radial crystallization rate and of corresponding film heating kinetics is used to estimate the temperature at which the larger crystalline regions transition into a spherulite. By combining the results obtained from the different characterization modes, it is learned that thermal conditions can sensitively affect the crystallization of rubrene and other organic thin films. The observations suggest opportunities for more complex temperature‐dependent processing to maximize hybrid structures’ functionality in organic thin films and demonstrate that multimodal studies deepen the understanding of structure‐function dynamics.

Tan, Jenna A.↗
DOE OSTI2025

Ternary Potassium‐Bismuth‐Telluride Intermetallic Support Promotes Electrochemical Stability in Potassium Metal Anodes

We employed accumulative roll bonding to fabricate self‐standing metallurgical composite of in situ formed alkaline potassium‐bismuth‐telluride intermetallic K 2 (Bi 2/6 Te 3/6 Vac 1/6 ) embedded in potassium metal. This newly discovered thermodynamically stable potassiophilic crystal, termed “KBT”, is fcc antifluorite with K 2 Te archetype. Symmetric cells achieve 880 h of cycling at 0.5 mA cm −2 and 0.5 mAh cm −2 . Potassium metal battery (KMB) with Prussian blue (PB) cathode in carbonate electrolyte retains 80% capacity after 200 cycles at 1C. In ether‐based electrolyte with organic cathode, it achieves 80% retention after 900 cycles at 2C. Combined synchrotron X‐ray nano‐tomography, cryogenic‐focused ion beam microscopy (Cryo‐FIB) and sputter‐down X‐ray photoelectron spectroscopy (XPS) demonstrate uniform electrodeposits, versus baseline of potassium filaments intermixed with pores and coarse SEI. Binary K 3 Bi‐K and K 2 Te‐K intermetallic supports also provide improved electrochemical performance, albeit to lesser extent. Multiscale simulation provides insight into role of support structure in adatom energetics, film nucleation, early‐stage SEI morphology and interfacial stability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Dynamic Bubbling Balanced Proactive CO 2 Capture and Reduction on a Triple-Phase Interface Nanoporous Electrocatalyst

The formation and preservation of the active phase of the catalysts at the triple-phase interface during CO 2 capture and reduction is essential for improving the conversion efficiency of CO 2 electroreduction toward value-added chemicals and fuels under operational conditions. Designing such ideal catalysts that can mitigate parasitic hydrogen generation and prevent active phase degradation during the CO 2 reduction reaction (CO 2 RR), however, remains a significant challenge. Herein, we developed an interfacial engineering strategy to build a new SnO x catalyst by invoking multiscale approaches. This catalyst features a hierarchically nanoporous structure coated with an organic F-monolayer that modifies the triple-phase interface in aqueous electrolytes, substantially reducing competing hydrogen generation (less than 5%) and enhancing CO 2 RR selectivity (~90%). This rationally designed triple-phase interface overcomes the issue of limited CO 2 solubility in aqueous electrolytes via proactive CO 2 capture and reduction. Concurrently, we utilized pulsed square-wave potentials to dynamically recover the active phase for the CO 2 RR to regulate the production of C1 products such as formate and carbon monoxide (CO). This protocol ensures profoundly enhanced CO 2 RR selectivity (~90%) compared with constant potential (~70%) applied at -0.8 V (V vs RHE). We further achieved a mechanistic understanding of the CO 2 capture and reduction processes under pulsed square-wave potentials via in situ Raman spectroscopy, thereby observing the potential-dependent intensity of Raman vibrational modes of the active phase and CO 2 RR intermediates. Finally, this work will inspire material design strategies by leveraging triple-phase interface engineering for emerging electrochemical processes, as technology moves toward electrification and decarbonization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Observation of a super-tetrahedral cluster of acetonitrile-solvated dodecaborate dianion via dihydrogen bonding

