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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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DOE OSTI2025

Depolymerization as a Design Strategy: Depolymerization Etching of Polymerization-Induced Microphase Separations

Thermally triggered depolymerization has traditionally been viewed through the lens of sustainability and recycling, not as a constructive tool for materials design. Herein, we show that selective, thermally triggered depolymerization to gaseous monomer serves as a solvent-free strategy for generating porosity in nanostructured polymer materials, offering a means to bypass the mass transport limitations inherent in conventional solution-based etching. As a demonstration platform, we employed polymerization-induced microphase separation (PIMS) to generate disordered bicontinuous block copolymer structures with embedded depolymerizable domains. By incorporating a methacrylate block susceptible to thermal depolymerization within a cross-linked, depolymerization-resistant styrenic matrix, we developed a process we term depolymerization etching of polymerization-induced microphase separations (DEPIMS). This approach enables highly selective and efficient domain removal via reversion to monomer to produce mesoporous materials with high surface areas (>200 m 2 /g). Subsequent surface functionalization yielded mesoporous adsorbents with tunable uptake kinetics and among the highest dye adsorption capacities reported for PIMS-derived materials, demonstrating the adaptability of the DEPIMS platform for chemical separations. DEPIMS can also be extended to a gram-scale, one-pot approach to yield mesoporous materials with recoverable monomer in under 12 h. These findings reposition thermal depolymerization from a sustainability tool to a broadly enabling strategy for scalable, on-demand fabrication of functional nanostructured materials.

Depolymerization↗

NASA NTRS1993

Time dependent turbulence modeling and analytical theories of turbulence

By simplifying the direct interaction approximation (DIA) for turbulent shear flow, time dependent formulas are derived for the Reynolds stresses which can be included in two equation models. The Green's function is treated phenomenologically, however, following Smith and Yakhot, we insist on the short and long time limits required by DIA. For small strain rates, perturbative evaluation of the correlation function yields a time dependent theory which includes normal stress effects in simple shear flows. From this standpoint, the phenomenological Launder-Reece-Rodi model is obtained by replacing the Green's function by its long time limit. Eddy damping corrections to short time behavior initiate too quickly in this model; in contrast, the present theory exhibits strong suppression of eddy damping at short times. A time dependent theory for large strain rates is proposed in which large scales are governed by rapid distortion theory while small scales are governed by Kolmogorov inertial range dynamics. At short times and large strain rates, the theory closely matches rapid distortion theory, but at long times it relaxes to an eddy damping model.

Rubinstein, R.↗

DOE OSTI2021

Observation of a chiral wave function in the twofold-degenerate quadruple Weyl system BaPtGe

Topological states in quantum materials are defined by bulk wave functions that possess nontrivial topological invariants. While edge modes are widely presented as signatures of nontrivial topology, how bulk wave functions can manifest explicitly topological properties remains unresolved. Here, using high-resolution inelastic x-ray spectroscopy (IXS) combined with first principles calculations, we report experimental signatures of chiral wave functions in the bulk phonon spectrum of BaPtGe, which we show to host a previously undiscovered twofold-degenerate quadruple Weyl node. The chirality of the degenerate phononic wave function yields a nontrivial phonon dynamical structure factor, S(Q,ω), along high-symmetry directions, that is in excellent agreement with numerical and model calculations. Furthermore, our results establish IXS as a powerful tool to uncover topological wave functions, providing a key missing ingredient in the study of topological quantum matter.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS1988

Theoretical study of the nitrogen atom hyperfine coupling constant

The nitrogen-atom isotropic hyperfine coupling constant A(iso) is studied as a function of improvements in both the one-particle and n-particle basis sets. The study underscores the importance of diffuse basis functions. For example, the (9s 5p) primitive set of Huzinaga (1965) augmented with an even-tempered diffuse s function yields values for A(iso) that are virtually identical to an energy-optimized (23s 12p) even-tempered set. The A(iso) constant is found to converge relatively quickly with increasing l quantum numbers: d, f, and g functions are estimated to contribute 2.5 + or - 0.2, 0.4 + or - 0.1, and 0.05 + or - 0.05 MHz, respectively. Full CI calibration calculations indicate that very high levels of correlation treatment are required for quantitative results. In addition, a strong coupling is observed between the one-particle and n-particle requirements. The best result, 10.4 MHz, is in excellent agreement with the accurate experimental value of 10.4509 MHz.

