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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

U-net architected deep material network training with microstructure local field information

The Deep Material Network (DMN) has recently emerged as a powerful reduced-order modeling framework for simulating the mechanical response of heterogeneous materials such as composites. Unlike most data-driven approaches that directly learn a material’s response under prescribed loading, the DMN acts as a homogenization operator, learning the kinematic constraints and mechanical interactions of the underlying microstructure. However, traditional DMN training relies exclusively on homogenized effective properties derived from Direct Numerical Simulations (DNS), discarding the rich local field data that govern microstructural interactions. In this work, we extend the DMN framework to incorporate such local field information into the offline training process. Utilizing a U-Net architecture, we augment the DMN training objective to include the first and second statistical moments of the local stress fields obtained from linear DNS. This ensures that the learned network topology not only fits the effective stiffness but also accurately reflects the internal local stress and strain partitioning of the microstructure. The results confirm that supervising the localization process during training yields a superior surrogate model, reducing local prediction errors by an order of magnitude and significantly improving generalization to unseen nonlinear constitutive behaviors compared to traditional DMNs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cambered Bipyridyl Ligand with Extended Aryl System Enables Electrochemical Reduction of Carbon Dioxide and Bicarbonate by Mn(bpy)(CO) 3 Br-type Catalyst Immobilized on Carbon Nanotubes

Heterogeneous materials containing molecular catalytic sites show promise for electrocatalytic reduction of CO 2 to energy-enriched carbon products. Interactions between the catalyst and the heterogeneous support increasingly are recognized as important in governing product selectivity and rate. Recent work on Mn(R-bpy)(CO) 3 Br type catalysts immobilized on multiwalled carbon nanotubes (MWCNT) demonstrated control of electrocatalytic behavior with steric modification of the molecular catalyst. Phenyl groups installed in the 4,4' positions of the bipyridine ligand (ph-bpy) maximized performance through π–π interactions with the MWCNT support. Herein we report the outcome of extending the ligand π system with Mn(nap-bpy)(CO) 3 Br (nap-bpy = 4,4'-di(naphthalen-1-yl)-2,2'-bipyridine) and Mn(pyr-bpy)(CO) 3 Br (pyr-bpy = 4,4'-di(pyren-1-yl)-2,2'-bipyridine) immobilized on MWCNT. We demonstrate exceptional electrocatalysis with Mn(nap-bpy)(CO) 3 Br/MWCNT (FE CO > 92%; J CO = 16.5 mA/cm 2 ) and find that this catalyst electrochemically reduces bicarbonate in the absence of deliberately added CO 2 at a remarkable overall selectivity of >80% for carbon products (FE HCOO – = 52% and FE CO = 29%). We show diminishing returns to simply adding aromatic character to the bipyridyl ligand with Mn(pyr-bpy)(CO) 3 Br/MWCNT and observe a unique cambering of the Mn(nap-bpy)(CO) 3 Br bipyridyl ligand that we believe enables selective catalysis. Mechanistic studies were carried out on Mn(nap-bpy)(CO) 3 Br/MWCNT using a novel thin-film infrared spectroelectrochemical (IR-SEC) technique. These experiments observe the immobilized Mn(nap-bpy)(CO) 3 Br undergo single electron reduction to a Mn-centered radical that binds CO 2 in a reduction-coupled process.

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

Adaptive Interface-PINNs (AdaI-PINNs) for transient diffusion: Applications to forward and inverse problems in heterogeneous media

We model transient diffusion in heterogeneous materials using a novel physics-informed neural networks framework (PINNs) termed Adaptive interface physics-informed neural networks or AdaI-PINNs (Roy et al. arXiv preprint arXiv:2406.04626, 2024). AdaI-PINNs utilize different activation functions with trainable slopes tailored to each material region within the computational domain, allowing for a fully automated and adaptive PINNs approach to model interface problems with strongly and weakly discontinuous solutions. To enhance its performance in highly heterogeneous transient diffusion systems, we prescribe a suite of robust practices, including appropriate non-dimensionalization of equations, a biased sampling method, Glorot initialization, and the hard enforcement of boundary and initial conditions. Here we evaluate the efficacy of the proposed method on several benchmark forward and inverse problems. Comparative studies on one-dimensional and two-dimensional benchmark problems reveal that the modified AdaI-PINNs outperform its unmodified counterpart, achieving root-mean-square errors that are at least two orders of magnitude better in forward problems. For inverse problems, the maximum errors in the approximated diffusion coefficients by modified AdaI-PINNs are four orders of magnitude better than those of the unmodified version. Additionally, modified AdaI-PINNs demonstrate improved stability in problems with large material mismatches.