We launched a combined negative ion photoelectron spectroscopy and multiscale theoretical investigation on the geometric and electronic structures of a series of acetonitrile-solvated dodecaborate clusters, i.e., B 12 H 12 2- ·nCH 3 CN (n = 1–4). The electron binding energies of B 12 H 12 2- ·nCH 3 CN are observed to increase with cluster size, suggesting their enhanced electronic stability. B3LYP-D3(BJ)/ma-def2-TZVP geometry optimizations indicate each acetonitrile molecule binds to B 12 H 12 2- via a threefold dihydrogen bond (DHB) B3–H3 ⋮⋮⋮ H3C–CN unit, in which three adjacent nucleophilic H atoms in B 12 H 12 2- interact with the three methyl hydrogens of acetonitrile. The structural evolution from n = 1 to 4 can be rationalized by the surface charge redistributions through the restrained electrostatic potential analysis. Notably, a super-tetrahedral cluster of B 12 H 12 2- solvated by four acetonitrile molecules with 12 DHBs is observed. The post-Hartree–Fock domain-based local pair natural orbital- coupled cluster singles, doubles, and perturbative triples [DLPNO-CCSD(T)] calculated vertical detachment energies agree well with the experimental measurements, confirming the identified isomers as the most stable ones. Furthermore, the nature and strength of the intermolecular interactions between B 12 H 12 2- and CH 3 CN are revealed by the quantum theory of atoms-in-molecules and the energy decomposition analysis. Ab initio molecular dynamics simulations are conducted at various temperatures to reveal the great kinetic and thermodynamic stabilities of the selected B 12 H 12 2- ·CH 3 CN cluster. The binding motif in B 12 H 12 2- ·CH 3 CN is largely retained for the whole halogenated series B 12 X 12 2- ·CH 3 CN (X = F–I). This study provides a molecular-level understanding of structural evolution for acetonitrile-solvated dodecaborate clusters and a fresh view by examining acetonitrile as a real hydrogen bond (HB) donor to form strong HB interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Insights into Supported Subnanometer Catalysts Exposed to CO via Machine-Learning-Enabled Multiscale Modeling

Subnanometer catalysts offer high noble metal utilization and superior performance for several reactions. However, understanding their structures and properties on an atomic scale under working conditions is challenging due to the large configurational space. Here, we introduce an efficient multiscale framework to predict their stability exposed to an adsorbate. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations, cluster expansion, machine learning, and structure optimization. The end-to-end machine-learning workflow guides DFT data generation and enables significant computational acceleration. We demonstrate the approach for CO-adsorbed Pdn (n = 1–55) clusters on CeO 2 (111). Simulation results reveal that CO can facilitate restructuring by stabilizing smaller planar structures and bilayer structures of specific intermediate sizes, consistent with experimental reports. Metal–support interactions, preferential CO adsorption, and metal nuclearity and structure control catalyst stability. As a result, the framework allows automatic discovery of stable catalyst structures and a systematic strategy to exploit properties in the subnanometer scale.

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

Selenium Migration and Local Structures in Cu‐Doped CdSeTe Solar Cells after Aging

Selenium grading plays a critical role in state-of-the-art Cadmium Telluride photovoltaic cells by enhancing long-wavelength absorption and extending minority carrier lifetimes —key to enabling the current performance record of 23.08%. However, very little is understood about selenium motion. In this study, a comprehensive, multimodal, and multiscale approach is employed to investigate Se migration and local structural changes in copper (Cu)-doped CdSeTe solar cells subjected to accelerated stress. X-ray fluorescence (XRF) microscopy shows unexpected levels of Se diffusion after 500 h under heat (75°C) and light (0.8 suns, 80 mW/cm 2 ), suggesting the coexistence of fast and slow diffusion channels even at low temperatures, with unexpectedly low activation energies (<0.85 eV). X-ray Absorption Near Edge Structure (XANES) analysis indicates a preferential migration of Se atoms to anionic lattice sites and a reduction in Se-Cl co-passivation at Te-terminated dislocation cores. Furthermore, these findings point to a reconfiguration of Se local environments and highlight the potential role of extended structural defects in enabling Se transport at low temperatures. Additionally, XANES results suggest that the presence of metallic Cu across the absorber layer may contribute to back-contact degradation and reduced hole density in both fresh and aged devices.

14 SOLAR ENERGY↗
DOE OSTI2023

Subtle Molecular Changes Largely Modulate Chiral Helical Assemblies of Achiral Conjugated Polymers by Tuning Solution-State Aggregation

