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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 OSTI2021

In situ mechanical reinforcement of polymer hydrogels via metal-coordinated crosslink mineralization

Biological organic-inorganic materials remain a popular source of inspiration for bioinspired materials design and engineering. Inspired by the self-assembling metal-reinforced mussel holdfast threads, we tested if metal-coordinate polymer networks can be utilized as simple composite scaffolds for direct in situ crosslink mineralization. Starting with aqueous solutions of polymers end-functionalized with metal-coordinating ligands of catechol or histidine, here we show that inter-molecular metal-ion coordination complexes can serve as mineral nucleation sites, whereby significant mechanical reinforcement is achieved upon nanoscale particle growth directly at the metal-coordinate network crosslink sites.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Probing the Contribution of Lateral Pathways to Out–of–Plane Charge Transport in Organic Bulk Heterojunctions

The nanoscale interpenetrating electron donor–acceptor network in organic bulk heterojunction (BHJ) solar cells results in efficient charge photogeneration but creates complex 3D pathways for charge transport. At present, little is known about the extent to which out-of-plane charge flow relies on lateral electrical connectivity. In this work, a procedure, based on conductive atomic force microscopy, is introduced to quantify lateral current spreading during out-of-plane charge transport. Using the developed approach, the dependence of lateral spreading on BHJ phase separation, composition, and molecule type (small molecule vs polymer) is studied. In the small-molecule BHJ, 7,7'-(4,4-bis(2-ethylhexyl)-4H-silolo[3,2-b:4,5-b']dithiophene-2,6-diyl)bis(6-fluoro-4-(5'-hexyl-[2,2'-bithiophen]-5-yl)benzo[c]-[1,2,5]thiadiazole):(6,6)-Phenyl-C 71 -butyric acid methyl ester (p-DTS(FBTTh 2 ) 2 :PC 71 BM), an increase is observed in lateral hole current spreading as the population of donor crystallites, bearing an edge-on molecular orientation, is increased. When integrated into BHJs, the polymer donor poly(3-hexylthiophene-2,5-diyl) (P3HT) leads to greater lateral hole current spreading and more spatially uniform charge transport than the small-molecule donor, owing to in-plane charge transport along the polymer backbone. Finally, through the newly introduced electrical characterization scheme, these experiments bring to light the role of lateral electrical connectivity in assisting charge navigation across BHJs.

14 SOLAR ENERGY↗
DOE OSTI2025

Ca X ML: Chemistry‐informed machine learning explains mutual changes between protein conformations and calcium ions in calcium‐binding proteins using structural and topological features

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of Ca X ML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

An algorithm for physics informed scan path optimization in additive manufacturing

Site specific microstructure control is a critical research area within the field of additive manufacturing due to its potential to revolutionize part performance. One way to achieve site specific microstructure control is through control of the solidification conditions via the construction of intricate scan paths; however, the search space for such a problem is large. Previous attempts only considered the solidification conditions at the top surface while also requiring either lots of manual-fine tuning or large amounts of computational resources. This paper introduces a general method for scan path optimization which considers the solidification conditions in the bulk of the material without an increase in computational expense. This method consists of three core components:1. A heat transfer model for simulating the temperature field at a given time.2. A surrogate model which takes scan pattern information and temperature data and predicts the solidification conditions of the bulk as well as the meltpool depths for a spot melt.3. A decision algorithm to decide which spot melt should be printed next based on the outputs of the surrogate model.Each of these components can be changed without changing the overall method. Within this work, this method is applied in the creation of an algorithm containing a semi-analytic heat transfer model to simulate the temperature field, a fully convolutional neural network (FCNN) as the surrogate model, and a greedy decision algorithm. The resulting algorithm produced complex scan patterns which gave strong results for simulated microstructure control.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Backpropagation-based learning with local derivative approximation and memory replay in biologically plausible neural systems

When learning, the brain modifies individual synaptic connections to reach a desired behavior. Animal and human brains have been shown to be incredibly capable of learning complex and varied functions across a wide variety of tasks. In recent years, artificial neural networks, inspired by human and animal brains, have shown great capabilities in learning a wide variety of difficult tasks. However, artificial neural networks primarily teach themselves through the use of backpropagation, a learning method which has no clear analogue within the brain. Additionally, Artificial Neural Networks primarily use continuous activation functions, which differ significantly from the spiking neuronal behavior present in the brain. In this paper, we discuss and demonstrate a biologically plausible learning method that approximates backpropagation through two techniques on Spiking Neural Networks. First, we show that the local temporal derivatives that are necessary for backpropagation can be approximately recovered through reconstruction using spike timings. Second, we show that through learning during a sleep phase, inspired by neuroscience research into memory replay, the localized parallel feedback path can learn to approximate the derivative through the forward path weight matrix, thus solving the weight transport problem. Lastly, we demonstrate that the combination of these two methods can approach or exceed the accuracy of backpropagation-based methods for a variety of neuromorphic vision tasks while maintaining biological plausibility.