Bauschlicher, Charles W., Jr.↗

NASA NTRS1987

Full CI benchmark calculations for several states of the same symmetry

Full CI (FCI) wave functions are used to compute energies for several electronic states of the same symmetry for SiH2, CH2, and CH2(+). It is found that CASSCF/multireference CI wave functions yield results very similar to FCI, irrespective of whether the CASSCF MOs are optimized independently for each state or using an average of the CASSCF energies for all desired states. The ionization potentials and excitation energies obtained from the FCI calculations should help calibrate methods (such as Green's function approaches, equations of motion and propagator methods, and cluster expansions) in which energy differences are computed directly.

Bauschlicher, Charles W., Jr.↗

NASA NTRS1984

Oscillator strengths and collision strengths for S III

The present calculation, in a close-coupled approximation for the energy range up to 1,000,000 K, yields collision strengths for the electron impact excitation of S III from the ground 3p2 3P state to the excited states 3s3p3 3D0, 3P0, 3S0, 3d 3D0, 3P0, and 4s 3P0. Also obtained are those transitions' oscillator strengths, and strengths for others involving 3p2 1D and 1S. Configuration-interaction target wave functions yielding oscillator strengths that are accurate to 20 percent are used in collision strength calculations.

Ho, Y. K.↗

DOE OSTI2021

Novel theranostic agent for PET imaging and targeted radiopharmaceutical therapy of tumour-infiltrating immune cells in glioma

Background: Malignant gliomas are deadly tumours with few therapeutic options. Although immunotherapy may be a promising therapeutic strategy for treating gliomas, a significant barrier is the CD11b + tumour-associated myeloid cells (TAMCs), a heterogeneous glioma infiltrate comprising up to 40% of a glioma's cellular mass that inhibits anti-tumour T-cell function and promotes tumour progression. A theranostic approach uses a single molecule for targeted radiopharmaceutical therapy (TRT) and diagnostic imaging; however, there are few reports of theranostics targeting the tumour microenvironment. Methods: Utilizing a newly developed bifunctional chelator, Lumi804, an anti-CD11b antibody (αCD11b) was readily labelled with either Zr-89 or Lu-177, yielding functional radiolabelled conjugates for PET, SPECT, and TRT. Findings: 89 Zr/ 177 Lu-labeled Lumi804-αCD11b enabled non-invasive imaging of TAMCs in murine gliomas. Additionally, 177 Lu-Lumi804-αCD11b treatment reduced TAMC populations in the spleen and tumour and improved the efficacy of checkpoint immunotherapy. Interpretation: 89 Zr- and 177 Lu-labeled Lumi804-αCD11b may be a promising theranostic pair for monitoring and reducing TAMCs in gliomas to improve immunotherapy responses.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Self‐Limiting Polymerization‐Induced Crystallization‐Driven Self‐Assembly (SL‐PI‐CDSA) Enables Templated Synthesis of Chiral Plasmonic, Hybrid 2D Hexagonal Assemblies

Examples of self-regulating synthetic self-assembly are relatively few, with most known chemical systems relying on kinetic rather than thermodynamic control. Herein, we demonstrate the rapid generation (t ≤ 5 min) of size-tunable ultralow dispersity (Ð ≤ 1.01) 2D hexagonal nanosheets governed by self-limiting self-assembly (SLSA). Self-assembly in natural systems occurs with exquisite control of structure, function, and dimension. We demonstrate that key aspects of biological assembly can be rationally applied toward the development of bottom-up approaches for the construction of chiral nanomaterials. To this end, self-limiting polymerization-induced crystallization-driven self-assembly (SL-PI-CDSA) of modular and templating aryl isocyanide (AIC) monomers yields functional 2D assemblies permitting post-polymerization/assembly modifications. Detailed study of the internal and external structure of the hexagonal nanosheets reveals topological defects which offer mechanistic insights into both their assembly and subsequent utilization. Specifically, these features enable fabrication of chiral hybrid organic–inorganic nanomaterials incorporating chiral plasmonic metal nanoparticles (MNPs). Our results suggest that the synergistic interplay of template-driven confinement and hierarchical chirality induce symmetry breaking of in situ-generated gold MNPs. We anticipate that the platform presented will facilitate fabrication of new hybrid, chiral organic–inorganic nanostructures.

