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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Progressive collapse analysis of stiffened panel structure with consideration of actual welding distortion and residual stresses

To date, inclusion of welding distortion and residual stresses in determining the strength of marine structures is based on empirical formulations due to welding complexity. In this study, a more feasible numerical model was developed from the perspective of inherent strain theory. The accuracy of this simplified numerical model was confirmed by transient thermo-elastic–plastic finite element analysis using a novel dynamic mesh refining and iterative substructure method which is 50 times faster than conventional finite element method. Welding distortion by the two numerical approaches was validated with experimental measurement. The collapse analyses indicated that the inherent strain model predicted a compressive strength and load-displacement curve with high accuracy, as validated by the transient thermo-mechanical model. The global stiffness and the ultimate strength of the welded structure would be overestimated by about 22% if no welding effects were considered. The proposed computation procedure for determining the ultimate strength of welded structures is accurate, simple, and practical and enables improvement of ultimate strength through optimization of welding joint design to reduce the welding residual stress and distortion.

Huang, Hui↗
DOE OSTI2017

Crystal structure of Mdm12 and combinatorial reconstitution of Mdm12/Mmm1 ERMES complexes for structural studies

Membrane contact sites between organelles serve as molecular hubs for the exchange of metabolites and signals. In yeast, the Endoplasmic Reticulum – Mitochondrion Encounter Structure (ERMES) tethers these two organelles likely to facilitate the non-vesicular exchange of essential phospholipids. Present in Fungi and Amoebas but not in Metazoans, ERMES is composed of five distinct subunits; among those, Mdm12, Mmm1 and Mdm34 each contain an SMP domain functioning as a lipid transfer module. We previously showed that the SMP domains of Mdm12 and Mmm1 form a hetero-tetramer. Here we describe our strategy to diversify the number of Mdm12/Mmm1 complexes suited for structural studies. We use sequence analysis of orthologues combined to protein engineering of disordered regions to guide the design of protein constructs and expand the repertoire of Mdm12/Mmm1 complexes more likely to crystallize. Using this combinatorial approach we report crystals of Mdm12/Mmm1 ERMES complexes currently diffracting to 4.5 Å resolution and a new structure of Mdm12 solved at 4.1 Å resolution. Our structure reveals a monomeric form of Mdm12 with a conformationally dynamic N-terminal β-strand; it differs from a previously reported homodimeric structure where the N-terminal β strands where swapped to promote dimerization. Based on our electron microscopy data, we propose a refined pseudo-atomic model of the Mdm12/Mmm1 complex that agrees with our crystallographic and small-angle X-ray scattering (SAXS) solution data. - Highlights: • We solved a 4.1 Å resolution crystal structure of Mdm12, the soluble subunit of ERMES. • The two Mdm12 apo monomers differ in the conformation of their N-terminal β-strands. • We reconstituted and crystallized two distinct Mdm12/Mmm1 complexes. • SAXS analysis of the complexes agrees with our previous electron microscopy data. • The structure suggests a head-to-tail association in the Mdm12/Mmm1 complex.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Mapping Thermal Conductivity at the Atomic Scale: A Step toward the Thermal Design of Materials

We describe a spatial decomposition of the thermal conductivity, termed site-projected thermal conductivity, which quantifies the thermal conduction activity at each atomic site—a critical parameter for the thermal design of materials. The method is based on the Green–Kubo formula and the harmonic approximation and requires the force-constant and dynamical matrices, as well as a relaxed structural model. Throughout the study, it uses high quality models previously tested and compared to many experiments. It discusses the method and underlying approximations for amorphous silicon, carries the detailed analysis for amorphous silicon, and then examines an amorphous-crystal silicon interface and representative carbon materials. This study identifies the sites and local structures that reduce heat transport, and quantify these (estimate the spatial range) over which these “thermal defects” are effective. It identifies filamentary structures in the amorphous silicon network which impact heat transport and electronic structure (the Urbach edge) and electronic transport.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Hollow-structured Ni-N-C catalysts for highly selective CO 2 electroreduction

