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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Photooxidation of Polyolefins to Produce Materials with In-Chain Ketones and Improved Materials Properties

Herein, we report a selective photooxidation of commodity postconsumer polyolefins to produce polymers with in-chain ketones. The reaction does not involve the use of catalyst, metals, or expensive oxidants, and selectively introduces ketone functional groups. Under mild and operationally simple conditions, yields up to 1.23 mol % of in-chain ketones were achieved. Installation of in-chain ketones resulted in materials with improved adhesion of the materials and miscibility of mixed plastics relative to the unfunctionalized plastics. The introduction of ketone groups into the polymer backbone allows these materials to react with diamines, forming dynamic covalent polyolefin networks. This strategy allows for the upcycling of mixed plastic waste into reprocessable materials with enhanced performance properties compared to polyolefin blends. Mechanistic studies support the involvement of photoexcited nitroaromatics in consecutive hydrogen and oxygen atom transfer reactions.

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

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

Abstract The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Jin, Ling↗
DOE OSTI2015

Cu-PDC-bpa solid coordination frameworks (PDC=2,5-pyrindinedicarboxylate; bpa=1,2-DI(4-pyridil)ethane)): 2D and 3D structural flexibility producing a 3-c herringbone array next to ideal

Combination of polycarboxylate anions and dipyridyl ligands is an effective strategy to produce solid coordination frameworks (SCF) which are crystalline materials based on connections between metal ions through organic ligands. In this context, this work is focused on two novel Cu{sup II}-based SCFs exhibiting PDC (2,5-pyridinedicarboxylate) and bpa (1,2-di(4-pyridyl)ethane), being the first structures reported in literature containing both ligands. Chemical formula are [Cu{sub 2}[(PDC){sub 2}(bpa)(H{sub 2}O){sub 2}]·3H{sub 2}O·DMF (1), and [Cu{sub 2}(PDC){sub 2}(bpa)(H{sub 2}O){sub 2}]·7H{sub 2}O (2), where DMF is dimethylformamide. Compounds 1 and 2 have been characterized by means of X-ray diffraction (XRD), infrared spectroscopy (IR), thermogravimetric (TG) analysis, differential thermal analysis (DTA) and dielectric measurements. The crystallographic analysis revealed that compounds 1 and 2 can be described as herringbone-type layers formed by helicoidal Cu-PDC-Cu chains connected through bpa ligands. Solvent molecules are crystallized between the layers, providing the inter-layer connections through hydrogen bonds. Differences between both compounds are attributable to the flexibility of bpa (in 2D) as well as to the 3D packing of the layers which is solvent dependent. This fact results in the fact that compound 2 is the most regular 3-c herringbone array reported so far. The structural dynamism of these networks is responsible for the crystalline to-amorphous to-crystalline (CAC) transformation from compound 1 to compound 2. Crystallochemical features for both compounds have also been studied and compared to similar 3-connected herringbone-arrays. - Graphical abstract: Cu-PDC-bpa 3-c herringbone arrays. - Highlights: • The most ideal herringbone array reported so far is a Cu-PDC-bpa SCF. • Conformational freedom of bpa results in 2D and 3D flexibility of the SCFs. • The flexibility of the SCFs is related to a phase transformation. • Dielectric measurements confirm the mobility of guest solvent molecules.

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

On the Network Topology of Cross-Linked Acrylate Photopolymers: A Molecular Dynamics Case Study

A reactive molecular dynamics approach is used to simulate cross-linking of acrylate polymer networks. By employing the same force field and reactive scheme and studying three representative multifunctional acrylate monomers, we isolate the importance of the nonreactive moieties within these model monomers. Analyses of reactive trajectories benchmark the estimated gel points, cyclomatic character, and spatially resolved cross-linking tendencies of the acrylates as a function of conversion. The insights into the similarities and differences of the polymerization and resulting networks presented here suggest molecular mechanics as a useful tool in the rational design of photopolymerization resins.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Structured Ionomer Thin Films at Water Interface: Molecular Dynamics Simulation Insight

