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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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The Transient Covalent Bond in Abiotic Nonequilibrium Systems

Abstract Biochemical systems accomplish many critical functions with by operating out‐of‐equilibrium using the energy of chemical fuels. The formation of a transient covalent bond is a simple but very effective tool in designing analogous reaction networks. This Minireview focuses on the fuel chemistries that have been used to generate transient bonds in recent demonstrations of abiotic nonequilibrium systems (i.e., systems that do not make use of biological components). Fuel reactions are divided into two fundamental classifications depending on whether the fuel contributes structural elements to the activated state, a distinction that dictates how they can be used. Reported systems are further categorized by overall fuel reaction (e.g., hydrolysis of alkylating agents, carbodiimide hydration) and illustrate how similar chemistry can be used to effect a wide range of nonequilibrium behavior, ranging from self‐assembly to the operation of molecular machines.

Kariyawasam, Lasith S.↗
DOE OSTI2024

Fluorophilic Sigma(σ)‐Lock Self‐Healable Copolymers

Abstract Although F‐Containing molecules and macromolecules are often used in molecular biology to increase the binding with Lewis acidic groups by introducing favorable C−F dipoles, there is virtually no experimental evidence and limited understanding of the nature of these interactions, especially their role in synthetic polymeric materials. These studies elucidate the molecular origin of inter‐ and intra‐Chain interactions responsible for self‐healing of F‐Containing copolymers composed of pentafluorostyrene and n‐butyl acrylate units (p(PFS/nBA). Guided by dynamic surface oscillating force (SOF) and spectroscopic measurements supported by molecular dynamics (MD) simulations, these studies show that the reformation of σ‐σ orbitals in −C−F of PFS and CH 3 CH 2 − of nBA units enables the recovery of entropic energy via fluorophilic‐σ‐lock van der Waals forces when PFS/nBA molar ratios are ~50/50. The strength of these interactions determined experimentally for self‐healable PFS/nBA compositions is in the order ~0.3 kcal/mol which primarily comes from fluorophilic‐σ‐lock (~70 %) contributions. These interactions are significantly diminished for non‐self‐healable counterparts. Strongly polarized −C−F σ orbitals create lateral dipolar forces enhancing the affinity towards −C−H orbitals, facilitating energetically favorable interactions. Entropic recovery driven by non‐Covalent bonding offers a valuable tool in designing materials with unique functionalities, particularly self‐healable batteries and energy storage devices.

Gaikwad, Samruddhi↗
DOE OSTI2026

Selective Electrochemical End‐Group Removal Enhances Polymer Thermal Stability

Here we introduce an electrochemical strategy for the selective and quantitative removal of thiocarbonylthio end groups from polymers prepared by reversible addition–fragmentation chain transfer (RAFT) or photoiniferter polymerization. Our results indicate that applying a cathodic potential in an undivided cell promotes reductive cleavage of the thiocarbonylthio moiety, generating terminal polymer radicals that are efficiently capped with hydrogen atoms in the presence of benign donors. This transformation proceeds cleanly across diverse polymer backbones and end-group chemistries, including trithiocarbonates and dithiobenzoates, without chain coupling or degradation. Moreover, the applied potential can be tuned to enable chemoselective end-group removal in mixed-polymer systems, a level of control inaccessible by thermal, photochemical, or nucleophilic strategies. Beyond delivering colorless and optically transparent materials, electrochemical end-group removal significantly enhances polymer stability. Poly(methyl methacrylate) subjected to electrochemical end-group removal exhibited a T 95 of 342 °C, exceeding the stabilities of analogous polymers with end groups removed by aminolysis (T 95 = 260 °C) or radical-based methods (T 95 = 299 °C). These findings demonstrate redox-directed post-polymerization modification as a tool for designing robust, transparent, and thermally stable macromolecules and establish electrochemistry as a platform strategy in polymer synthesis and processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

The Transient Covalent Bond in Abiotic Nonequilibrium Systems

Biochemical systems accomplish many critical functions with by operating out-of-equilibrium using the energy of chemical fuels. The formation of a transient covalent bond is a simple but very effective tool in designing analogous reaction networks. This Minireview focuses on the fuel chemistries that have been used to generate transient bonds in recent demonstrations of abiotic nonequilibrium systems (i.e., systems that do not make use of biological components). Here, fuel reactions are divided into two fundamental classifications depending on whether the fuel contributes structural elements to the activated state, a distinction that dictates how they can be used. Reported systems are further categorized by overall fuel reaction (e.g., hydrolysis of alkylating agents, carbodiimide hydration) and illustrate how similar chemistry can be used to effect a wide range of nonequilibrium behavior, ranging from self-assembly to the operation of molecular machines.

