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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Nanopores with dynamic pore opening diameter

Solid state nanopores have emerged as model systems for understanding transport properties on the nanoscale. They serve as templates for both preparing mimics of biological channels and designing biological sensors. Unlike their biological inspirations, the majority of nanopores prepared thus far, however, have been structurally static devices such that the pore opening diameter is fixed. If we could prepare nanopores whose opening diameter fluctuated in time with controlled amplitude at known locations in the pore, we could create ionic memristors as well as achieve new transport modes. Here we present ∼10 nm diameter single nanopores drilled through a 10 nm thick gold layer positioned on top of a 30 nm thick silicon nitride film. Two types of devices were prepared; one containing single stranded DNA and the other containing hairpin DNA attached to the discrete layer of gold using thiol chemistry. When an external electric field was applied across a nanopore with single stranded DNA, the nanoconfined DNA molecules exhibited steric and electrical constraints that led to memristor-like behavior in the current–voltage curves. The degree of hysteresis was controlled by salt concentration, magnitude of voltage and pore diameter. In contrast, nanopores containing DNA hairpins conducted similar currents in forward and reverse bias in agreement with the rigidity of the hairpin molecule. The experiments are explained by Brownian dynamics simulations that reveal voltage and salt concentration induced changes in DNA extension. The degree of DNA extension was also found to be dependent on the location of the molecules along the pore axis. The nanopores presented here provide the first steps towards preparation of non-equilibrium nanopore systems.

Vlassiouk, Ivan [ORNL] (ORCID:0000000254940386)↗
DOE OSTI2026

Biochemical approaches for synthesis of performance-advantaged polymers from lignocellulosic biomass

Lignocellulose is an abundant renewable feedstock for production of sustainable fuels, chemicals, and materials. The structural complexity of lignocellulose provides key material properties and inspires the design of advanced materials. However, this same complexity also presents challenges for conversion of lignocellulosic biomass into new materials with consistent properties. Conventional physical and chemical strategies for valorization of biomass to new materials are often limited by the technical challenges of precisely manipulating complex feedstocks, sensitivity to feedstock variability, and associated costs. In contrast, biological approaches are capable of selectively manipulating complex architectures under mild conditions. Recent advances demonstrate the potential of biological and hybrid biochemical methods to tailor biomass-derived polymers and generate new materials. This review provides an overview of biological strategies to valorize lignocellulosic biomass into novel materials, highlighting approaches for in planta engineering, biochemical modification of natural biomass polymers, microbial funneling of deconstructed biomass, and direct biosynthesis of novel polymers. In combination, these approaches open new avenues for the synthesis of performance-advantaged materials from lignocellulosic biomass.

Qian, Liangyu [ORNL] (ORCID:0009000212029938)↗
DOE OSTI2024

Boosting RDataFrame performance with transparent bulk event processing

RDataFrame is ROOT’s high-level interface for Python and C++ data analysis. Since it first became available, RDataFrame adoption has grown steadily and it is now poised to be a major component of analysis software pipelines for LHC Run 3 and beyond. Thanks to its design inspired by declarative programming principles, RDataFrame enables the development of highperformance, highly parallel analyses without requiring expert knowledge of multi-threading and I/O: user logic is expressed in terms of self-contained, small computation kernels tied together by a high-level API. This design completely decouples analysis logic from its actual execution, and opens several interesting avenues for workflow optimization. In particular, in this work we explore the benefits of moving internal data processing from an event-by-event to a bulkby-bulk loop. This refactoring dramatically reduces the framework’s runtime overheads; in collaboration with the I/O layer it improves data access patterns; it exposes information that optimizing compilers might use to auto-vectorize the invocation of user-defined computations; finally, while existing user-facing interfaces remain unaffected, it becomes possible to additionally offer interfaces that explicitly expose bulks of events, useful e.g. for the injection of GPU kernels into the analysis workflow. In order to inform similar future R&D, design challenges will be presented, as well as an investigation of the relevant timememory trade-off backed by novel performance benchmarks.