36 MATERIALS SCIENCE↗

DOE OSTI2020

nPINNs: Nonlocal physics-informed neural networks for a parametrized nonlocal universal Laplacian operator. Algorithms and applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius $\delta$ goes to zero, and to the fractional Laplacian as $\delta$ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We also provide theoretical convergence rates with respect to $\delta$ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, $\delta$ and $\alpha$, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order $\alpha(y)$, which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as $\delta$. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

nPINNs: nonlocal Physics-Informed Neural Networks for a parametrized nonlocal universal Laplacian operator. Algorithms and Applications

Physics-informed neural networks (PINNs) are effective in solving inverse problems based on differential and integro-differential equations with sparse, noisy, unstructured, and multifidelity data. PINNs incorporate all available information, including governing equations (reflecting physical laws), initial-boundary conditions, and observations of quantities of interest, into a loss function to be minimized, thus recasting the original problem into an optimization problem. In this paper, we extend PINNs to parameter and function inference for integral equations such as nonlocal Poisson and nonlocal turbulence models, and we refer to them as nonlocal PINNs (nPINNs). The contribution of the paper is three-fold. First, we propose a unified nonlocal Laplace operator, which converges to the classical Laplacian as one of the operator parameters, the nonlocal interaction radius δ goes to zero, and to the fractional Laplacian as δ goes to infinity. This universal operator forms a super-set of classical Laplacian and fractional Laplacian operators and, thus, has the potential to fit a broad spectrum of data sets. We provide theoretical convergence rates with respect to δ and verify them via numerical experiments. Second, we use nPINNs to estimate the two parameters, δ and α, characterizing the kernel of the unified operator. The strong non-convexity of the loss function yielding multiple (good) local minima reveals the occurrence of the operator mimicking phenomenon, that is, different pairs of estimated parameters could produce multiple solutions of comparable accuracy. Third, we propose another nonlocal operator with spatially variable order α(γ), which is more suitable for modeling turbulent Couette flow. Our results show that nPINNs can jointly infer this function as well as δ. More importantly, these parameters exhibit a universal behavior with respect to the Reynolds number, a finding that contributes to our understanding of nonlocal interactions in wall-bounded turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Nonequilibrium information entropy approach to ternary fission of actinides

Ternary fission of actinides probes the state of the nucleus at scission. Light clusters are produced in space and time very close to the scission point. Within the nonequilibrium statistical operator method, a generalized Gibbs distribution is constructed from the information given by the observed yields of isotopes. Using this relevant statistical operator, yields are calculated taking excited states and continuum correlations into account, in accordance with the virial expansion of the equation of state. Furthermore, clusters with mass number A≤10 are well described using the nonequilibrium generalizations of temperature and chemical potentials. Improving the virial expansion, in-medium effects may become of importance in determining the contribution of weakly bound states and continuum correlations to the intrinsic partition function. Yields of larger clusters, which fail to reach this quasiequilibrium form of the relevant distribution, are described by nucleation kinetics, and a saddle-to-scission relaxation time of about 7000 fm/c is inferred. Light-charged particle emission, described by reaction kinetics and virial expansions, may therefore be regarded as a very important tool to probe the nonequilibrium time evolution of actinide nuclei during fission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1977