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

Fluorophilic Sigma(σ)‐Lock Self‐Healable Copolymers

Although F-Containing molecules and macromolecules are often used in molecular biology to increase the binding with Lewis acidic groups by introducing favorable C−F dipoles, there is virtually no experimental evidence and limited understanding of the nature of these interactions, especially their role in synthetic polymeric materials. These studies elucidate the molecular origin of inter- and intra-Chain interactions responsible for self-healing of F-Containing copolymers composed of pentafluorostyrene and n-butyl acrylate units (p(PFS/nBA). Guided by dynamic surface oscillating force (SOF) and spectroscopic measurements supported by molecular dynamics (MD) simulations, these studies show that the reformation of σ-σ orbitals in −C−F of PFS and CH 3 CH 2 − of nBA units enables the recovery of entropic energy via fluorophilic-σ-lock van der Waals forces when PFS/nBA molar ratios are ~50/50. The strength of these interactions determined experimentally for self-healable PFS/nBA compositions is in the order ~0.3 kcal/mol which primarily comes from fluorophilic-σ-lock (~70 %) contributions. These interactions are significantly diminished for non-self-healable counterparts. Strongly polarized −C−F σ orbitals create lateral dipolar forces enhancing the affinity towards −C−H orbitals, facilitating energetically favorable interactions. Entropic recovery driven by non-Covalent bonding offers a valuable tool in designing materials with unique functionalities, particularly self-healable batteries and energy storage devices.

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

Human readiness levels and Human Views as tools for user-centered design

The Human Readiness Level (HRL) scale is a simple nine-level scale that brings structure and consistency to the real-world application of user-centered design. It enables multidisciplinary consideration of human-focused elements during the system development process. Use of the standardized set of questions comprising the HRL scale results in a single human readiness number that communicates system readiness for human use. The Human Views (HVs) are part of an architecture framework that provides a repository for human-focused system information that can be used during system development to support the evaluation of HRL levels. Here, this paper illustrates how HRLs and HVs can be used in combination to support user-centered design processes. A real-world example for a U.S. Army software modernization program is described to demonstrate application of HRLs and HVs in the context of user-centered design.

42 ENGINEERING↗
DOE OSTI2023

Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures

Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD directly to treat the deformation and failure of materials at the mesoscale is still largely beyond reach. In this work, we propose a learning framework to extract a peridynamics model as a mesoscale continuum surrogate from MD simulated material fracture data sets. Firstly, we develop a novel coarse-graining method, to automatically handle the material fracture and its corresponding discontinuities in the MD displacement data sets. Inspired by the weighted essentially non-oscillatory (WENO) scheme, the key idea lies at an adaptive procedure to automatically choose the locally smoothest stencil, then reconstruct the coarse-grained material displacement field as the piecewise smooth solutions containing discontinuities. Then, based on the coarse-grained MD data, a two-phase optimization-based learning approach is proposed to infer the optimal peridynamics model with damage criterion. In the first phase, we identify the optimal nonlocal kernel function from the data sets without material damage to capture the material stiffness properties. Then, in the second phase, the material damage criterion is learnt as a smoothed step function from the data with fractures. As a result, a peridynamics surrogate is obtained. As a continuum model, our peridynamics surrogate model can be employed in further prediction tasks with different grid resolutions from training, and hence allows for substantial reductions in computational cost compared with MD. We illustrate the efficacy of the proposed approach with several numerical tests for the dynamic crack propagation problem in a single-layer graphene. Our tests show that the proposed data-driven model is robust and generalizable, in the sense that it is capable of modeling the initialization and growth of fractures under discretization and loading settings that are different from the ones used during training.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Phase-field modeling of thermally-grown oxide and damage evolution in environmental barrier coatings