Understanding the solution-state aggregate structure and the consequent hierarchical assembly of conjugated polymers is crucial for controlling multiscale morphologies during solid thin-film deposition and the resultant electronic properties. However, it remains challenging to comprehend detailed solution aggregate structures of conjugated polymers, let alone their chiral assembly due to the complex aggregation behavior. Herein, we present solution-state aggregate structures and their impact on hierarchical chiral helical assembly using an achiral diketopyrrolopyrrole-quaterthiophene (DPP-T4) copolymer and its two close structural analogues wherein the bithiophene is functionalized with methyl groups (DPP-T2M2) or fluorine atoms (DPP-T2F2). Combining in-depth small-angle X-ray scattering analysis with various microscopic solution imaging techniques, we find distinct aggregate in each DPP solution: (i) semicrystalline 1D fiber aggregates of DPP-T2F2 with a strongly bound internal structure, (ii) semicrystalline 1D fiber aggregates of DPP-T2M2 with a weakly bound internal structure, and (iii) highly crystalline 2D sheet aggregates of DPP-T4. These nanoscopic aggregates develop into lyotropic chiral helical liquid crystal (LC) mesophases at high solution concentrations. Intriguingly, the dimensionality of solution aggregates largely modulates hierarchical chiral helical pitches across nanoscopic to micrometer scales, with the more rigid 2D sheet aggregate of DPP-T4 creating much larger pitch length than the more flexible 1D fiber aggregates. Combining relatively small helical pitch with long-range order, the striped twist-bent mesophase of DPP-T2F2 composed of highly ordered, more rigid 1D fiber aggregate exhibits an anisotropic dissymmetry factor (g-factor) as high as 0.09. This study can be a prominent addition to our knowledge on a solution-state hierarchical assembly of conjugated polymers and, in particular, chiral helical assembly of achiral organic semiconductors that can catalyze an emerging field of chiral (opto)electronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Improving High Resolution Offshore Wind Resource Assessments and Forecasts using Observations in the MA/RI Lease Areas

The third Wind Forecast Improvement Project (WFIP3) sought to improve understanding of the physical phenomena in the atmosphere and ocean that dictate the structure and variability of wind and thermodynamic fields within the Marine Atmospheric Boundary Layer (MABL). WFIP3 conducted a comprehensive 18-month observational study over the Northeast U.S. outer continental shelf, a high use coastal zone, using a 3-D multiscale sensor array to highly resolve the temporal, vertical, and horizontal structure of the coupled atmospheric and oceanic boundary layers. Multiple land-based study sites adjacent to the coastal ocean observed surface meteorology and vertical profiles of atmospheric properties via passive infrared and microwave radiometers, active lidars and radars, and radiosondes. At sea, an array of surface flux buoys and two vertical profiling lidar buoys observed both atmospheric and oceanic properties, augmented by land-based oceanographic radar systems and routine ship-based surveys. Intensive observations of the MABL over the ocean was done from an air-sea interaction flux tower and extended deployments of a large barge platform. WFIP3 focused on mesoscale and sub-mesoscale flows -- including sea breezes, low-level jets, low-level clouds, and coastal storms -- and the ability of advanced numerical model parameterizations to represent them within fully coupled oceanic and atmospheric modeling systems and foundational weather forecast models. Numerous critical forecasting phenomena were observed, however the project was terminated prior to the completion of the field observational period and the analysis period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional models, the entropic loss of information leads to emergent physics which are dissipative, history-dependent, and stochastic. To machine learn coarse-grained dynamics from time-series observations of particle trajectories, we propose a framework using the metriplectic bracket formalism that preserves these properties by construction; most notably, the framework guarantees discrete notions of the first and second laws of thermodynamics, conservation of momentum, and a discrete fluctuation-dissipation balance crucial for capturing non-equilibrium statistics. We introduce the mathematical framework abstractly before specializing to a particle discretization. As labels are generally unavailable for entropic state variables, we introduce a novel self-supervised learning strategy to identify emergent structural variables. We validate the method on benchmark systems and demonstrate its utility on two challenging examples: (1) coarse-graining star polymers at challenging levels of coarse-graining while preserving non-equilibrium statistics, and (2) learning models from high-speed video of colloidal suspensions that capture coupling between local rearrangement events and emergent stochastic dynamics. We provide open-source implementations in both PyTorch and LAMMPS, enabling large-scale inference and extensibility to diverse particle-based systems.

Computational Engineering, Finance, and Science (c↗
DOE OSTI2025

Artificial Intelligence and Multiscale Modeling for Sustainable Biopolymers and Bioinspired Materials

Abstract Biopolymers and bioinspired materials contribute to the construction of intricate hierarchical structures that exhibit advanced properties. The remarkable toughness and damage tolerance of such multilevel materials are conferred through the hierarchical assembly of their multiscale (i.e., atomistic to macroscale) components and architectures. Here, the functionality and mechanisms of biopolymers and bio‐inspired materials at multilength scales are explored and summarized, focusing on biopolymer nanofibril configurations, biocompatible synthetic biopolymers, and bio‐inspired composites. Their modeling methods with theoretical basis at multiple lengths and time scales are reviewed for biopolymer applications. Additionally, the exploration of artificial intelligence‐powered methodologies is emphasized to realize improvements in these biopolymers from functionality, biodegradability, and sustainability to their characterization, fabrication process, and superior designs. Ultimately, a promising future for these versatile materials in the manufacturing of advanced materials across wider applications and greater lifecycle impacts is foreseen.