Atomically dispersed single-atom catalysts have emerged as promising non-precious catalyst alternatives to expensive Ag and Au catalysts for electrochemical CO 2 reduction reaction (CO 2 RR). In particular, nickel-nitrogen-carbon (Ni-N-C) catalysts have demonstrated a high faradaic efficiency (FE) toward CO formation at low overpotentials. Nonetheless, the exact nature of Ni active sites under CO 2 RR remains elusive and conventional Ni-N-C catalysts are limited by microporosity and low density of Ni single atoms, hindering performance in CO 2 electrolyzers. Here, we report the synthesis of hollow-structured Ni-N-C ( hs -Ni-N-C) catalysts via a post-synthesis modification (PSM) strategy using partial ligand exchange of 2-methylimidazole with 3-amino-1,2,4-triazole. This approach enables the formation of a hollow structure, resulting in more than a twofold increase in Ni atom density compared to regular Ni-N-C (r-Ni-N-C). In a zero-gap CO 2 electrolyzer, the optimized hs -Ni-N-C allows for achieving an FE CO of 97% at a current density of > 100 mA cm⁻ 2 , while maintaining high CO selectivity with stable performance over 100 h at 2.5 V. hs-Ni-N-C shows a more than sevenfold increase in the CO partial current density relative to r-Ni-N-C resulting from the combined effects of a higher density of Ni single-atom sites, improved kinetics, and lower transport resistance under the operating conditions, as indicated by electrochemical impedance spectra and distribution of relaxation times analysis. Operando high energy-resolution X-ray absorption spectroscopy (XAS) reveals that atop-bonded CO on Ni single sites induces dynamic transformations of the Ni–N coordination environment, leading to a symmetric coordination structure of hs -Ni-N-C. Under CO 2 RR, the catalysts undergo a more pronounced structural change and form a minor fraction of Ni nanoparticles. Density functional theory calculations are consistent with the XAS results and provide molecular insights showing that the interplay between protonation and CO adsorption leads to adsorbate-induced restructuring of the Ni single atom. This work demonstrates the synergistic role of hollow structure and high-density Ni atoms in governing CO 2 RR selectivity and provides mechanistic insights into the structural dynamics of single-atom catalysts under operating conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unveiling the nanoscale architectures and dynamics of protein assembly with in situ atomic force microscopy

Proteins play a vital role in different biological processes by forming complexes through precise folding with exclusive inter- and intra-molecular interactions. Understanding the structural and regulatory mechanisms underlying protein complex formation provides insights into biophysical processes. Furthermore, the principle of protein assembly gives guidelines for new biomimetic materials with potential applications in medicine, energy, and nanotechnology. Atomic force microscopy (AFM) is a powerful tool for investigating protein assembly and interactions across spatial scales (single molecules to cells) and temporal scales (milliseconds to days). It has significantly contributed to understanding nanoscale architectures, inter- and intra-molecular interactions, and regulatory elements that determine protein structures, assemblies, and functions. This review describes recent advancements in elucidating protein assemblies with in situ AFM. We discuss the structures, diffusions, interactions, and assembly dynamics of proteins captured by conventional and high-speed AFM in near-native environments and recent AFM developments in the multimodal high-resolution imaging, bimodal imaging, live cell imaging, and machine-learning-enhanced data analysis. These approaches show the significance of broadening the horizons of AFM and enable unprecedented explorations of protein assembly for biomaterial design and biomedical research.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The emergence and transmission dynamics of HIV-1 CRF07_BC in Mainland China