42 ENGINEERING↗
DOE OSTI2020

Artificial Iron Proteins: Modeling the Active Sites in Non-Heme Dioxygenases

An important class of non-heme dioxygenases contains a conserved Fe binding site that consists of a 2-His-1-carboxylate facial triad. Results from structural biology show that, in the resting state, these proteins are six-coordinate with aqua ligands occupying the remaining three coordination sites. Here, we have utilized biotin–streptavidin (Sav) technology to design new artificial Fe proteins (ArMs) that have many of the same structural features found within active sites of these non-heme dioxygenases. An Sav variant was isolated that contains the S 112 E mutation, which installed a carboxylate side chain in the appropriate position to bind to a synthetic Fe II complex confined within Sav. Structural studies using X-ray diffraction (XRD) methods revealed a facial triad binding site that is composed of two N donors from the biotinylated ligand and the monodentate coordination of the carboxylate from S 112 E. Two aqua ligands complete the primary coordination sphere of the Fe II center with both involved in hydrogen bond networks within Sav. The corresponding Fe III protein was also prepared and structurally characterized to show a six-coordinate complex with two exogenous acetato ligands. The Fe III protein was further shown to bind an exogenous azido ligand through replacement of one acetato ligand. Spectroscopic studies of the ArMs in solution support the results found by XRD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Toward accelerating rare-earth metal extraction using equivariant neural networks

The separation of rare-earth metals, vital for numerous advanced technologies, is hampered by their similar chemical properties, making ligand discovery a significant challenge. Traditional experimental and quantum chemistry approaches for identifying effective ligands are often resource-intensive. We introduce a machine learning protocol based on an equivariant neural network, Allegro, for the rapid and accurate prediction of binding energies in rare-earth complexes. Key to this work is our newly curated dataset of rare-earth metal complexes—made publicly available to foster further research—systematically generated using the Architector program. This dataset distinctively features functionalized derivatives of proven rare-earth-chelating scaffolds, hydroxypyridinone (HOPO), catecholamide (CAM), and their thio-analogues, selected for their established efficacy in binding these elements. Trained on this valuable resource, our Allegro models demonstrate excellent performance, particularly when trained to directly predict DFT-level binding energies, yielding highly accurate results that closely correlate with theoretical calculations on a diverse test set. Furthermore, this strategy exhibited strong out-of-sample generalization, accurately predicting binding energies for an isomeric HOPO-derivative ligand not seen during training. By substantially reducing computational demands, this machine learning framework, alongside the provided dataset, represent powerful tools to accelerate the high-throughput screening and rational design of novel ligands for efficient rare-earth metal separation.

Gupta, Ankur K. [Lawrence Berkeley National Labora↗
DOE OSTI2023

Coarse-to-fine Task-driven Inpainting for Geoscience Images

The processing and recognition of geoscience images have wide applications. Most of existing researches focus on understanding the high-quality geoscience images by assuming that all the images are clear. However, in many real-world cases, the geoscience images might contain occlusions during the image acquisition. This problem actually implies the image inpainting problem in computer vision and multimedia. As far as we know, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images, and they never consider the following geoscience task when developing inpainting methods. Here, this paper aims to repair the occluded regions for a better geoscience task performance and advanced visualization quality simultaneously, without changing the current deployed deep learning based geoscience models. Because of the complex context of geoscience images, we propose a coarse-to-fine encoder-decoder network with the help of designed coarse-to-fine adversarial context discriminators to reconstruct the occluded image regions. Due to the limited data of geoscience images, we propose a MaskMix based data augmentation method, which augments inpainting masks instead of augmenting original images, to exploit the limited geoscience image data. The experimental results on three public geoscience datasets for remote sensing scene recognition, cross-view geolocation and semantic segmentation tasks respectively show the effectiveness and accuracy of the proposed method. The code is available at: https://github.com/HMS97/Task-driven-Inpainting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Deep-Learning-Based Koopman Modeling for Online Control Synthesis of Nonlinear Power System Transient Dynamics

Power system stability and control have become more challenging due to the increasing uncertainty associated with renewable generation. Here, the performance of conventional control is highly driven by the physics-based offline-developed dynamic models that can deviate from the actual system characteristics under different operating conditions and/or configurations. Data-driven approaches based on online measurements can be a better solution to addressing these issues by capturing real-time operation conditions. This article describes a novel fully data-driven probabilistic framework to derive a linear representation of postcontingency grid dynamics and online prescribe control based on the derived model to enhance transient stability. The complex nonlinear power system dynamics is approximated by a linear model by using multiple neural network modules that infer distributions of the observations and introducing a Koopman layer to sample possible Koopman linear models from the inferred distributions. The trained model features linearity that can be easily incorporated into the existing linear control design paradigm and ease the controller design process. The effectiveness of Koopman-based control designs is validated through comparative case studies, which demonstrate increased prediction accuracy and control performance when applied to a power system with heterogeneous generator dynamics.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Time-resolved tracking of cellulose biosynthesis and assembly during cell wall regeneration in live Arabidopsis protoplasts