42 ENGINEERING↗
DOE OSTI2024

Role of Metal–Organic Framework Topology on Thermodynamics of Polyoxometalate Encapsulation

Polyoxometalates (POMs) are discrete anionic clusters whose rich redox properties, strong Brønsted acidity, and high availability of active sites poise them as potent catalysts for oxidation reactions. Here, metal–organic frameworks (MOFs) have emerged as tunable, porous platforms to immobilize POMs, thus increasing their solution stability and catalytic activity. While POM@MOF composite materials have been widely used for a variety of applications, little is known about the thermodynamics of the encapsulation process. Here, we utilize an up-and-coming technique in the field of heterogeneous materials, isothermal titration calorimetry (ITC), to obtain full thermodynamic profiles (ΔH, ΔS, ΔG, K a ) of POM binding. Six different 8-connected hexanuclear Zr-MOFs were investigated to determine the impact of MOF topology (csq, scu, the) on POM encapsulation thermodynamics.

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

Osmotic control of the spacing of parallel shear cracks in shale growing subcritically in geologic past

The geological genesis of natural cracks in sedimentary rocks such as shale is a problem that needs to be understood to improve the technology of hydraulic fracturing as well as deep sequestration of harmful fluids. Why are the vertical natural cracks roughly parallel and equidistant, and why is the spacing roughly 10 cm rather than 1 cm or 100 cm? Fracture mechanics of critical cracks cannot answer this question. Neither can the material heterogeneity. The growth of critical parallel cracks is impossible because the relative crack face displacements would immediately localize into one crack, leading to an earthquake. The cracks must have formed, on the tectonic time scale, by a slow growth of subcritical shear cracks governed by the Charles-Evans law. The idea advanced here is that what controls the crack spacing is the balance between the reduction, due to shear dilatancy, of the concentration of ions such as Na + and Cl - in each fracture process zone (PFZ), which decelerates the cracks, and the restoration of ion concentration by diffusion of ions from the space between the cracks into the FPZ. This diffusion of water is driven mainly by the osmotic pressure gradient, which offsets the deceleration and depends strongly on the crack spacing. A simple analytical solution of the steady state is rendered possible by approximating the ion concentration profiles between adjacent cracks by parabolic arcs. Applying this theory to Woodford shale yields the approximate crack spacing of 10 cm, which is realistic. Furthermore, the stability of unlimited parallel mode II frictional crack growth is proven by examining the second variation of the free energy. Water concentration drop in the FPZ due to shear dilatancy and its restoration by water diffusion from the inter-crack space have similar effect, although probably much weaker.

42 ENGINEERING↗
DOE OSTI2025

Supramolecular Support of Cuprophilic Network Bonding in 2-D Copper n -Alkanethiolates

Here, the development of heterogeneous materials, catalysts, and semiconductors is often reliant on precise control of self-assembly and crystal packing. Many new materials are initially synthesized as microcrystalline powders, making them incompatible with typical methods of structure determination, such as single-crystal X-ray diffraction. This resultant lack of structural information has made thorough investigation into the effect of metal substitution on crystal structure in metal-organic chalcogenolates (MOChas) challenging. Here, we use small molecule serial femtosecond crystallography (smSFX) to present the structures of four copper n-alkanethiolates: CuSC4, CuSC5, CuSC6, and CuSC7. Divergent patterns of alkyl chain packing are identified from microcrystalline powders via smSFX. An odd-even effect in crystal packing has been identified and attributed to different orientations of symmetry elements in the even- and odd-numbered chains. This results in minute changes in the azimuthal organization of the even-numbered chains and the network of cuprophilic interactions. Additionally, we present a synthesis of crystalline gold n-alkanethiolates to provide the first comparison between three d 10 coinage metals (Cu, Ag, and Au) and their resultant n-alkanethiolates.

Willson, Maggie C. [Univ. of Connecticut, Storrs, ↗
DOE OSTI2024

A Titanium-Based Metal–Organic Framework For Tandem Metallaphotocatalysis

Metal–organic frameworks (MOFs) have garnered substantial attention for their unique properties, such as high porosity and tunable structures, making them versatile for various applications. This paper constructs photoactive titanium–organic frameworks by combining Ti(IV) clusters and a bipyridine linker. The MOF is synthesized in situ through imine condensation, resulting in NU-2300. Subsequent ex situ nickel salt complexation results in NU-2300-Ni, which is then used for light-mediated carbon–heteroatom cross-couplings. The photophysical properties of the metallaphotocatalyst were investigated by UV-vis and EPR analysis, and both the Ti cluster and the bipyridine linker were found to contribute to successful catalysis, making it a tandem catalyst. The heterogeneous material retained its performance through five cycles of thioetherification. As a result, this work contributes not only to MOF synthetic strategies, but also to expanding MOF applications as recyclable, tandem metallaphotocatalysts.