An investigation of rotor noise generation by aerodynamic disturbance

An open rotor was considered as a process for converting an unsteady velocity inflow into sound radiation. With the aid of crude assumptions, aero-acoustic transfer functions were defined theoretically for both discrete frequency and broad band noise. A study of the validity of these transfer functions yielded results which show good agreement at discrete frequencies though slightly less good for broad band noise. Agreement in both cases holds over three or more decades of the relevant parameters. A rotating hot wire anemometry system consisting of a single hot wire probe mounted in the nose cone of the rotor was used to quantify fluctuations in the airflow onto a single rotor blade for the transfer function results. Further theoretical analysis revealed that the sound field can be expressed in terms of blade-to-blade correlations in the airflow, and results from two probes rotating simultaneously were modelled mathematically and inserted in the theory. Preliminary results snow encouraging agreement with experimental data.

Whitfield, C. E.↗

NASA NTRS1976

Autoignition - A liquid propellant explosive potential limiting phenomena

It has been thought that the unplanned mixing of the propellants of liquid launch vehicles could result in massive explosion and destruction. This paper presents results of NASA's attempt to develop a precise analytical theory and predictive model for liquid propellant explosives. Prediction model experiments involving a 'seven-chart' approach showing the relationship between yield function, mixing function, time dependence, chemical and physical reactant properties and quantity effects were used to establish a theory that autoignition takes place when propellants mix in a certain 'critical mass' or greater. An analytical confirmation of the theory has been carried out, with particular reference to possible autoignition in LOX/LH2 and LOX/RP-1 and Saturn V: it was confirmed that autoignition occurs and prevents the mixing of more than the 'critical mass' and therefore limits the explosive yield to several thousand pounds, which will disperse the remaining propellant and prohibit detonation of the total quantities otherwise suspected.

Boggs, W. H.↗

DOE OSTI2025

Uncertainty propagation in feed-forward neural network models

We develop new uncertainty propagation methods for feed-forward neural network architectures with leaky ReLU activation functions subject to random perturbations in the input vectors. In particular, we derive analytical expressions for the probability density function (PDF) of the neural network output and its statistical moments as a function of the input uncertainty and the parameters of the network, i.e., weights and biases. A key finding is that an appropriate linearization of the leaky ReLU activation function yields accurate statistical results even for large perturbations in the input vectors. This can be attributed to the way information propagates through the network. We also propose new analytically tractable Gaussian copula surrogate models to approximate the full joint PDF of the neural network output. To validate our theoretical results, we conduct Monte Carlo simulations and a thorough error analysis on a multi-layer neural network representing a nonlinear integro-differential operator between two polynomial function spaces. Our findings demonstrate excellent agreement between the theoretical predictions and Monte Carlo simulations.

MLP networks↗

DOE OSTI2024

Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics

Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints and to overcome the challenging and time-consuming task of formulating phenomenological constitutive laws that can accurately capture the observed material response. However, even though neural network-based constitutive laws have been shown to generalize proficiently, the generated representations are not easily interpretable due to their high number of trainable parameters. Sparse regression approaches exist that allow for obtaining interpretable expressions, but the user is tasked with creating a library of model forms which by construction limits their expressiveness to the functional forms provided in the libraries. Here, in this work, we propose to train regularized physics-augmented neural network-based constitutive models utilizing a smoothed version of $L^0$-regularization. This aims to maintain the trustworthiness inherited by the physical constraints, but also enables interpretability which has not been possible thus far on any type of machine learning-based constitutive model where model forms were not assumed a priori but were actually discovered. During the training process, the network simultaneously fits the training data and penalizes the number of active parameters, while also ensuring constitutive constraints such as thermodynamic consistency. We show that the method can reliably obtain interpretable and trustworthy constitutive models for compressible and incompressible hyperelasticity, yield functions, and hardening models for elastoplasticity, using synthetic and experimental data. This work aims to set a new paradigm for interpretable machine learning models in the broad area of solid mechanics where low and limited data is available along with prior knowledge of physical constraints that the learned maps need to obey. This paradigm can potentially be extended to a broader spectrum of scientific exploration.