Wang, Xing Quan [Department of Mechanical Engineer↗
DOE OSTI2022

Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network

A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Investigating the Effect of Water on the Mechanical Properties of Cellulose from Multiscale Molecular Dynamics Simulations

Classical molecular dynamics (MD) simulations provide insight into the structure and physicochemical properties of materials with atomic resolution. However, the length and time scales accessible to atomistic MD are orders of magnitude smaller than many relevant processes such as the response of a bulk material to experimentally accessible strain rates, which presents challenges when comparing models to experimental measurements. Bottom-up coarse-graining provides a means for systematically mapping atomistic information to lower resolution models to increase the length and time scales achievable by simulation. Cellulose is an abundant carbohydrate biopolymer with applications to many fields of research, such as materials science and renewable energy, due to its desirable mechanical properties and viability for conversion into biofuel. The effect of moisture content on the Young's modulus of cellulose is of special interest due to its native environment often being in the hydrated secondary plant cell wall and the grinding energy requirements for biomass feedstock preprocessing. The current work investigates the effects of water solvent on the Young's modulus of cellulose calculated from coarse-grained MD mechanical stress simulations. The coarse-grained model was parametrized from atomistic MD calculations of cellulose-cellulose potentials of mean force using umbrella sampling techniques under vacuum and solvated conditions. The Young's moduli of the coarse-grained cellulose assemblies parametrized from cellulose in vacuum or solvated in water were computed via mechanical stress simulations to highlight the importance of capturing solvent interactions for modeling the mechanical behavior of cellulose.

BASIC BIOLOGICAL SCIENCES,RADIATION PROTECTION AND↗
DOE OSTI2022

Towards computational polar-topotronics: Multiscale neural-network quantum molecular dynamics simulations of polar vortex states in SrTiO3/PbTiO3 nanowires

Recent discoveries of polar topological structures ( e.g ., skyrmions and merons) in ferroelectric/paraelectric heterostructures have opened a new field of polar topotronics. However, how complex interplay of photoexcitation, electric field and mechanical strain controls these topological structures remains elusive. To address this challenge, we have developed a computational approach at the nexus of machine learning and first-principles simulations. Our multiscale neural-network quantum molecular dynamics molecular mechanics approach achieves orders-of-magnitude faster computation, while maintaining quantum-mechanical accuracy for atoms within the region of interest. This approach has enabled us to investigate the dynamics of vortex states formed in PbTiO 3 nanowires embedded in SrTiO 3 . We find topological switching of these vortex states to topologically trivial, uniformly polarized states using electric field and trivial domain-wall states using shear strain. These results, along with our earlier results on optical control of polar topology, suggest an exciting new avenue toward opto-electro-mechanical control of ultrafast, ultralow-power polar topotronic devices.

Linker, Thomas↗
DOE OSTI2021

Convergence of micro-geochemistry and micro-geomechanics towards understanding proppant shale rock interaction: A Caney shale case study in southern Oklahoma, USA

As a direct outcome of economic development coupled with an increase in population, global energy demand will continue to rise in the coming decades. Although renewable energy sources are increasingly investigated for optimal production, the immediate needs require focus on energy sources that are currently available and reliable, with a minimal environmental impact; the efficient exploration and production of unconventional hydrocarbon resources is bridging the energy needs and energy aspirations, during the current energy transition period. The main challenges are related to the accurate quantification of the critical rock properties that influence production, their heterogeneity and the multiscale driven physico-chemical nature of rock–fluid interactions. A key feature of shale reservoirs is their low permeability due to dominating nanoporosity of the clay-rich matrix. As a means of producing these reservoirs in a cost-effective manner, a prerequisite is creation of hydraulic fracture networks capable of the highest level of continued conductivity. Fracturing fluid chemical design, formation brine geochemical composition, and rock mineralogy all contribute to swelling-induced conductivity damage. The Caney Shale is an organic-rich, often calcareous mudrock. Many studies have examined the impact that clay has on different kinds of shale productivity but there is currently no data reported on the Caney Shale in relation to horizontal drilling; all reported data on the Caney Shale is on vertical wells which are shallow, compared to an emerging play that is at double the depth. Here, in this work we develop geochemical–geomechanical integration of rock properties at micro-and nanoscales that can provide insights into the potential proppant embedment and its mitigation. The novel methodology amalgamates the following: computed X-ray tomography, scanning electron microscopy, energy dispersive spectroscopy, micro-indentation, and Raman spectroscopy techniques. Here, our results show that due to the multiscale heterogeneity in the Caney Shale, these geochemical and structural properties translate into a variation in mechanical properties that will impact interaction between the proppant and the host shale rock.