Controlling the structure and dynamics of thin films of ionizable polymers at water interfaces is critical to their many applications. As the chemical diversity within one polymer is increased, controlling the structure and dynamics of the polymer, which is a key to their use, becomes a challenge. Here molecular dynamics simulations (MD) are used to obtain molecular insight into the structure and dynamics of thin films of one such macromolecule at the interface with water. The polymer consists of an ABCBA topology with randomly sulfonated polystyrene (C), tethered symmetrically to flexible poly(ethylene- r -propylene) blocks (B), and end-capped by a poly( t -butylstyrene) block (A). The compositions of the interfacial and bulk regions of thin films of the ABCBA polymers are followed as a function of exposure time to water. We find that interfacial rearrangements take place where buried ionic segments migrate toward the water interface. The hydrophobic blocks collapse and rearrange to minimize their exposure to water. In conclusion, the water that initially drives interfacial reengagements breaks the ionic clusters within the film, forming a dynamic hydrophilic internal network within the hydrophobic segments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ion-Electron Coupling-Driven Redox Behavior in Metal–Organic Frameworks

Redox-active metal–organic frameworks (MOFs) have long been proposed as electronic transport platforms, yet the microscopic origin of their conductivity remains debated. A theoretical demonstration reveals charge transport in a Zn(pyrazole–naphthalene diimide (NDI)) MOF arising not from delocalized band-like states but from redox hopping between discrete linker sites. Using ab initio molecular dynamics simulations combined with electronic structure analysis, we established a direct link among electron injection, structural reorganization, and transport. Electron accumulation proceeds sequentially and site-selectively from imide and carbonyl groups of the NDI core progressively involving pyrazole N atoms at higher reduction states, through a hierarchy of redox-active sites. In contrast, Zn nodes remain essentially redox-inactive, which confirms their structural role. Density-of-states analysis corroborates a transport regime dominated by linker-centered states with evolving p-character upon reduction, resulting in dynamically reconfigured conduction networks. Real-time trajectories reveal anisotropic linker-to-linker electron transfer modulated by counterion coordination. This cooperative ion–electron regime emerges from potential energy surface collapse into a single low-barrier transition (ΔG ‡ ≈ 45 meV), where ionic and electronic motions evolve adiabatically on the same free-energy landscape. Elucidating redox conductivity in Zn(pyrazole–NDI) MOFs provides a theoretical framework for use in neuromorphic computing and related technologies.

Charge transfer↗
DOE OSTI2011

Conformational Variability of Organophosphorus Hydrolase upon Soman and Paraoxon Binding

The bacterial enzyme organophosphorus hydrolase (OPH) exhibits both catalytic and substrate promiscuity. It hydrolyzes bonds in a variety of phosphotriester (P-O), phosphonothioate (P-S), phosphofluoridate (P-F) and phosphonocyanate (F-CN) compounds. However, its catalytic efficiency varies markedly for different substrates, limiting the broad-range application of OPH as catalyst in the bioremediation of pesticides and chemical war agents. In the present study, pK{sub a} calculations and multiple explicit-solvent molecular dynamics (MD) simulations were performed to characterize and contrast the structural dynamics of OPH bound to two substrates hydrolyzed with very distinct catalytic efficiencies: the nerve agent soman (O-pinacolyl-methyl-phosphonofluoridate) and the pesticide paraoxon (diethyl p-nitrophenyl phosphate). pK{sub a} calculations for the substrate-bound and unbound enzyme showed a significant pK{sub a} shift from standard values ({Delta}pK{sub a} = {+-} 3 units) for residues 254His and 275Arg. MD simulations of the doubly protonated 254His revealed a dynamic hydrogen bond network connecting the catalytic residue 301Asp via 254His to 232Asp, 233Asp, 275Arg and 235Asp, and is consistent with a previously postulated proton relay mechanism to ferry protons away from the active site with substrates that do not require activation of the leaving group. Hydrogen bonds between 301Asp and 254His were persistent in the OPH-paraoxon complex but not in the OPH-soman one, suggesting a potential role for such interaction in the more efficient hydrolysis of paraoxon over soman by OPH. These results are in line with previous mutational studies of residue 254His, which led to an increase of the catalytic efficiency of OPH over soman yet decreased its efficiency for paraoxon. In addition, comparative analysis of the molecular trajectories for OPH bound to soman and paraoxon suggests that binding of the latter facilitates the conformational transition of OPH from the open to the closed substate promoting a tighter binding of paraoxon.