Silicon carbide-based ceramic matrix composites protected by environmental barrier coatings (EBCs) present a promising materials solution for next-generation gas turbines. Developing more robust and efficient EBCs is therefore of significant technological importance. During the service in high-temperature oxidative environments, there is a thermally grown oxide (TGO) layer, spontaneously formed in the EBC system. TGO is recognized as a critical factor for the degradation and failure of EBCs, yet the detailed mechanisms of TGO growth and its effect on EBC failure remain unclear. In this study we develop a comprehensive chemo-mechano-phase-field model to simulate growth of the TGO in EBCs, factoring in creep and deformation, and especially the cracking behaviors. The volume expansion due to TGO growth and the resulting large inelastic deformation are addressed by using our recently developed, so-called incremental realization of inelastic deformation (IRID) algorithm, in combination with an adapted Hu-Chen spectral solver for elasticity. Simulations of TGO growth are performed considering different growth modes of TGOs determined mainly by the ratio of oxidant permeability in the topcoat to that in the TGO itself. Large-scale three-dimensional (3D) simulations are performed to model the formation of interconnecting vertical/channel cracks (often called ‘mud cracks’). The simulated crack morphology are in excellent agreement with the experimental observations from the literature. The simulations also provide insights into the cracking of EBCs and its dependence on the structure and constituent properties of the coating system. Furthermore, these results demonstrate the developed damage model can be a useful tool for design of more durable EBCs.

Cracking↗
DOE OSTI2023

Conformational space exploration of cryo-EM structures by variability refinement

Cryo-EM observation of biological samples enables visualization of sample heterogeneity, in the form of discrete states that are separable, or continuous heterogeneity as a result of local protein motion before flash freezing. Variability analysis of this continuous heterogeneity describes the variance between a particle stack and a volume, and results in a map series describing the various steps undertaken by the sample in the particle stack. While this observation is absolutely stunning, it is very hard to pinpoint structural details to elements of the maps. Here, in order to bridge the gap between observation and explanation, we designed a tool that refines an ensemble of structures into all the maps from variability analysis. Using this bundle of structures, it is easy to spot variable parts of the structure, as well as the parts that are not moving. Comparison with molecular dynamics simulations highlights the fact that the movements follow the same directions, albeit with different amplitudes. Ligand can also be investigated using this method. Variability refinement is available in the Phenix software suite, accessible under the program name phenix.varref.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Generation of random geological models using multi-randomization for machine learning

Generating high-fidelity geological models is essential for advancing machine learning (ML) methods in automated seismic interpretation. For instance, seismic images paired with corresponding fault labels are foundational for ML-based fault detection from seismic migration sections. While several open-access datasets of random geological models exist, open-source tools specifically designed to produce large volumes of such models for ML applications remain scarce. To address this gap, we present RGM (Random Geological Model), an open-source software package for efficiently generating 2D and 3D synthetic geological models tailored for ML workflows. RGM supports the creation of diverse model components, including medium property distributions (P-/S-wave velocities and density), seismic reflectivity images (i.e., synthetic migration sections), relative geological time, and discrete fault attributes such as probability, dip, strike, rake, and displacement. It also accommodates the creation of complex geological features such as salt bodies and unconformities. The model generation algorithm employs a multi-randomization strategy, yielding an effectively infinite-dimensional model space that encompasses a wide range of geological scenarios and associated seismic features. Furthermore, RGM incorporates a method to generate synthetic elastic migration images using analytical elastic reflection coefficients combined with frequency-dependent scaling. This functionality enables the creation of training datasets for ML models that leverage elastic seismic images. RGM is implemented in modern object-oriented Fortran, allowing users to flexibly control statistical parameters governing model variability. We demonstrate the capability, performance, and geological realism of the package through comprehensive 2D and 3D examples.

58 GEOSCIENCES↗
DOE OSTI2022

Enhancement of phase change material hysteresis model: A case study of modeling building envelope in EnergyPlus

Nowadays, buildings are expected to offer demand side services to the power grid to enhance the electrical load flexibility, which leads to the concepts of grid-interactive efficient buildings (GEBs). Phase change material (PCM)-based thermal energy storage has seen increasing attention in recent years for peak load shifting of grid-interactive efficient buildings (GEBs). Numerical models are critical tools for design and evaluation of PCM-integrated systems. Most industrial-grade PCMs are reported to melt/freeze over a temperature range instead of at a unique temperature. Such thermal hysteresis effect significantly affects the reliability of simulation results because not only the heat transfer process depends on melting and freezing temperatures, the PCM thermal properties change significantly during the phase change process as well. This study is aimed to develop a model for the PCMs used in the building envelope with the capability to accurately simulate hysteretic behaviors. Further, this model is based on a two-phase assumption and is implemented in a whole building energy performance simulation program (i.e., EnergyPlus). A comparison between numerical results and experimental data shows that during a complete phase transition, the two-phase model could achieve a good agreement with the experimental data. During a partial phase transition, the two-phase model could lead to significant improvements compared to other alternative PCM models, including the existing PCM model in EnergyPlus. Last, whole building simulations were performed to study this model's performance regarding heating/cooling loads and zone mean air temperature of a given building. The results show that the difference in hourly heating/cooling loads introduced by the models was less than 1% in design conditions, while significant changes were observed in both hourly heating/cooling loads and zone mean air temperature when the PCM envelope underwent partial phase transition processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Cybersecurity Vulnerability Mitigation Framework through Empirical Paradigm: Enhanced Prioritized Gap Analysis