Data-driven constitutive models↗

DOE OSTI2021

Bridging the gap between simulated and experimental ionic conductivities in lithium superionic conductors

Lithium superionic conductors (LSCs) are of major importance as solid electrolytes for next-generation all-solid-state lithium-ion batteries. While ab initio molecular dynamics have been extensively applied to study these materials, there are often large discrepancies between predicted and experimentally measured ionic conductivities and activation energies due to the high temperatures and short time scales of such simulations. Here, we present a strategy to bridge this gap using moment tensor potentials (MTPs). We show that MTPs trained on energies and forces computed using the van der Waals optB88 functional yield much more accurate lattice parameters, which in turn leads to accurate prediction of ionic conductivities and activation energies for the Li 0·33 La 0·56 TiO 3 , Li 3 YCl 6 and Li 7 P 3 S 11 LSCs. NPT MD simulations using the optB88 MTPs also reveal that all three LSCs undergo a transition between two quasi-linear Arrhenius regimes at relatively low temperatures. This transition can be traced to an increase in the number and diversity of diffusion pathways, in some cases with a change in the dimensionality of diffusion. Furthermore, this work presents not only an approach to develop high accuracy MTPs, but also outlines the diffusion characteristics for LSCs which is otherwise inaccessible through ab initio computation.

25 ENERGY STORAGE↗

DOE OSTI2022

Experimental study of compaction localization in carbonate rock and constitutive modeling of mechanical anisotropy

Abstract Sedimentary rocks are inherently anisotropic and prone to strain localization. While the influence of rock anisotropy on the brittle/dilative regime has been studied extensively, its influence on the ductile/compactive regime is much less explored. This paper discusses the anisotropic behavior of a high‐porosity carbonate rock from central Europe (the Maastricht Tuffeau). A set of triaxial tests with concurrent x‐ray tomography has been performed at different confining pressures. The anisotropic characteristics of this rock have been investigated by testing samples cored at different inclinations of the bedding, thus revealing non‐negligible effects of the coring direction on yielding and compaction behavior. Specifically, samples cored perpendicular to bedding display higher strength and longer stages of post‐yielding deformation before manifesting re‐hardening. Despite such alterations of the inelastic response, Digital Image Correlation has revealed that the strain localization mode is independent of the coring direction, thus being primarily affected by the confinement level. To capture the observed interaction between material anisotropy and compaction behavior at the continuum‐scale, an elastoplastic constitutive law has been proposed. For this purpose, a set of tensorial bases has been introduced to replicate how the oriented rock fabric modulates the yielding and plastic flow characteristics of the material. The analyses show that the impact of the coring direction on yield function and plastic flow rule is fundamentally different, thus requiring the use of distinct projection strategies (a strategy here defined heterotopic mapping). The performance of the model, studied through parametric analyses and by calibrating the experimental results, illustrates the improved capability of the proposed constitutive approach when applied to strongly anisotropic porous rocks.

Shahin, Ghassan↗

DOE OSTI2019

A micromorphically regularized Cam-clay model for capturing size-dependent anisotropy of geomaterials

Here we introduce a regularized anisotropic modified Cam-clay (MCC) model which captures the size-dependent anisotropic elastoplastic responses for clay, mudstone, shales, and sedimentary rock. By homogenizing the multiscale anisotropic effects induced by clay particle aggregate, clusters, peds, micro-fabric, and mineral contact across length scales, we introduce two distinctive anisotropic mechanisms for the MCC model at the material point and mesoscale levels. We first employ a mapping that links the anisotropic stress state to a fictitious isotropic principal stress-space to introduce anisotropy at the material point scale. Then, the mesoscale anisotropy is introduced via an anisotropic regularization mechanism. This anisotropic regularization mechanism is triggered by introducing gradient-dependence of the internal variables through a penalty method such that the resultant gradient-enhanced plastic flow may exhibit anisotropic responses non-coaxial to the stress gradient of the yield function. The influence of the size-dependent anisotropy on the formation of the shear band and the macroscopic responses of the effective media are analyzed in 2D and 3D numerical examples.

42 ENGINEERING↗