08 HYDROGEN↗
DOE OSTI2010

Structural Stability of Carbon Nanotube Films: The Role of Bending Buckling

In films, mats, buckypaper, and other materials composed of carbon nanotubes (CNTs), individual CNTs are bound together by van der Waals forces and form entangled networks of bundles. Mesoscopic dynamic simulations reproduce the spontaneous self-assembly of CNTs into continuous networks of bundles and reveal that the bending buckling and the length of CNTs are the two main factors responsible for the stability of the network structures formed by defect-free CNTs. Bending buckling of CNTs reduces the bending energy of interconnections between bundles and stabilizes the interconnections by creating effective barriers for CNT sliding. The length of the nanotubes is affecting the ability of van der Waals forces of intertube interactions to counterbalance the internal straightening forces acting on curved nanotubes present in the continuous networks. The critical length for the formation of stable network structures is found to be ~120 nm for (10,10) single-walled CNTs. In the simulations where the bending buckling is artificially switched off, the network structures are found to be unstable against disintegration into individual bundles even for micrometer-long CNTs.

Chemistry↗
DOE OSTI2015

From globally coupled maps to complex-systems biology

Studies of globally coupled maps, introduced as a network of chaotic dynamics, are briefly reviewed with an emphasis on novel concepts therein, which are universal in high-dimensional dynamical systems. They include clustering of synchronized oscillations, hierarchical clustering, chimera of synchronization and desynchronization, partition complexity, prevalence of Milnor attractors, chaotic itinerancy, and collective chaos. The degrees of freedom necessary for high dimensionality are proposed to equal the number in which the combinatorial exceeds the exponential. Future analysis of high-dimensional dynamical systems with regard to complex-systems biology is briefly discussed.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Synchronization of chaotic systems

We review some of the history and early work in the area of synchronization in chaotic systems. We start with our own discovery of the phenomenon, but go on to establish the historical timeline of this topic back to the earliest known paper. The topic of synchronization of chaotic systems has always been intriguing, since chaotic systems are known to resist synchronization because of their positive Lyapunov exponents. The convergence of the two systems to identical trajectories is a surprise. We show how people originally thought about this process and how the concept of synchronization changed over the years to a more geometric view using synchronization manifolds. We also show that building synchronizing systems leads naturally to engineering more complex systems whose constituents are chaotic, but which can be tuned to output various chaotic signals. We finally end up at a topic that is still in very active exploration today and that is synchronization of dynamical systems in networks of oscillators.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Genomics-enabled analysis of specialized metabolism in bioenergy crops: Current progress and challenges

Plants produce a staggering diversity of specialized small molecule metabolites that play vital roles in mediating environmental interactions and stress adaptation. This chemical diversity derives from dynamic biosynthetic pathway networks that are often species-specific and operate under tight spatiotemporal and environmental control. A growing divide between demand and environmental challenges in food and bioenergy crop production have intensified research on these complex metabolite networks and their contribution to crop fitness. High-throughput omics technologies provide access to ever-increasing data resources for investigating plant metabolism. However, the efficiency of using such system-wide data to decode the gene and enzyme functions controlling specialized metabolism has remained limited; due largely to the recalcitrance of many plants to genetic approaches and the lack of ‘user-friendly’ biochemical tools for studying the diverse enzyme classes involved in specialized metabolism. With emphasis on terpenoid metabolism in the bioenergy crop switchgrass as an example, this review aims to illustrate current advances and challenges in the application of DNA synthesis and synthetic biology tools for accelerating the functional discovery of genes, enzymes and pathways in plant specialized metabolism. These technologies have accelerated knowledge development on the biosynthesis and physiological roles of diverse metabolite networks across many ecologically and economically important plant species and can provide resources for application to precision breeding and natural product metabolic engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Spectral Similarity Masks Structural Diversity at Hydrophobic Water Interfaces

The air-water and graphene-water interfaces represent quintessential examples of the liquid-gas and liquid-solid boundaries, respectively. While the sum-frequency generation (SFG) spectra of these interfaces show similarities, a consensus on their signals and interpretations has yet to be reached. Leveraging deep learning, we computed first-principles SFG spectra for both systems, addressing experimental discrepancies. Here, our findings reveal that similarities in SFG signals do not translate into comparable interfacial microscopic properties. Instead, graphene-water and air-water interfaces exhibit fundamental differences in SFG-active thicknesses, hydrogen-bonding networks, and surface dynamics. These distinctions underscore roughness suppression and electronic interactions present at the solid-liquid interface but absent at the gas-liquid interface.

Wang, Yong [Princeton Univ., NJ (United States)] (↗
DOE OSTI2016

An Application Development Platform for Neuromorphic Computing

Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic computing systems developed as a hardware based approach to the implementation of neural networks. They feature highly adaptive and programmable structural elements, which model arti cial neural networks with spiking behavior. We design them to solve problems using evolutionary optimization. In this paper, we highlight the current hardware and software implementations of DANNA, including their features, functionalities and performance. We then describe the development of an Application Development Platform (ADP) to support efficient application implementation and testing of DANNA based solutions. We conclude with future directions.