Guiraud, Enrico↗
DOE OSTI2023

Software Citation in HEP: Current State and Recommendations for the Future

In November 2022, the HEP Software Foundation and the Institute for Research and Innovation for Software in High-Energy Physics organized a workshop on the topic of Software Citation and Recognition in HEP. The goal of the workshop was to bring together different types of stakeholders whose roles relate to software citation, and the associated credit it provides, in order to engage the community in a discussion on: the ways HEP experiments handle citation of software, recognition for software efforts that enable physics results disseminated to the public, and how the scholarly publishing ecosystem supports these activities. Reports were given from the publication board leadership of the ATLAS, CMS, and LHCb experiments and HEP open source software community organizations (ROOT, Scikit-HEP, MCnet), and perspectives were given from publishers (Elsevier, JOSS) and related tool providers (INSPIRE, Zenodo). This paper summarizes key findings and recommendations from the workshop as presented at the 26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Engage the Public in Science and Embrace Future Change with Human-Centric Stories, Art, and Imaginings

Human-centric stories that weave in real scientific data may be able to engage the public in environmental issues that do not yet directly affect them. Science fiction and artistically rendered futuristic scenarios can unleash the imagination and act as a lens to envision technological, social, and cultural aspects of transitioning to clean energy. Cultures with strong oral traditions use stories to record history, develop a shared identity, pass on environmental lessons, and prepare for future change. Through the lenses of our non-physical science disciplines (cultural anthropology and environmental sociology), we discuss challenges to communicating science, the use of narratives to illustrate ecosystem processes, and we report on a collaborative, interdisciplinary workshop and book project that is creating narratives of hope and visions for the future through inspiring art, short stories, and essays. We explore the surprising potential of 'cli-fi,' humor, games, and the research behind emotion and imagination-driven engagement. We describe methods that help people visualize their future well-being and we explore opportunities to spread those methods.

clean energy↗
DOE OSTI2020

Gate field effects on the topological insulator BiSbTeSe 2 interface

Interfaces between two topological insulators are of fundamental interest in condensed matter physics. Inspired by experimental efforts, we study interfacial processes between two slabs of BiSbTeSe 2 (BSTS) via first principles calculations. Topological surface states are absent for the BSTS interface in its equilibrium separation, but our calculations show that they appear if the inter-slab distance is greater than 6 Å. More importantly, we find that topological interface states can be preserved by inserting two or more layers of hexagonal boron nitride between the two BSTS slabs. In experiments, the electric current tunneling through the interface is insensitive to back gate voltage when the bias voltage is small. Using a first-principles based method that allows us to simulate the gate field, we show that at low bias, the extra charge induced by a gate voltage resides on the surface that is closest to the gate electrode, leaving the interface almost undoped. This provides clues to understand the origin of the observed insensitivity of transport properties to back voltage at low bias. Our study resolves a few questions raised in experiment, which does not yet offer a clear correlation between microscopic physics and transport data. We provide a road map for the design of vertical tunneling junctions involving the interface between two topological insulators.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A feasible path for the use of ferromagnetic josephson junctions in quantum circuits: The ferro-transmon

We discuss the capabilities of ferromagnetic (F) Josephson junctions (JJs) in a variety of layouts and configurations. The main goal is to demonstrate the potential of these hybrid JJs to disclose new physics and the possibility to integrate them in superconducting classical and quantum electronics for various applications. The feasible path towards the use of ferromagnetic Josephson junctions in quantum circuits starts from experiments demonstrating macroscopic quantum tunneling in NbN/GdN/NbN junctions with ferro-insulator barriers and with triplet components of the supercurrent, supported by a self-consistent electrodynamic characterization as a function of the barrier thickness. This has inspired further studies on tunnel ferromagnetic junctions with a different layout and promoted the first generation of ferromagnetic Al-based JJs, specifically Al/AlO x /Al/Py/Al. This layout takes advantage of the capability to integrate the ferromagnetic layer in the junction without affecting the quality of the superconducting electrodes and of the tunnel barrier. The high quality of the devices paves the way for the possible implementation of Al tunnel-ferromagnetic JJs in superconducting quantum circuits. These achievements have promoted the notion of a novel type of qubit incorporating ferromagnetic JJs. This qubit is based on a transmon design featuring a tunnel JJ in parallel with a ferromagnetic JJ inside a SQUID loop capacitively coupled to a superconducting readout resonator. In conclusion, the effect of an external RF field on the magnetic switching processes of ferromagnetic JJs has been also investigated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Reproducibility of protein x-ray diffuse scattering and potential utility for modeling atomic displacement parameters