A total of 1155 partial pol gene sequences of human immunodeficiency virus (HIV)-1 CRF07_BC were sampled between 1997 and 2015, spanning 13 provinces in Mainland China and risk groups [heterosexual, injecting drug users (IDU), and men who have sex with men (MSM)] to investigate the evolution, adaptation, spatiotemporal and risk group dynamics, migration patterns, and protein structure of HIV-1 CRF07_BC. Due to the unequal distribution of sequences across time, location, and risk group in the complete dataset (‘full1155’), subsampling methods were used. Maximum-likelihood and Bayesian phylogenetic analysis as well as discrete trait analysis of geographical location and risk group were carried out. To study mutations of a cluster of HIV-1 CRF07_BC (CRF07-1), we performed a comparative analysis of this cluster to the other CRF07_BC sequences (‘backbone_295’) and mapped the mutations observed in the respective protein structure. Our findings showed that HIV-1 CRF07_BC most likely originated among IDU in Yunnan Province between October 1992 to July 1993 [95 per cent hightest posterior density (HPD): May 1989–August 1995] and that IDU in Yunnan Province and MSM in Guangdong Province likely served as the viral sources during the early and more recent spread in Mainland China. We also revealed that HIV-1 CRF07-1 has been spreading for roughly 20 years and continues to cause local transmission in Mainland China and worldwide. Overall, our study sheds light on the dynamics of HIV-1 CRF07_BC distribution patterns in Mainland China. Our research may also be useful in formulating public health policies aimed at controlling acquired immune deficiency syndrome in Mainland China and globally.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Leveraging Optimal Sparse Sensor Placement to Aggregate a Network of Digital Twins for Nuclear Subsystems

Nuclear power plants (NPPs) require continuous monitoring of various systems, structures, and components to ensure safe and efficient operations. The critical safety testing of new fuel compositions and the analysis of the effects of power transients on core temperatures can be achieved through modeling and simulations. They capture the dynamics of the physical phenomenon associated with failure modes and facilitate the creation of digital twins (DTs). Accurate reconstruction of fields of interest (e.g., temperature, pressure, velocity) from sensor measurements is crucial to establish a two-way communication between physical experiments and models. Sensor placement is highly constrained in most nuclear subsystems due to challenging operating conditions and inherent spatial limitations. This study develops optimized data-driven sensor placements for full-field reconstruction within reactor and steam generator subsystems of NPPs. Optimized constrained sensors reconstruct field of interest within a tri-structural isotropic (TRISO) fuel irradiation experiment, a lumped parameter model of a nuclear fuel test rod and a steam generator. The optimization procedure leverages reduced-order models of flow physics to provide a highly accurate full-field reconstruction of responses of interest, noise-induced uncertainty quantification and physically feasible sensor locations. Accurate sensor-based reconstructions establish a foundation for the digital twinning of subsystems, culminating in a comprehensive DT aggregate of an NPP.

42 ENGINEERING↗
DOE OSTI2025

All-Atom Modeling and Simulation of Biopolymer Interface: Dual Role of Antifouling Polymer Brushes

Antifouling polymer brushes are well-known for their exceptional resistance to unwanted protein adsorption. While experimental studies have extensively characterized protein–polymer brush interactions, computational investigations remain limited, largely due to the challenges in accurately modeling and integrating these complex, multicomponent systems without resorting to oversimplification. To address this challenge, this study presents one of the most comprehensive and realistic model systems to date, comprising the substrate, grafted polymers, and proteins. Our work interprets the interactions between polymer brush and protein based on realistic modeling without simplification. In particular, this study utilizes molecular modeling and simulation of polycationic and polyzwitterionic brushes─poly(dimethylaminoethyl methacrylate) (PDMAEMA), poly(2-(N-oxide-N,N-dimethylamino)ethyl methacrylate) (PNOMA), and poly(2-(N-3-sulfopropyl-N,N-dimethylammonium)ethyl methacrylate) (PSBMA)─grafted onto α-quartz substrates via polymerization initiator linkers. The brush models were developed to closely replicate experimentally synthesized samples and to provide detailed insights into structural and dynamical changes at the molecular level during protein adsorption. Using steered molecular dynamics simulations, we show that the PSBMA brush, due to its high local density, exhibits the greatest resistance to protein insertion. Cα root-mean-square deviation and interaction pattern analyses further reveal that the PSBMA brush also induces the most significant destabilization of lysozyme, while the PDMAEMA brush enhances protein stability through ion-mediated interactions. The PNOMA brush, while requiring the lowest force for protein adsorption, induces greater protein destabilization than the PDMAEMA brush, primarily due to electrostatic repulsion caused by a short carbon spacer length. Hydration analysis reveals that both the PSBMA brush and the lysozyme interacting with it exhibit the most rapid dehydration, attributed to the brush’s high local chain density, which results in the greatest lysozyme destabilization and the highest adsorption force. These findings highlight the dual role of antifouling polymer brushes: resisting protein adsorption and modulating protein structural dynamics. In conclusion, this study provides valuable insights for the rational design of next-generation antifouling materials and offers a framework for realistic model development in complex multicomponent systems.

