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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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Final technical report on DOE project conducted at Texas A & M University Dept. of Materials Science and Engineering concerning finding and understanding the design rules for triply periodic, bicontinuous morphological microdomain structures of block copolymers.
Windows have the unique capability of being able to achieve a net zero energy impact by admitting solar gains in the winter to offset thermal losses and admitting daylight to offset electric lighting. If rejection or admission of solar heat gains and daylight are appropriately timed, then heating, cooling and lighting energy use at the perimeter zone can be reduced to net zero energy levels. Nano-scale switchable coatings on glass have been developed to actively modulate solar intensity and spectral transmission. We provide a brief overview of these switchable glazing materials, discuss the desired performance objectives for such materials, and present results from recently completed monitored studies of state-of-the-art switchable windows, particularly with respect to occupant response and market factors. Careful application of state-of-the-art switchable windows and new material science developments on the horizon can deliver the desired net zero energy performance while meeting critical human factors and market related requirements.
Despite more than a century of research, drying shrinkage still causes unwanted stresses and cracks in our concrete infrastructures. This is partly due to the lack of understanding of the underlying mechanisms governing the drying shrinkage in cementitious materials. Inspired by the tremendous impact of atomistic simulations on materials science, we expect such innovative simulation techniques to hold a key to unlocking the enigmatic nature of drying shrinkage at the nanoscale. In this treatise, we first introduce basic physics concepts and atomistic simulation methods in detail. Afterward, we discuss the progress brought by atomistic simulations in understanding the drying shrinkage in cementitious materials and other nanoporous and nanolayered systems. This review also highlights important remaining fundamental questions and practical issues regarding drying shrinkage and delineates how atomistic simulations can help resolve them.
Filled and cross-linked elastomeric rubbers are versatile network materials with a multitude of applications ranging from artificial organs and biomedical devices to cushions, coatings, adhesives, interconnects, and seismic-isolation-, thermal-, and electrical barriers. External factors like mechanical stress, temperature fluctuations, or radiation are known to create chemical changes in such materials that can directly affect the molecular weight distribution (MWD) of the polymer between cross-links and alter the structural and mechanical properties. From a Materials Science point of view it is highly desirable to understand, effect, and manipulate such property changes in a controlled manner. In this report we summarize our modeling efforts on a polysiloxane elastomer TR-55, which is an important component in several of our systems, and representative of a wide class of filled rubber materials. The primary aging driver in this work has been γ-radiation, and a variety of modeling approaches have been employed, including constitutive, mesoscale, and population-based models. The work utilizes diverse experimental data, including mechanical stress-strain and compression set measurements, as well as MWD measurements using multiquantum NMR.
The design of a quantum computer can be broken down into different steps, e.g., the material science aspect of designing qubits and devices, considerations of controlling the state of the qubits and their environment, the computer science aspects of mapping algorithms to the available primitives of the quantum computer, and the programming of an application in terms of the available algorithms. Research in these areas is currently fairly isolated, and there is framework for an end-to-end design approach where a desired application informs the choice of materials for the qubits and their environment, and vice versa.We identify knowledge gaps and opportunities for research that builds on existing PNNL capabilities.
Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.
Here in this work we provide direct evidence of shock-induced melting and associated kinetics in a porous solid (aluminum powder) using time-resolved x-ray diffraction. Unambiguous evidence of melting in 50% porous aluminum (Al) powder samples, shocked to peak pressures between ~13 - 19 GPa, was provided by the broadening of the Debye-Scherrer ring corresponding to the (111) peak. Shocked Al powder did not melt completely in any of our experiments within the durations of measurement. Incomplete (partial) melting of the powder, even after several hundreds of nanoseconds of shock loading, provides insights into thermal transport with Al powder particles under high-pressure dynamic loading. Such insights are quite valuable for developing well-constrained melting models and thermodynamic equations of state for porous Al and other porous solids relevant to planetary and materials science.