Protein structure and dynamics can be probed using x-ray crystallography. Whereas the Bragg peaks are only sensitive to the average unit-cell electron density, the signal between the Bragg peaks—diffuse scattering—is sensitive to spatial correlations in electron-density variations. Although diffuse scattering contains valuable information about protein dynamics, the diffuse signal is more difficult to isolate from the background compared to the Bragg signal, and the reproducibility of diffuse signal is not yet well understood. We present a systematic study of the reproducibility of diffuse scattering from isocyanide hydratase in three different protein forms. Both replicate diffuse datasets and datasets obtained from different mutants were similar in pairwise comparisons (Pearson correlation coefficient ≥0.8). The data were processed in a manner inspired by previously published methods using custom software with modular design, enabling us to perform an analysis of various data processing choices to determine how to obtain the highest quality data as assessed using unbiased measures of symmetry and reproducibility. The diffuse data were then used to characterize atomic mobility using a liquid-like motions (LLM) model. This characterization was able to discriminate between distinct anisotropic atomic displacement parameter (ADP) models arising from different anisotropic scaling choices that agreed comparably with the Bragg data. Our results emphasize the importance of data reproducibility as a model-free measure of diffuse data quality, illustrate the ability of LLM analysis of diffuse scattering to select among alternative ADP models, and offer insights into the design of successful diffuse scattering experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

The structural information filtered features (SIFF) potential: Maximizing information stored in machine-learning descriptors for materials prediction

Machine learning inspired potentials continue to improve the ability for predicting structures of materials. However, many challenges still exist, particularly when calculating structures of disordered systems. These challenges are primarily due to the rapidly increasing dimensionality of the feature-vector space which in most machine-learning algorithms is dependent on the size of the structure. In this article, we present a feature-engineered approach that establishes a set of principles for representing potentials of physical structures (crystals, molecules, and clusters) in a feature space rather than a physically motivated space. Our goal in this work is to define guiding principles that optimize information storage of the physical parameters within the feature representations. In this manner, we focus on keeping the dimensionality of the feature space independent of the number of atoms in the structure. Our Structural Information Filtered Features (SIFF) potential represents structures by utilizing a feature vector of low-correlated descriptors, which correspondingly maximizes information within the descriptor. We present results of our SIFF potential on datasets composed of disordered (carbon and carbon–oxygen) clusters, molecules with C 7 O 2 H 2 stoichiometry in the GDB9-14B dataset, and crystal structures of the form (Al x Ga y In z ) 2 O 3 as proposed in the NOMAD Kaggle competition. Our potential's performance is at least comparable, sometimes significantly more accurate, and often more efficient than other well-known machine-learning potentials for structure prediction. However, primarily, we offer a different perspective on how researchers should consider opportunities in maximizing information storage for features.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dynamics of poly[n]catenane melts

Inspired by advances in the chemical synthesis of interlocking polymer architectures, extensive molecular dynamics simulations have been conducted to study the dynamical properties of poly[n]catenanes-polymers composed entirely of interlocking rings-in the melt state. Both the degree of polymerization (number of links) and the number of beads per ring are systematically varied, and the results are compared to linear and ring polymers. Herein, a simple Rouse-like model is presented, and its analytical solution suggests a decomposition of the dynamics into "ring-like" and "linear-like" regimes at short and long times, respectively. In agreement with this picture, multiple sub-diffusive regimes are observed in the monomer mean-squared-displacements even though interchain entanglement is not prevalent in the system. However, the Rouse-type model does not account for the topological effects of the mechanical bonds, which significantly alter the dynamics at intermediate length scales both within the rings and at the chain segment scales. The stress relaxation in the system is extremely rapid and may be conveniently separated into ring-like and linear-like contributions, again in agreement with the Rouse picture. However, the viscosity has a non-monotonic dependence on the ring size for long chains, which disagrees strongly with theoretical predictions. This unexpected observation cannot be explained in terms of chain disentanglement and is inconsistent with other measures of polymer relaxation. Possible mechanisms for this behavior are proposed and implications for materials design are discussed.