Adsorption↗
DOE OSTI2017

Recordings and Analysis of Atomic Ledge and Dislocation Movements in InGaAs to Nickelide Nanowire Phase Transformation

The formation of low resistance and self‐aligned contacts with thermally stable alloyed phases is a prerequisite for realizing reliable functionality in ultrascaled semiconductor transistors. Detailed structural analysis of the phase transformation accompanying contact alloying can facilitate contact engineering as transistor channels approach a few atoms across. Original in situ heating transmission electron microscopy studies are carried out to record and analyze the atomic scale dynamics of contact alloy formation between Ni and In 0.53 Ga 0.47 As nanowire channels. It is observed that the nickelide reacts on the In 0.53 Ga 0.47 As (111) || Ni 2 In 0.53 Ga 0.47 As (0001) interface with atomic ledge propagation along the Ni 2 In 0.53 Ga 0.47 As direction. Ledges nucleate as a train of strained single‐bilayers and propagate in‐plane as double‐bilayers that are associated with a misfit dislocation of . The atomic structure is reconstructed to explain this phase transformation that involves collective gliding of three Shockley partials in In 0.53 Ga 0.47 As lattice to cancel out shear stress and the formation of misfit dislocations to compensate the large lattice mismatch in the newly formed nickelide phase and the In 0.53 Ga 0.47 As layers. This work demonstrates the applicability of interfacial disconnection (ledge + dislocation) theory in a nanowire channel during thermally induced phase transformation that is typical in metal/III–V semiconductor reactions.

Chen, Renjie↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2015

A Client/Server Architecture for Supporting Science Data Using EPICS Version 4

The Phase 1 grant that serves as a precursor to this proposal, prototyped complex storage techniques for high speed structured data that is being produced in accelerator diagnostics and beam line experiments. It demonstrates the technologies that can be used to archive and retrieve complex data structures and provide the performance required by our new accelerators, instrumentations, and detectors. Phase 2 is proposed to develop a high-performance platform for data acquisition and analysis to provide physicists and operators a better understanding of the beam dynamics. This proposal includes developing a platform for reading 109 MHz data at 10 KHz rates through a multicore front end processor, archiving the data to an archive repository that is then indexed for fast retrieval. The data is then retrieved from this data archive, integrated with the scalar data, to provide data sets to client applications for analysis, use in feedback, and to aid in identifying problem with the instrumentation, plant, beam steering, or model. This development is built on EPICS version 4 , which is being successfully deployed to implement physics applications. Through prior SBIR grants, EPICS version 4 has a solid communication protocol for middle layer services (PVAccess), structured data representation and methods for efficient transportation and access (PVData), an operational hierarchical record environment (JAVA IOC), and prototypes for standard structured data (Normative Types). This work was further developed through project funding to successfully deploy the first service based physics application environment with demonstrated services that provide arbitrary object views, save sets, model, lattice, and unit conversion. Thin client physics applications have been developed in Python that implement quad centering, orbit display, bump control, and slow orbit feedback. This service based architecture has provided a very modular and robust environment that enables commissioning teams to rapidly develop and deploy small scripts that build on powerful services. These services are all built on relational database data stores and scalar data. The work proposed herein, builds on these previous successes to provide data acquisition of high speed data for online analysis clients.