Existing cybersecurity vulnerability assessment tools were designed based on the policies and standards defined by organizations such as the U.S. Department of Energy and the National Institute of Standards and Technology (NIST). Frameworks such as the cybersecurity capability maturity model (C2M2) and the NIST Cybersecurity Framework (CSF) are often used by the critical infrastructure owners and operators to determine the cybersecurity maturity of their facility. Although these frameworks are exceptional at performing qualitative cybersecurity analysis and identifying vulnerabilities, they do not provide a means to perform prioritized mitigation of those vulnerabilities in order to achieve a desired cybersecurity maturity. To address that challenge, we developed a framework and software application called the cybersecurity vulnerability mitigation framework through empirical paradigm (CyFEr). This paper presents the detailed architecture of CyFEr’s enhanced prioritized gap analysis (EPGA) methodology and its application to CSF. The efficacy of the presented framework is demonstrated by comparing against existing similar models and testing against the cyber injects from a real-world cyber-attack that targeted industrial control systems (ICS) in critical infrastructures.

Gourisetti, Sri Nikhil G.↗
DOE OSTI2024

Neural network interatomic potential-driven analysis of phase stability in Ti–V alloys at the atomistic scale

The evolution of the ω phase in titanium–vanadium (Ti–V) alloys is critical for their mechanical properties, particularly in aerospace and biomedical applications. Here, this study employs a Rapid Artificial Neural Network (RANN) potential to model the ω phase evolution at the atomistic level, demonstrating a high degree of consistency with experimental observations, unlike the Modified Embedded Atom Method (MEAM), which fails to capture this phase transformation accurately. RANN simulations replicate key phenomena such as the nucleation of α precipitates at ω/β interfaces and accurate lattice orientations, enhancing our understanding of phase stability and transformation kinetics. The findings affirm that RANN potentials can significantly improve the prediction accuracy of complex material behaviors, offering a powerful tool for designing advanced materials with tailored properties such as solute effect in various stacking fault energies. This approach not only bridges the gap between theoretical predictions and empirical data but also sets a new direction for future research in materials science, emphasizing the integration of machine learning techniques in the development and optimization of new alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Chemical composition based machine learning model to predict defect formation in additive manufacturing

With a goal of exploiting additive manufacturing to improve the manufacturing of existing reactor materials, we developed a chemical composition-based machine learning model to predict the printability of any given alloy in laser powder bed fusion (L-PBF) using experimental data from peer-reviewed literature. We defined printability as the ability to avoid defects like cracking, balling, porosity, and lack of fusion, that are caused by thermal stresses (during solidification or liquation), molten pool disintegration into disconnected small beads or lack of heat input respectively. Our models predict the tendency of balling defect formation and porosity percentage for a given composition, under a given set of processing conditions. To predict the likelihood of balling defect, three models: a random forest classifier, a gradient boost regressor and a neural network were trained on a dataset containing both traditional alloys and high entropy alloys. The neural network model showed the highest accuracy of 92.3 % in predicting the balling defect formation. A random forest regressor, gradient boost regressor and neural network were trained and tested on a dataset of various alloys to predict porosity. The random forest regressor showed the best predictions with an R 2 score of 0.97. The models also revealed the relative importance of the input descriptors on defect-formation tendency. Of particular significance was the identification of carbon as an important element in determining the occurrence of balling and percent porosity in alloys like steel, as well as being moderately important to the percentage porosity in other alloys as well as steel. Manganese was also identified as a key descriptor for the percentage of porosity in steel and other alloys. Manganese’s low thermal conductivity and consistent presence in the dataset is the likely cause for its contribution. Carbon’s role is attributable to its relatively high specific heat and high melting temperature. In conclusion, our model serves as a swift, chemistry-based tool to design experiments and find modified compositions better suited for additive manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