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

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles

The molecular dipole moment ( μ ) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly—via the ground state electron density—from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). Here, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression framework which assigns a (vector) dipole moment to each atom, while the movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting “MuML” models are fitted together to reproduce molecular μ computed using high-level coupled-cluster theory and density functional theory (DFT) on the QM7b dataset, achieving more accurate results due to the physics-based combination of these complementary terms. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models—and the optimal weighting of their combination—depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to μ ; furthermore, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

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

Accurate prediction of core-level spectra of radicals at density functional theory cost via square gradient minimization and recoupling of mixed configurations

State-specific orbital optimized approaches are more accurate at predicting core-level spectra than traditional linear-response protocols, but their utility had been restricted due to the risk of "variational collapse" down to the ground state. We employ the recently developed square gradient minimization algorithm to reliably avoid variational collapse and study the effectiveness of orbital optimized density functional theory (DFT) at predicting second period element 1s core-level spectra of open-shell systems. Several density functionals (including SCAN, B3LYP, and ωB97X-D3) are found to predict excitation energies from the core to singly occupied levels with high accuracy (≤0.3 eV RMS error) against available experimental data. Higher excited states are, however, more challenging by virtue of being intrinsically multiconfigurational. We thus present a configuration interaction inspired route to self-consistently recouple single determinant mixed configurations obtained from DFT, in order to obtain approximate doublet states. This recoupling scheme is used to predict the C K-edge spectra of the allyl radical, the O K-edge spectra of CO + , and the N K-edge of NO 2 with high accuracy relative to experiment, indicating substantial promise in using this approach for the computation of core-level spectra for doublet species [vs more traditional time dependent DFT, equation of motion coupled cluster singles and doubles (EOM-CCSD), or using unrecoupled mixed configurations]. Finally, we also present general guidelines for computing core-excited states from orbital optimized DFT.

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

Biomimetic composite architecture achieves ultrahigh rate capability and cycling life of sodium ion battery cathodes

Sodium ion batteries are an emerging candidate to replace lithium ion batteries in large-scale electrical energy storage systems due to the abundance and widespread distribution of sodium. Despite the growing interest, the development of high-performance sodium cathode materials remains a challenge. In particular, polyanionic compounds are considered as a strong cathode candidate owing to their better cycling stability, a flatter voltage profile, and stronger thermal stability compared to other cathode materials. Here, we report the rational design of a biomimetic bone-inspired polyanionic Na3V2(PO4)3-reduced graphene oxide composite (BI-NVP) cathode that achieves ultrahigh rate charging and ultralong cycling life in a sodium ion battery. At a charging rate of 1 C, BI-NVP delivers 97% of its theoretical capacity and is able to retain a voltage plateau even at the ultra-high rate of 200 C. It also shows long cycling life with capacity retention of 91% after 10 000 cycles at 50 C. The sodium ion battery cells with a BI-NVP cathode and Na metal anode were able to deliver a maximum specific energy of 350 W h kg−1 and maximum specific power of 154 kW kg−1. In situ and postmortem analyses of cycled BI-NVP (including by Raman and XRD spectra) HRTEM, and STEM-EELS, indicate highly reversible dilation–contraction, negligible electrode pulverization, and a stable NVP-reduced graphene oxide layer interface. The results presented here provide a rational and biomimetic material design for the electrode architecture for ultrahigh power and ultralong cyclability of the sodium ion battery full cells when paired with a sodium metal anode.