Dean, Mark↗
DOE OSTI2025

Integrated Routing and Traffic Signal Control for CAVs via Reinforcement Learning Approach

Incorporating Connected and Automated Vehicles (CAVs) into urban traffic networks presents opportunities and challenges for traffic management systems. This paper aims to develop an integrated routing and traffic signal control system designed explicitly for CAVs, utilizing a Reinforcement Learning (RL) approach. The objective is to enhance traffic flow and improve overall transportation efficiency in the controlled areas. We propose an innovative framework that employs the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Q-network (DQN), to dynamically adjust the number of vehicles in the routes and the duration of traffic signals. Our simulation results demonstrate that a DQN agent successfully optimizes the number of vehicles in the routes and traffic signal timings of traffic signal controllers, eventually reducing total travel time. The study illustrates the potential usage of RL-based systems in managing routing and traffic signals for CAVs, offering a promising opportunity for future urban traffic management strategies.

Park, Jiho [New York University]↗
DOE OSTI2022

A scalable solid-state nanoporous network with atomic-level interaction design for carbon dioxide capture

Carbon capture and sequestration reduces carbon dioxide emissions and is critical in accomplishing carbon neutrality targets. Here, we demonstrate new sustainable, solid-state, polyamine-appended, cyanuric acid–stabilized melamine nanoporous networks (MNNs) via dynamic combinatorial chemistry (DCC) at the kilogram scale toward effective and high-capacity carbon dioxide capture. Polyamine-appended MNNs reaction mechanisms with carbon dioxide were elucidated with double-level DCC where two-dimensional heteronuclear chemical shift correlation nuclear magnetic resonance spectroscopy was performed to demonstrate the interatomic interactions. We distinguished ammonium carbamate pairs and a mix of ammonium carbamate and carbamic acid during carbon dioxide chemisorption. The coordination of polyamine and cyanuric acid modification endows MNNs with high adsorption capacity (1.82 millimoles per gram at 1 bar), fast adsorption time (less than 1 minute), low price, and extraordinary stability to cycling by flue gas. This work creates a general industrialization method toward carbon dioxide capture via DCC atomic-level design strategies.

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

Fast and converged classical simulations of evidence for the utility of quantum computing before fault tolerance

A recent quantum simulation of observables of the kicked Ising model on 127 qubits implemented circuits that exceed the capabilities of exact classical simulation. We show that several approximate classical methods, based on sparse Pauli dynamics and tensor network algorithms, can simulate these observables orders of magnitude faster than the quantum experiment and can also be systematically converged beyond the experimental accuracy. Our most accurate technique combines a mixed Schrödinger and Heisenberg tensor network representation with the Bethe free entropy relation of belief propagation to compute expectation values with an effective wave function–operator sandwich bond dimension >16,000,000, achieving an absolute accuracy, without extrapolation, in the observables of <0.01, which is converged for many practical purposes. We thereby identify inaccuracies in the experimental extrapolations and suggest how future experiments can be implemented to increase the classical hardness.

Science & Technology - Other Topics↗
DOE OSTI2024

Machine learning–based extreme event attribution

The observed increase in extreme weather has prompted recent methodological advances in extreme event attribution. We propose a machine learning–based approach that uses convolutional neural networks to create dynamically consistent counterfactual versions of historical extreme events under different levels of global mean temperature (GMT). We apply this technique to one recent extreme heat event (southcentral North America 2023) and several historical events that have been previously analyzed using established attribution methods. We estimate that temperatures during the southcentral North America event were 1.18° to 1.42°C warmer because of global warming and that similar events will occur 0.14 to 0.60 times per year at 2.0°C above preindustrial levels of GMT. Additionally, we find that the learned relationships between daily temperature and GMT are influenced by the seasonality of the forced temperature response and the daily meteorological conditions. Our results broadly agree with other attribution techniques, suggesting that machine learning can be used to perform rapid, low-cost attribution of extreme events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

pnnl/Active-Sampling-for-Atomistic-Potentials

This software contains a routine for active sampling of a DFT-generated dataset for training a neural network potential (SchNet). Major updates to the base SchNet code are also included to accommodate large systems (100s of atoms). We also provide code to apply the neural network potential in dynamic shear simulations.

Sprueill, Henry↗