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

Surface-Controlled TiO 2 Nanocrystals with Catalytically Active Single-Site Co Incorporation for the Oxygen Evolution Reaction

The design of advanced electrocatalysts is often hindered by uncertainties in identifying and controlling the active surfaces and catalytic centers within heterogeneous materials. Here we present the synthesis of single-site Co catalysts, substitutionally doped into surface-controlled TiO 2 anatase nanocrystals, aimed at enhancing the oxygen evolution reaction (OER). Grand canonical quantum mechanics calculations reveal that the kinetics of the OER, following an adsorbate evolution mechanism, is markedly influenced by the coordination environment of Co. The simulations suggest significantly higher turnover frequencies when Co is doped into the (001) surface of TiO 2 compared to the (101) surface. Consistent with the computational findings, experimental results show that Co-doped TiO 2 (Co-TiO 2 ) nanoplates with selectively exposed {001} surfaces exhibit enhanced current densities and turnover frequencies compared to Co-TiO 2 nanobipyramids with {101} surfaces. This study highlights the synergy between theoretical calculations and precision synthesis in the development of more effective catalysts.

25 ENERGY STORAGE↗
DOE OSTI2025

Continuous flow photochemical synthesis of metal–ceramic composite microparticles

We developed a UV-assisted microfluidic system to synthesize FeCrAl–Al 2 O 3 composite microparticles for additive manufacturing. Here, the system ensures precise particle size, morphology, and elemental distribution control. Increasing the dispensing pressure while keeping the oil flow rate constant resulted in larger microparticles. Laser fusing revealed FeCrAl and Al 2 O 3 phases, showing their potential as feedstocks for 3D printed heterogeneous materials such as FeCrAl/Al 2 O 3 bi-phase materials.

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

Phase field dislocation dynamics formulation coupled with Fourier based micromechanics solver and its application to grain boundary–dislocation interactions

A new phase field dislocation dynamics (PFDD) formulation for homogeneous and heterogeneous materials is presented, which couples micromechanical solvers and the time-dependent Ginzburg–Landau equation. The strain fields are obtained from the micromechanical solver by solving the Lippmann–Schwinger equation and then used to define energy terms to model the evolution of the dislocations. Grain boundary (GB)–dislocation interactions are studied using the coupled PFDD formulation and by describing GBs as inclusions. GB energy and stiffness tensors are computed from molecular statics simulations, and a newly proposed lattice energy term that is dependent on the GB energy is considered in the calculations. Interaction of a screw dislocation with minimum energy and metastable states of low and high angle ⟨110⟩ symmetric tilt grain boundaries are studied. We show good agreement between predictions from our PFDD formulation and molecular dynamics simulations of grain boundary–dislocation interactions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Driving macro-scale transformations in three-dimensional-printed biopolymers through controlled induction of molecular anisotropy at the nanoscale

Motivated by the need to harness the properties of renewable and biodegradable polymers for the design and manufacturing of multi-scale structures with complex geometries, we have employed our additive manufacturing platform that leverages molecular self-assembly for the production of metre-scale structures characterized by complex geometries and heterogeneous material composition. As a precursor material, we used chitosan, a chemically modified form of chitin, an abundant and sustainable structural polysaccharide. We demonstrate the ability to control concentration-dependent crystallization as well as the induction of the preferred orientation of the polymer chains through the combination of extrusion-based robotic fabrication and directional toolpathing. Anisotropy is demonstrated and assessed through high-resolution micro-X-ray diffraction in conjunction with finite element simulations. Using this approach, we can leverage controlled and user-defined small-scale propagation of residual stresses to induce large-scale folding of the resulting structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

PyCMG-based Simulation of Volumetric Concrete Microstructure

Concrete is a complex, heterogeneous material with a microstructure composed of aggregates, cement paste, and pores spanning multiple length scales. Understanding this microstructure is critical for advancing the performance, durability, and modeling of concrete-based systems. While experimental imaging such as X-ray computed tomography (XCT) provides valuable insights, generating large datasets with detailed ground truth annotations is both costly and labor-intensive due to challenges in segmenting similar phases, such as aggregates and cement paste, that often share similar attenuation properties. To address this, we developed a pipeline to simulate realistic 3D concrete microstructures using the open-source Python package PyCMG. This simulation effort focuses on generating high-fidelity, annotated microstructures that can serve as training or benchmarking datasets for image analysis, segmentation algorithms, and machine learning models, particularly in scenarios where experimental data is scarce.