This report summarizes progress by an external entity (Prometheus Innovations LLC) that collaborated with PNNL on furthering the commercialization of materials science technologies to improve visibility of underwater netting for fish protection at hydropower dams.
Topological semimetals (TSMs) including Weyl semimetals and nodal-line semimetals are expected to open the next frontier of condensed matter and materials science. Although the first inversion breaking Weyl semimetal was recently discovered in TaAs, its magnetic counterparts, i.e., the time-reversal breaking Weyl and nodal line semimetals, remain elusive. They are predicted to exhibit exotic properties distinct from the inversion breaking TSMs including TaAs. In this paper, we identify the magnetic topological semimetal states in the ferromagnetic half-metal compounds Co 2 TiX (X = Si, Ge, or Sn) with Curie temperatures higher than 350 K. Our first-principles band structure calculations show that, in the absence of spin-orbit coupling, Co 2 TiX features three topological nodal lines. The inclusion of spin-orbit coupling gives rise to Weyl nodes, whose momentum space locations can be controlled as a function of the magnetization direction. Lastly, our results not only open the door for the experimental realization of topological semimetal states in magnetic materials at room temperature, but also suggest potential applications such as unusual anomalous Hall effect in engineered monolayers of the Co 2 TiX compounds at high temperature.
Nanoparticles (NPs) hold tremendous promise for diverse applications infields such as imaging, sensing, nanorobotics, and for optical and electronic materials. These applications often require precisely controlled interactions between multiple NPs or between NPs and other components. Hence, the organization of NPs into composite materials has been an active area of research. DNA origami nanotechnology offers a promising path forward with unparalleled control over complex nanoscale geometry and functionalization, programmed dynamic and mechanical properties, stimulus response to local or externally applied triggers, and capability of assembly into higher-order 1D, 2D, or 3D materials. Furthermore, DNA origami self-assembly is rapid and scalable, overcoming limitations of top-down NP organization methods. In this review, we outline the challenges, recent advances, and opportunities for NP-DNA origami composites. We go into depth on aspects of DNA origami that enhance materials function, such as dynamic actuation, and we discuss practical aspects involved in making NP-DNA composites. Whereas the vast majority of research in NP-DNA origami composite synthesis focuses on gold NPs, these methods can be generalized to other DNA-coated NPs, and therefore more broadly establish a path towards functional NP-DNA origami composites. We envision this review will serve as a guide to materials science and engineering researchers to pursue new materials based on NP-DNA composites.
Infrared (IR) spectroscopy has evolved into a powerful analytical technique to probe molecular and lattice vibrations, low-energy electronic excitations and correlations, and related collective surface plasmon, phonon, or other polaritonic resonances. In combination with scanning probe microscopy, near-field infrared nano-spectroscopy and -imaging techniques have recently emerged as a frontier in imaging science, enabling the study of complex heterogeneous materials with simultaneous nanoscale spatial resolution and chemical and quantum state spectroscopic specificity. Here, we describe synchrotron infrared nano-spectroscopy (SINS), which takes advantage of the low-noise, broadband, high spectral irradiance, and coherence of synchrotron infrared radiation for near-field infrared measurements across the mid- to far-infrared with nanometer spatial resolution. This powerful combination provides a qualitatively new form of broadband spatio-spectral analysis of nanoscale, mesoscale, and surface phenomena that were previously difficult to study with IR techniques, or even any form of micro-spectroscopy in general. We review the development of SINS, describe its technical implementations, and highlight selected examples representative of the rapidly growing range of applications in physics, chemistry, biology, materials science, geology, and atmospheric and space sciences.
With continued penetration of consumer electronics and electric vehicles into the evergrowing marketplace, there is a critical need for an efficient and sustainable battery supply chain. This includes the production of raw materials, the design, development, and processing of cell materials and systems, and end-of-life product recycling. The Oak Ridge National Laboratory (ORNL) has established a research program dedicated to addressing many aspects of these key challenges. Here, we asked Jianlin Li, a Senior R&D Staff and leader of the Energy Storage and Conversion Manufacturing Group, his opinion on the role of chemistry and materials science in mitigating the impacts of critical materials in battery supply chains.