Cellulose, the most abundant polysaccharide on earth composing plant cell walls, is synthesized by coordinated action of multiple enzymes in cellulose synthase complexes embedded within the plasma membrane. Multiple chains of cellulose fibrils form intertwined extracellular matrix networks. It remains largely unknown how newly synthesized cellulose is assembled into an intricate fibril network on cell surfaces. Here, we have established an in vivo time-resolved imaging platform to continuously visualize cellulose biosynthesis and fibril network assembly onArabidopsis thalianaprotoplast surfaces as the primary cell wall regenerates. Our observations provide the basis for a model of cellulose fibril network development in protoplasts driven by an interplay of multiscale dynamics that includes rapid diffusion and coalescence of nascent cellulose fibrils, processive elongation of single fibrils, and cellulose fibrillar network rearrangement during maturation. This study provides fresh insights into the dynamic and mechanistic aspects of cell wall synthesis at the single-cell level.

Science & Technology - Other Topics↗
DOE OSTI2022

interflow: A Python package to organize, calculate, and visualize sectoral interdependency flow data

Many economic sectors rely on an uninterrupted “upstream” supply of a resource to conduct their primary functions, leaving them vulnerable to adverse effects should that resource flow be interrupted or compromised (OECD, 2017; U.S. EPA, 2010). Well-known examples of these relationships include water demand by the energy sector (e.g., thermoelectric cooling for nuclear generation) (Grubert & Sanders, 2018; Webber, 2017) and energy demand by the water sector (e.g., electricity required to treat or move water in the public water sector) (Congressional Research Service, 2017) though many others exist. Being able to calculate and document these interdependencies and evaluate where the greatest cross-sectoral intensities and flows exist can reveal opportunities to enhance the overall network. Despite the implications and potential impacts, however, these interconnections and flows have been historically complex to analyze and understand. The interflow package provides a flexible tool to organize, calculate, and visualize (using Sankey diagrams and other visualizations) sectoral interdependency flows for multiple subsectors and resources. This tool can help decision-makers, researchers, and other audiences more easily pull meaning from these interdependencies to reveal multi-faceted opportunities and risks. interflow can help investigate questions such as (1) which sectors have high cross-resource dependencies, (2) how does demand for a resource in various sectors compare across regions, and (3) where the sectoral and regional opportunities are for enhanced efficiency, security, and resiliency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine learning for postprocessing ensemble streamflow forecasts

Skillful streamflow forecasts can inform decisions in various areas of water policy and management. We integrate numerical weather prediction ensembles, distributed hydrological model, and machine learning to generate ensemble streamflow forecasts at medium-range lead times (1–7 days). We demonstrate the application of machine learning as postprocessor for improving the quality of ensemble streamflow forecasts. Our results show that the machine learning postprocessor can improve streamflow forecasts relative to low-complexity forecasts (e.g., climatological and temporal persistence) as well as standalone hydrometeorological modeling and neural network. The relative gain in forecast skill from postprocessor is generally higher at medium-range timescales compared to shorter lead times; high flows compared to low–moderate flows, and the warm season compared to the cool ones. Overall, our results highlight the benefits of machine learning in many aspects for improving both the skill and reliability of streamflow forecasts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

DECAL MDN Resolution Calibration

The simulated pixelated calorimeter uses 0.1 mm × 0.1 mm silicon pixels (0.0001 cm²) silicon pixels as active layers — far finer than current concepts like HGCAL (~0.5 cm²) — improving energy resolution through much finer segmentation. While the standard resolution formalism fits three numbers to a handful of discrete test-beam energies, this work is a first try at a more data-efficient alternative: learning the full response shape continuously in energy with a mixture density network. This continuous, differentiable surrogate is a natural building block for fast simulation of extremely complex calorimeters.