Ziabari, Amir [Oak Ridge National Laboratory; ORNL↗
DOE OSTI2025

Model Development and Analysis of a High-Fidelity Neutron Transport Sensor: The Quadrupole Detector Concept for Measurement of the Neutron Flux Gradient

Accurate reconstruction of the neutron flux distribution within a reactor core is essential for safe and efficient reactor operation. Traditional power shape synthesis in Light Water Reactors relies on hundreds of in-core detectors. However, this approach becomes impractical for Advanced Reactors and Microreactors due to limited space and harsh environments. To address this challenge, we propose a data-driven methodology that combines high-fidelity modeling with real-time ex-core sensor measurements, enabling the reconstruction of core power distribution while minimizing the reliance on intrusive in-core instrumentation. This project began in FY24 and achieved two initial milestones: (1) the definition of a three-year development plan for a Digital Twin framework and (2) the development of high-fidelity neutronics models of the Purdue University Reactor One (PUR-1) using both MCNP6 and OpenMC. The PUR-1 reactor, a zero-power facility, was selected due to its suitability for neutronics-focused modeling and the availability of experimental data for validation. Both models were benchmarked using neutron flux measurements obtained from irradiated gold foils, which were strategically placed within the core during a dedicated campaign in July 2024. This report marks the continuation and completion of those foundational tasks. The OpenMC model has been refined (improved geometric accuracy, expanded cross-section libraries, and refined sampling) and validated using additional experimental data. An updated sensor design—based on quadrupole configuration—was designed to measure both ex-core flux and its spatial gradient. These measurements will serve as inputs to a neural network-based reconstruction algorithm. Finally, the methodology was demonstrated on a two-dimensional test case representative of the heterogeneous material composition of the PUR-1 reactor core. A neural network implementation of the Kirchhoff-Helmholtz integral equation was employed to solve the boundary value problem using peripheral sensor measurements. The preliminary results confirm the strong potential of the proposed approach for accurate and minimally invasive neutron flux reconstruction.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Aberration corrected RF flipper for high resolution neutron spectroscopy

Project Summary Company: Adelphi Technology, Inc. Title: Aberration-corrected High Frequency RF Flipper for High-Resolution Neutron Spectroscopy PI: Dr. Jay Theodore Cremer Topic: C55-11 Enhancement of Scattering Instrumentation Technology Used at Pulsed and Continuous Sources Subtopic: d. Other Statement of the problem or situation that is being addressed. The quest to understand heterogeneous and hierarchical materials is gathering momentum, as described in a 2015 report by the Basic Energy Sciences Advisory Committee on Challenges at the Frontiers of Matter and Energy. For the past 40 years a technique called neutron spin echo (NSE) has been used to probe molecular motions in such non-crystalline materials over time scales from 10’s of picoseconds to 100’s of nanoseconds. The method has provided unique information about the dynamics of soft heterogeneous materials, including confirmation of the de Gennes model of polymer reptation and quantitative measurement of bending constants of biologically relevant lipid membranes. However, scientists continue to clamor for even higher resolution than NSE can provide. Biomaterials, polymers, glasses, and artificially nanostructured materials all manifest slow molecular motions because of weak or competing interactions between subunits and are amenable to study with neutrons, provided sufficiently long dynamical correlation times can be achieved. All these materials have important applications to advanced technologies so understanding them is key to technological progress. General statement of how this problem is being addressed. We will address the need for high-resolution neutron spectroscopy by using a technique called Neutron Resonance Spin Echo (NRSE). While similar to NSE in many respects, this method has the potential to exceed the NSE capabilities, if 2 technical hurdles can be overcome. The major impediments to successful high-resolution NRSE are the availability of two technologies: a very high frequency, efficient, radiofrequency (rf) flipper for neutrons and a method to correct certain magnetic aberrations. Based on previous STTR support and follow-on research we have developed a suitable rf flipper and we have invented a method to correct the magnetic aberrations. Both technologies need refinement to make them suitable for implementation at a neutron source such as the Oak Ridge National Laboratory nuclear reactor. In this proposal we seek to perfect the two technologies and to combine them into a single, operationally convenient device. Commercial Applications and Other Benefits In view of the increasing demand for the unique scientific information that high resolution neutron spectroscopy can provide, we expect several major instrumentation upgrades at both U.S. and foreign neutron centers will require make use of the NRSE method over the coming decade, creating a market for the devices we will design. These components will enhance scientists’ abilities to probe the time dependence of density fluctuations in a wide range of hierarchical and heterogeneous materials many of which are vital to existing and future technologies. Key Words – Polarized Neutrons, Neutron Spin Echo, Neutron Scattering, advanced materials. Summary for Members of Congress Neutron beams are a powerful materials-science probe that provide unique information about the structure of matter. The proposed devices will accelerate scientific discoveries required to achieve national goals for new technological materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