Human enamel is an incredibly resilient biological material, withstanding repeated daily stresses for decades. The mechanisms behind this resilience remain an open question, with recent studies demonstrating a crack-deflection mechanism contributing to enamel toughness and other studies detailing the roles of the organic matrix and remineralization. Here, we focus on the mineral and hypothesize that self-healing of cracks in enamel nanocrystals may be an additional mechanism acting to prevent catastrophic failure. To test this hypothesis, we used a molecular dynamics (MD) approach to compare the fracture behavior of hydroxyapatite (HAP) and calcite, the main minerals in human enamel and sea urchin teeth, respectively. In this work, we find that cracks heal under pressures typical of mastication by fusion of crystals in HAP but not in calcite, which is consistent with the resilience of HAP enamel that calcite teeth lack. Scanning transmission electron microscopy (STEM) images of structurally intact ("sound") human enamel show dashed-line nanocracks that resemble and therefore might be the cracks healed by fusion of crystals produced in silico . The fast, self-healing mechanism shown here is common in soft materials and ceramics but has not been observed in single crystalline materials at room temperature. The crack self-healing in sound enamel nanocrystals, therefore, is unique in the human body and unique in materials science, with potential applications in designing bioinspired materials.
Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity to capture complex relational structures. While machine learning performance has historically improved with larger models and datasets, GNNs for atomistic materials modeling remain relatively small compared to large language models (LLMs), which leverage billions of parameters and terabyte-scale datasets to achieve remarkable performance in their respective domains. To address this gap, we explore the scaling limits of GNNs for atomistic materials modeling by developing a foundational model with billions of parameters, trained on extensive datasets in terabytescale. Our approach incorporates techniques from LLM libraries to efficiently manage large-scale data and models, enabling both effective training and deployment of these large-scale GNN models. This work addresses three fundamental questions in scaling GNNs: the potential for scaling GNN model architectures, the effect of dataset size on model accuracy, and the applicability of LLM-inspired techniques to GNN architectures. Specifically, the outcomes of this study include (1) insights into the scaling laws for GNNs, highlighting the relationship between model size, dataset volume, and accuracy, (2) a foundational GNN model optimized for atomistic materials modeling, and (3) a GNN codebase enhanced with advanced LLM-based training techniques. Our findings lay the groundwork for large-scale GNNs with billions of parameters and terabyte-scale datasets, establishing a scalable pathway for future advancements in atomistic materials modeling.
Strains play a pivotal role in determining the phase equilibrium, domain configuration, and functional properties of the low-dimensional ferroelectrics. There is growing interest in the strain engineering of ferroelectric K x Na 1- x NbO 3 (KNN) epitaxial thin films, which exhibit excellent physical properties and promise as eco-friendly alternatives to lead-based ferroelectrics for microdevice applications. Further, advances have been made in understanding the phase equilibria and transitions, domains and domain walls, and their relations to the physical properties of KNN epitaxial thin films using a combination of experiments and theoretical modeling, particularly phase-field simulations. Here, we review recent progress in these aspects and showcase the phase-field method for establishing strain phase diagrams, elucidating the domain and domain wall structures at equilibrium, and predicting the structure–property relationships in ferroelectric KNN thin films. We also discuss challenges and opportunities to further advance our understanding of KNN thin films and potentially unlock new functionalities by leveraging phase-field simulations.