43 PARTICLE ACCELERATORS↗
DOE OSTI2018

A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions

Recent developments in the ability to automatically and efficiently extract natural frequencies, damping ratios, and full-field mode shapes from video of vibrating structures has great potential for reducing the resources and time required for performing experimental and operational modal analysis at very high spatial resolution. Furthermore, these techniques have the added advantage that they can be implemented remotely and in a non-contact fashion. Emerging full-field imaging techniques therefore have potential to allow the identification of the modal properties of structures in regimes that used to be challenging. For instance, these techniques suggest that the high spatial resolution structural identification could be performed on an aircraft during flight using a ground or aircraft-based imager. They also have the potential to identify the dynamics of microscopic systems. In order to realize this capability it will be necessary to develop techniques that can extract full-field structural dynamics in the presence of non-ideal operating conditions. In this work, we develop a framework for the deployment of emerging algorithms that allow the automatic extraction of high-resolution, full-field modal parameters in the presence of non-ideal operating conditions. One of the most notable non-ideal operating conditions is the rigid body motion of both the structure being measured as well as the imager performing the measurement. We demonstrate an instantiation of the framework by showing how it can be used to address, in-plane, translational, rigid body motion. The development of a frame-to-frame keypoint–based technique for identifying full-field structural dynamics in the presence of either rigid body motion is presented and demonstrated in the context of the framework for the deployment of full-field structural identification techniques in the presence of non-ideal operating conditions. It is expected that this framework will ultimately help enable the collection of full-field structural dynamics using measurement platforms including unmanned aerial vehicles, robotic telescopes, satellites, imagers mounted in high-vibration environments (seismic, industrial, harsh weather), characterization of microscopic structures, and human-carried imagers. If imager-based structural identification techniques mature to the point that they can be used in non-ideal field conditions, it could open up the possibility that the structural health monitoring community will be able to think beyond monitoring individual structures, to full-field structural integrity monitoring at the city scale.

Dasari, Sudeep↗
DOE OSTI2015

Analysis of Strand-Specific RNA-Seq Data Using Machine Learning Reveals the Structures of Transcription Units in Clostridium thermocellum

The identification of transcription units (TUs) encoded in a bacterial genome is essential to elucidation of transcriptional regulation of the organism. To gain a detailed understanding of the dynamically composed TU structures, we have used four strand-specific RNA-seq (ssRNA-seq) datasets collected under two experimental conditions to derive the genomic TU organization of Clostridium thermocellum using a machine-learning approach. Our method accurately predicted the genomic boundaries of individual TUs based on two sets of parameters measuring the RNA-seq expression patterns across the genome: expression-level continuity and variance. A total of 2590 distinct TUs are predicted based on the four RNA-seq datasets. Moreover, among the predicted TUs, 44% have multiple genes. We assessed our prediction method on an independent set of RNA-seq data with longer reads. The evaluation confirmed the high quality of the predicted TUs. Functional enrichment analyses on a selected subset of the predicted TUs revealed interesting biology. To demonstrate the generality of the prediction method, we have also applied the method to RNA-seq data collected on Escherichia coli and achieved high prediction accuracies. The TU prediction program named SeqTU is publicly available athttps://code.google.com/p/seqtu/. We expect that the predicted TUs can serve as the baseline information for studying transcriptional and post-transcriptional regulation in C. thermocellum and other bacteria.