An improved pressure drop correlation for modeling localized effects in a pebble bed reactor

Advances in the development of pebble bed reactors (PBRs) has created a desire for accurate and cost-effective simulation tools for design scoping studies and safety analysis. The current state-of-the-art for these simulations is the use of porous media models, although these models rely on correlations to capture the effects of flow features that are not explicitly modeled. One of the areas where correlation accuracy is currently lacking is in the near-wall region of the bed. In this region, the presence of the wall causes the pebbles to pack more orderly, drastically changing the geometry and flow behavior in this region. This work presents a new generalized pressure drop correlation for PBRs based on the KTA equation. A high-to-low methodology is applied, where large eddy simulation (LES) is performed on two beds of 1568 and 1700 pebbles to generate a high-fidelity dataset. The flow fields are then averaged in time and separated into concentric rings of 0.05 D peb width. Average porosity, velocity, and pressure drop are extracted for each ring and the friction and form losses are calculated. The Reynolds number range for this study is 625–10,000, and thus the form losses are dominant over the friction losses. The form losses across the rings are investigated, and a correction term for the form loss calculation is determined and applied to the KTA equation to drastically improve the capability of modeling localized porosity effects in a porous media code. Finally, the improved correlation reduces near-wall velocity prediction error from over 50% with the KTA correlation to around 5%. Agreement in pressure drop prediction between LES and porous media simulations is also improved.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Effect of Local Structural Distortions on Antiferroelectric–Ferroelectric Phase Transition in Dilute Solid Solutions of K x Na 1– x NbO 3

The fundamental principles that govern antiferroelectric (AFE)–ferroelectric (FE) transitions are not well understood for many solid solutions of perovskite compounds. For example, crystal chemical considerations based on the average Goldschmidt tolerance factor or ionic polarizability do not precisely predict the boundary between the AFE and FE phases in dilute solid solutions of alkali niobates, such as K x Na 1– x NbO 3 ( x ≤ 0.02). Here, based on detailed structural analysis from neutron total scattering experiments, we provide insights about how the relative local distortions around the A- and B-sites of the ABO 3 perovskite structure affect the AFE/FE order of the average crystallographic phases in K x Na 1– x NbO 3 . Specifically, we show that a higher (lower) ratio of B-site-centered distortions over A-site-centered distortions drives transition toward a long-range FE (AFE) phase, which is based on a competition between the long-range polarizing field of the Nb–O dipoles and the disordering effect of local distortions around the A-site. Our study provides a predictive tool for designing complex solid-solution perovskites with tunable (anti)ferroelectric polarization properties, which can be of interest for various energy-related applications such as high-density energy storage and solid-state cooling.

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

A Robotic High-Throughput Grid-Search Platform for Mapping Phase Behavior in Triblock Copolymer–Homopolymer Blends

We present a high-throughput experimental investigation of the phase behavior in triblock copolymers (PS-b-PB-b-PS and PS-b-PI-b-PS) and polystyrene (PS) homopolymer blends as a function of homopolymer molecular weight (MW) and blend ratio. Using a robotic thin-film processing platform (NOVA) integrated with Grazing Incidence Small-Angle X-ray Scattering (GISAXS) and Atomic Force Microscopy (AFM), we systematically mapped the order–disorder transition (ODT) boundaries and domain spacing evolution across a broad MW range (4.0–101.3 kDa) with varying homopolymer loadings (10% to 90%). The results reveal three distinct regimes: low-MW homopolymers, corresponding to the wet-brush regime produced only gradual domain swelling before disordering at high blend ratios (weight fraction); medium-MW homopolymers, corresponding to thedry-brush regime induced significant domain spacing increase up to 80% followed by earlier disordering, while high-MW homopolymers led to macrophase separation with minimal changes in domain spacing. Additionally, coarse-grained molecular dynamics simulations confirmed our experimental finding that in the low-MW region, the PS homopolymer uniformly distributed in the PS domain. These findings demonstrate that homopolymer molecular weight critically governs both the extent of domain swelling and the onset of disorder in triblock copolymer systems. This high-throughput platform enables the rapid mapping of composition–morphology relationships and can be integrated with AI/ML tools for designing next-generation nanostructured polymers.

36 MATERIALS SCIENCE↗