25 ENERGY STORAGE↗
DOE OSTI2020

Two-dimensional electronic Stark spectroscopy of a photosynthetic dimer

Stark spectroscopy, which measures changes in the linear absorption of a sample in the presence of an external DC electric field, is a powerful experimental tool for probing the existence of charge-transfer (CT) states in photosynthetic systems. CT states often have small transition dipole moments, making them insensitive to other spectroscopic methods, but are particularly sensitive to Stark spectroscopy due to their large permanent dipole moment. In a previous study, we demonstrated a new experimental method, two- dimensional electronic Stark spectroscopy (2DESS), which combines two-dimensional electronic spectroscopy (2DES) and Stark spec- troscopy. In order to understand how the presence of CT states manifest in 2DESS, here, we perform computational modeling and calculations of 2DESS as well as 2DES and Stark spectra, studying a photosynthetic dimer inspired by the photosystem II reaction center. Here, we identify specific cases where qualitatively different sets of system parameters produce similar Stark and 2DES spectra but significantly different 2DESS spectra, showing the potential for 2DESS to aid in identifying CT states and their coupling to excitonic states.

14 SOLAR ENERGY↗
DOE OSTI2020

Active learning for robust, high-complexity reactive atomistic simulations

Machine learned reactive force fields based on polynomial expansions have been shown to be highly effective for describing simulations involving reactive materials. Nevertheless, the highly flexible nature of these models can give rise to a large number of candidate parameters for complicated systems. In these cases, reliable parameterization requires a well-formed training set, which can be difficult to achieve through standard iterative fitting methods. In this paper, we present an active learning approach based on cluster analysis and inspired by Shannon information theory to enable semi-automated generation of informative training sets and robust machine learned force fields. The use of this tool is demonstrated for development of a model based on linear combinations of Chebyshev polynomials explicitly describing up to four-body interactions, for a chemically and structurally diverse system of C/O under extreme conditions. We show that this flexible training database management approach enables development of models exhibiting excellent agreement with Kohn–Sham density functional theory in terms of structure, dynamics, and speciation.

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

A cautionary tale: Problems in the valence-CASSCF description of the ground state (X1Σ+) of BF

In the full optimized reaction space and valence-complete active space self-consistent field (vCAS) methods, a set of active orbitals is defined as the union of the valence orbitals on the atoms, all possible configurations involving the active orbitals are generated, and the orbitals and configuration coefficients are self-consistently optimized. Such wave functions have tremendous flexibility, which makes these methods incredibly powerful but can also lead to inconsistencies in the description of the electronic structure of molecules. In this paper, the problems that can arise in vCAS calculations are illustrated by calculations on the BH and BF molecules. BH is well described by the full vCAS wave function, which accounts for molecular dissociation and 2s–2p near-degeneracy in the boron atom. The same is not true for the full vCAS wave function for BF. There is mixing of core and active orbitals at short internuclear distances and swapping of core and active orbitals at large internuclear distances. In addition, the virtual 2π orbitals, which were included in the active space to account for the 2s–2p near degeneracy effect, are used instead to describe radial correlation of the electrons in the F2pπ-like pairs. Although the above changes lead to lower vCAS energies, they lead to higher vCAS+1+2 energies as well as irregularities and/or discontinuities in the potential energy curves. All of the above problems can be addressed by using the spin-coupled generalized valence bond-inspired vCAS wave function for BF, which includes only a subset of the atomic valence orbitals in the active space.

Xu, Lu T. (ORCID:0000000182064902)↗
DOE OSTI2021

Ultrasonic oscillatory two-phase flow in microchannels

Experimental and numerical investigations are performed to provide an assessment of the transport behavior of an ultrasonic oscillatory two-phase flow in a microchannel. The work is inspired by the flow observed in an innovative ultrasonic fabric drying device using a piezoelectric bimorph transducer with microchannels, where a water-air two-phase flow is transported by harmonically oscillating microchannels. The flow exhibits highly unsteady behavior as the water and air interact with each other during the vibration cycles, making it significantly different from the well-studied steady flow in microchannels. Here, the computational fluid dynamics (CFD) modeling is realized by combing the turbulence Reynolds-averaged Navier-Stokes (RANS) k – ω model with the phase-field method to resolve the dynamics of the two-phase flow. The numerical results are qualitatively validated by the experiment. Through parametric studies, we specifically examined the effects of vibration conditions (i.e., frequency and amplitude), microchannel taper angle, and wall surface contact angle (i.e., wettability) on the flow rate through the microchannel. The results will advance the potential applications where oscillatory or general unsteady microchannel two-phase flows may be present.

42 ENGINEERING↗