09 BIOMASS FUELS↗
DOE OSTI2025

Influence of isoelectronic mass modulation on phonon decay and transport in layered pnictogen-carbon systems

This study systematically investigates the structural, electronic, and thermal transport properties of layered pnictogen–carbon (Pn 2 C 2 ; Pn = P, As, Sb, Bi) monolayers, revealing a profound influence of isoelectronic mass modulation on phonon dynamics and thermal conductivity. Through first-principles calculations and lattice dynamics analysis, we demonstrate that increasing the atomic mass of pnictogens leads to elongated Pn-C bonds, weakened interatomic interactions, and enhanced phonon-phonon scattering, resulting in a dramatic reduction in lattice thermal conductivity (κ). Specifically, κ decreases exponentially from 65.6 W/m-K in P 2 C 2 to an ultralow 0.37 W/m-K in Bi 2 C 2 , driven by suppressed acoustic-optical phonon gaps Δ$^{Γ}_{A-0}$, increased anharmonicity, and reduced phonon lifetimes and group velocities. The transition from semiconducting to metallic behavior in heavier pnictogen-based systems further enhances phonon-electron scattering, contributing to thermal conductivity suppression. Structural analysis highlights the strengthening of out-of-plane C—C bonds and the contraction of C—Pn—C bond angles, which disrupt in-plane phonon transport. Further, these findings establish a design framework for engineering ultralow-κ materials through chemical substitution and structural tuning, offering significant potential for thermoelectric and thermal management applications. This work provides fundamental insights into phonon decay mechanisms and thermal transport in 2D materials, paving the way for advanced phononic and energy-efficient devices.

2D materials↗
DOE OSTI2015

Monte Carlo generators for studies of the 3D structure of the nucleon

In this study, extraction of transverse momentum and space distributions of partons from measurements of spin and azimuthal asymmetries requires development of a self consistent analysis framework, accounting for evolution effects, and allowing control of systematic uncertainties due to variations of input parameters and models. Development of realistic Monte-Carlo generators, accounting for TMD evolution effects, spin-orbit and quark-gluon correlations will be crucial for future studies of quark-gluon dynamics in general and 3D structure of the nucleon in particular.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Prediction of B1 to B10 phase transition in LuN under pressure: An ab-initio investigation

Ab-initio total energy calculations have been performed in lutetium nitride (LuN) as a function of hydrostatic compression to understand the high pressure behavior of this compound. Our calculations predict a phase transition from ambient rocksalt type structure (B1 phase) to a tetragonal structure (B10 phase) at ~ 240 GPa. The phase transition has been identified as first order in nature with volume discontinuity of ~ 6%. The predicted high pressure phase has been found to be stable up to at least 400 GPa, the maximum pressure up to which calculations have been performed.Further, to substantiate the results of static lattice calculations analysis of lattice dynamic stability of B1 and B10 phase has been carried out at different pressures. Apart from this, we have analyzed the lattice dynamic stability CsCl type (B2) phase around the 240 GPa, the pressure reported for B1 to B2 transition in previous all-electron calculations by Gupta et al. 2013. We find that the B2 structure is lattice dynamically unstable at this pressure and remains unstable up to ~ 400 GPa, ruling out the possibility of B1 to B2 phase transition at least up to ~ 400 GPa. Further, the theoretically determined equation of state has been utilized to derive various physical quantities such as zero pressure equilibrium volume, bulk modulus, and pressure derivative of bulk modulus of B1 phase at ambient conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Hygrothermal behavior of polybenzimidazole

Poly[2,2’-(m-phenylene)-5,5’-bibenzimidazole] (PBI) is used in extremely high temperature harsh environment applications. It is a unique engineering material that is formed into parts by powder-sintering at temperatures as high as 500 °C. Recently, ever increasing demands for high temperature polymers have led to significant interest in PBI such that engineering guidelines could be established for its application in high temperature and highly humid environments. The goal of this work was to understand the material science of PBI in hot-wet environments at temperatures up to 288 °C. Thermal gravimetric analysis and mass spectrometry were employed to identify the degraded volatile products. The molecular scale damping behavior of PBI was probed using dynamic mechanical analysis. The changes in tensile properties and fracture toughness due to environmental exposure were also characterized. Upon heating above 250 °C, moisture-containing PBI exhibits obvious molecular structure change. Evidence of crosslinking and degradation is observed. With 288 °C hot water treatment severe degradation of PBI is observed. As a result, fundamental structure-property relationships of PBI affected by these higher temperature, high moisture content environments are discussed.

36 MATERIALS SCIENCE↗