BEAST DB: Grand-Canonical Database of Electrocatalyst Properties

We present BEAST DB, an open-source database comprised of ab initio electrochemical data computed using grand-canonical density functional theory in implicit solvent at consistent calculation parameters. The database contains over 20,000 surface calculations and covers a broad set of heterogeneous catalyst materials and electrochemical reactions. Calculations were performed at self-consistent fixed potential as well as constant charge to facilitate comparisons to the computational hydrogen electrode. This article presents common use cases of the database to rationalize trends in catalyst activity, screen catalyst material spaces, understand elementary mechanistic steps, analyze the electronic structure, and train machine learning models to predict higher fidelity properties. Users can interact graphically with the database by querying for individual calculations to gain a granular understanding of reaction steps or by querying for an entire reaction pathway on a given material using an interactive reaction pathway tool. BEAST DB will be periodically updated, with planned future updates to include advanced electronic structure data, surface speciation studies, and greater reaction coverage.

database↗
DOE OSTI2024

Physics-informed neural networks for heterogeneous poroelastic media

This study presents a novel physics-informed neural network (PINN) framework for modeling poroelasticity in heterogeneous media with material interfaces. The approach introduces a composite neural network (CoNN) where separate neural networks predict displacement and pressure variables for each material. While sharing identical activation functions, these networks are independently trained for all other parameters. To address challenges posed by heterogeneous material interfaces, the CoNN is integrated with the Interface-PINNs (I-PINNs) framework (Sarma et al., Comput. Methods Appl. Mech. Eng. 429: 117135, 2024), allowing different activation functions across material interfaces. Further, this ensures accurate approximation of discontinuous solution fields and gradients. Performance and accuracy of this combined architecture were evaluated against the conventional PINNs approach, a single neural network (SNN) architecture, and the eXtended PINNs (XPINNs) framework through two one-dimensional benchmark examples with discontinuous material properties. The results show that the proposed CoNN with I-PINNs architecture achieves an RMSE that is two orders of magnitude better than the conventional PINNs approach and is at least 40 times faster than the SNN framework. Compared to XPINNs, the proposed method achieves an RMSE at least one order of magnitude better and is 40% faster.

42 ENGINEERING↗
DOE OSTI2025

Elucidation of Local Ordering and Atomic-Scale Structure in Polymer-Derived SiOC

Silicon oxycarbide (SiOC) is a versatile ceramic material with tunable microstructure and compositions that can be modulated through precursor chemistry and processing conditions. Though there are several noteworthy uses of SiOC across a range of application spaces, the difficulties in elucidating the short- to medium-range order within these materials have limited the maturation of strategies to precisely control SiC x O 4–x compositions for user-tailored applications. In this contribution, we implement a range of synchrotron scattering and spectroscopy methods coupled with stochastic modeling techniques to elucidate changes in local chemistry and structure associated with the pyrolysis of a commercially available SiOC polymer precursor. Stochastic modeling approaches provide valuable insights into decoupling local Si–O and Si–C environments while confirming predominate heterogeneous phases in materials. Using pyrolysis temperatures between 250 to 800 °C results in a heterogeneous material predominately composed of SiOC and amorphous SiO 2 domains. At 1100 °C, redistribution of Si–C pairs in the SiOC network and Si–O from the SiO 2 domains create a more ordered SiOC phase with local cubic SiC-like ordering. In addition, residual carbon leads to a detectable carbon phases at 1100 °C that persist at higher temperatures. These efforts address the difficulties of obtaining atomic-scale insights into the local structure and nanoscale heterogeneities in SiOC, providing pathways toward establishing structure–property relationships for future materials development.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