03 NATURAL GAS↗
DOE OSTI2026

Hierarchical Chiral Self-Assembly of Nanocylinders Composed of Sequence-Defined Mesogenic Dimers

Chiral ensembles can arise through supramolecular curvature that resolves geometric frustrations in the packing of bent, achiral molecular or colloidal building blocks. Here, we leverage orthogonal protection−deprotection click chemistry to create sequence-defined mesogenic heterodimers exhibiting emergent chirality. We compare the hierarchical self-assembly of the synthesized asymmetric, achiral heterodimers, which differ only in the position of a methyl substituent. Both dimers form chiral spherulites composed of nanocylinders. However, the detailed arrangement of nanocylinders depends on the position of the methyl substituent and the crystallization conditions. Despite the chemical similarity, in one dimer, two crystalline forms are optically active. They form conglomerates of dextrorotatory and levorotatory spherulites. The other dimer forms more highly anisotropic spherulites that mask circular birefringence arising from the misorientation of nanocylinders, while mapping of nanocylinder directors reveals a sense at the spherulite surface. We propose that differences in nanocylinder arrangements may arise from changes in nanocylinder curvature and dimensions dictated by the methyl substituent position, inducing chirality. These results demonstrate multiscale hierarchical assembly relevant to dense systems of tubular structures and highlight the role of sequence and molecular design in directing the bottom-up hierarchical self-assembly and chirality of mesogenic systems.

Alkyls↗
DOE OSTI2025

High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning

Abstract The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high‐resolution (∼37 km 2 ) differentiable models (a type of hybrid model): one with implicit, unit‐hydrograph‐style routing and another with explicit Muskingum‐Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process‐based equations to provide a structural backbone, which were trained simultaneously (“end‐to‐end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash‐Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long‐standing modeling challenge. The Muskingum‐Cunge version further improved performance for basins >10,000 km 2 . Overall, our results show how neural‐network‐based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next‐generation NWM. We also provide a CONUS‐scale hydrologic data set for further evaluation and use.

Song, Yalan [Civil and Environmental Engineering T↗
DOE OSTI2024

Controlled patterning of crystalline domains by frontal polymerization

Materials with hierarchical architectures that combine soft and hard material domains with coalesced interfaces possess superior properties compared with their homogeneous counterparts. These architectures in synthetic materials have been achieved through deterministic manufacturing strategies such as 3D printing, which require an a priori design and active intervention throughout the process to achieve architectures spanning multiple length scales. Here we harness frontal polymerization spin mode dynamics to autonomously fabricate patterned crystalline domains in poly(cyclooctadiene) with multiscale organization. This rapid, dissipative processing method leads to the formation of amorphous and semi-crystalline domains emerging from the internal interfaces generated between the solid polymer and the propagating cure front. The size, spacing and arrangement of the domains are controlled by the interplay between the reaction kinetics, thermochemistry and boundary conditions. Small perturbations in the fabrication conditions reproducibly lead to remarkable changes in the patterned microstructure and the resulting strength, elastic modulus and toughness of the polymer. Furthermore, this ability to control mechanical properties and performance solely through the initial conditions and the mode of front propagation represents a marked advancement in the design and manufacturing of advanced multiscale materials. Drawing inspiration from biological systems in which structural complexity develops through dissipative reaction–diffusion processes, this study explores a transformative synthetic manufacturing strategy aimed at harnessing the principles underpinning morphogenic growth, unlocking new avenues for advanced materials design and fabrication. Synthetic coupled reaction-transport processes offer a versatile yet relatively underexplored method to manipulate the spatial attributes of synthetic materials10. Here we introduce an innovative manufacturing approach based on frontal ring-opening metathesis polymerization (FROMP) that draws parallels with morphogenic growth and development, enabling the formation of patterned microstructures within polymeric materials.

36 MATERIALS SCIENCE↗