Realizing the promise of fusion as a commercially attractive energy source requires the development of advanced structural materials capable of sustained operation in an incredibly hostile environment. The fusion environment presents many challenges associated with high cyclic thermal-mechanical loadings, welding and joining disparate materials, and achieving chemical compatibility with coolants and tritium breeders. Yet, the overarching concern is the degradation of physical and mechanical properties, resulting from a neutron energy spectrum peaked at 14 MeV. The high-energy fusion neutron irradiation produces both displacement damage and high levels of hydrogen and helium through transmutation reactions. Advanced materials development for use in such a hostile environment is predicated on understanding the underlying mechanisms responsible for physical and material property degradation. This project has closely combined computational, theoretical and experimental techniques within a multiscale materials science paradigm to determine the mechanisms controlling material degradation in the fusion environment.
After five decades of nuclear power operation encompassing more than 20,000 reactor-years across 35 countries and 647 reactors, zero double-ended guillotine breaks (DEGBs) have been documented in commercial reactor coolant systems—despite DEGB being the fundamental design-basis assumption driving Emergency Core Cooling System (ECCS) sizing, structural protection requirements, and containment design specifications. This report examines the basis for DEGB requirements in nuclear power plant design. The DEGB postulate assumes the instantaneous, complete circumferential severance of the largest diameter pipes in reactor coolant systems, driving major design requirements under 10 Code of Federal Regulations 50.46, General Design Criterion 4 and containment design specifications. The United States (4,880 reactor-years) and France (2,505 reactor-years) contribute the largest operational datasets. Probabilistic assessments estimate direct DEGB occurrence probabilities with extremely low event frequencies, far below the 10-5/reactor-year thresholds typically used to define non-credible events in nuclear-safety analyses; i.e., events with probability this low fall into beyond-design-basis events. Current material-science knowledge demonstrates that the ductile steel materials used in nuclear piping systems exhibit stable crack-growth behavior fundamentally incompatible with instantaneous severance. International regulatory experience, particularly Germany’s comprehensive break-preclusion implementation, and successful leak-before-break (LBB) applications in almost all of U.S. pressurized water reactor units validate that alternatives can maintain safety performance while reducing economic burden. Current DEGB protection systems impose estimated lifetime costs of hundreds of millions of dollars per unit, over the life of a plant across the nuclear industry (including ongoing costs), representing substantial resource allocation toward scenarios with extremely low probability. Although this report acknowledges uncertainties regarding long-term aging effects, potential synergistic degradation mechanisms, and site-specific seismic considerations that warrant continued evaluation as regulatory policy evolves, there remains no documented evidence that a DEGB has occurred as a consequence of the conditions or mechanisms described in this report. This report acknowledges the Nuclear Regulatory Commission’s (NRC’s) recent efforts—outlined in the draft Interim Staff Guidance (ISG) NRC-DSS-ISG-2025-XX (“Treatment of Certain Loss-of-Coolant Accident Locations as Beyond-Design-Basis Accidents Draft Interim Staff Guidance”)—to reduce overly conservative requirements for large-break loss of coolant accidents through technical justifications and exemptions. However, extensive operating experience and validated methodologies—such as LBB and in-service inspection programs—demonstrate that the probability of a DEGB in reactor coolant-loop piping is extremely low, even under seismic conditions. The authors and reviewers of this report recommend that DEGB be removed as a design-basis event through formal rulemaking, rather than case-by-case exemptions, to better reflect credible failure modes, align with current data, and align with modern, risk-informed safety analysis.
Containerless melting, reaction, and solidification experiments and processes which potentially can lead to new understanding of material science and production of new or improved materials in the weightless space environment are reviewed in terms of planning for spacelab. Most of the experiments and processes discussed are amenable to the employment of electromagnetic position control and electromagnetic induction or electron beam heating and melting. The spectrum of relevant properties of materials, which determine requirements for a space laboratory electromagnetic containerless processing facility are reviewed. Appropriate distributions and associated coil structures are analyzed and compared on the basis of efficiency, for providing the functions of position sensing, control, and induction heating. Several coil systems are found capable of providing these functions. Exchangeable modular coils in appropriate sizes are recommended to achieve the maximum power efficiencies, for a wide range of specimen sizes and resistivities, in order to conserve total facility power.