Wang, Peter [U. Chicago (main)]↗
DOE OSTI2020

Innovation in Radiological Security, Part 1 of 2 – Using a Cloud Solution to Compress the Timely Detection Model

The design and implementation of new security technologies must account for numerous and complex operational challenges. For example, traditionally isolated systems must now survive amid the proliferation of network connectivity and endure the dynamic environments created by organizations tolerating bring-your-own-device policies. When evaluating the cyber, physical, or cyber-physical security of an asset, a common practice in the security industry is to apply the Timely Detection Model (TDM)—a versatile concept that relates the security functions of detection, delay, and response to the progression of an oppositional force’s attack timeline. If implemented correctly, security enhancements can compress the TDM to offer efficiencies to the stakeholders responsible for adjudicating threat scenarios. The efficiencies gained by the response force allow for more effective protection strategies to be realized. One such enhancement, deployed within the radiological security domain—the Sentry-Remote Monitoring System (Sentry-RMS)—is a stand-alone security system that detects, assesses, and communicates priority alarms as a means of thwarting internal and external threats. The SEntry-RMS CommUnications and REsponse (Sentry-SECURE) platform—an optional feature of the Sentry-RMS—is being developed to facilitate more efficient alarm adjudication by site stakeholders and, if necessary, a faster response by law enforcement. This Cloud-hosted platform receives protected information from deployed Sentry-RMS units and relays it to stakeholders that have vested interests in maintaining an elevated level of situational awareness. Operationally, this enables real-time delivery of high-priority alarm and video imagery directly to an identified response stakeholder, such as local law enforcement or site management, via natively developed mobile applications or full platform integration. The Sentry-SECURE platform, as implemented by the U.S. Department of Energy’s Office of Radiological Security, is an example of an innovative security technology that compresses the TDM by (1) enabling a more efficient time to target and (2) better informing a response force’s predetermined tactics, techniques, and procedures. This paper explores the platform’s operational roles, highlights its principal functions, and presents a use case that demonstrates how the platform provides enhanced situational awareness when adjudicating priority alarms.

Sentry-RMS, Timely Detection Model, Sentry-SECURE↗
DOE OSTI2022

Physics-informed neural networks (PINNs) for fluid mechanics: a review

Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier–Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is complex, and we cannot tackle high-dimensional problems governed by parametrized NSE. Moreover, solving inverse flow problems is often prohibitively expensive and requires complex and expensive formulations and new computer codes. Here, we review flow physics-informed learning, integrating seamlessly data and mathematical models, and implement them using physics-informed neural networks (PINNs). Here, we demonstrate the effectiveness of PINNs for inverse problems related to three-dimensional wake flows, supersonic flows, and biomedical flows.

42 ENGINEERING↗
DOE OSTI2023

MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data

Background: Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking. Results: Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses. Conclusion: Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User’s guide of MetaboDirect are freely available through (https://github.com/Coayala/MetaboDirect) and (https://metabodirect.readthedocs.io/en/latest/), respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Structural asymmetry governs the assembly and GTPase activity of McrBC restriction complexes

Abstract McrBC complexes are motor-driven nucleases functioning in bacterial self-defense by cleaving foreign DNA. The GTP-specific AAA + protein McrB powers translocation along DNA and its hydrolysis activity is stimulated by its partner nuclease McrC. Here, we report cryo-EM structures of Thermococcus gammatolerans McrB and McrBC, and E. coli McrBC. The McrB hexamers, containing the necessary catalytic machinery for basal GTP hydrolysis, are intrinsically asymmetric. This asymmetry directs McrC binding so that it engages a single active site, where it then uses an arginine/lysine-mediated hydrogen-bonding network to reposition the asparagine in the McrB signature motif for optimal catalytic function. While the two McrBC complexes use different DNA-binding domains, these contribute to the same general GTP-recognition mechanism employed by all G proteins. Asymmetry also induces distinct inter-subunit interactions around the ring, suggesting a coordinated and directional GTP-hydrolysis cycle. Our data provide insights into the conserved molecular mechanisms governing McrB family AAA + motors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A Quantum-Based Approach to Predict Primary Radiation Damage in Polymeric Networks

Initial atomistic-level radiation damage in chemically reactive materials is thought to induce reaction cascades that can result in undesirable degradation of macroscale properties. Ensembles of quantum-based molecular dynamics (QMD) simulations can accurately predict these cascades, but extracting chemical insights from the many underlying trajectories is a labor-intensive process that can require substantial a priori intuition. We develop here a general and automated graph-based approach to extract all chemically distinct structures sampled in QMD simulations and apply our approach to predict primary radiation damage of polydimethylsiloxane (PDMS), the main constituent of silicones. A postprocessing protocol is developed to identify underlying polymer backbone structures as connected components in QMD trajectories. Furthermore, these backbones form a repository of radiation-damaged structures. A scheme for extracting and updating a library of isomorphically distinct structures is proposed to identify the spanning set and aid chemical interpretation of the repository. The analyses are applied to ensembles of cascade QMD simulations in which the four element types in PDMS are selectively excited in primary knock-on atom events. Our approach reveals a much higher degree of combinatorial complexity in this system than was inferred through radiolysis experiments. Probabilities are extracted for radiation-induced network changes including formation of branch points, carbon linkages, cycles, bond scissions, and carbon uptake into the Si–O siloxane backbone network. The general analysis framework presented here is readily extendable to modeling chemical degradation of other polymers and molecular materials and provides a basis for future quantum-informed multiscale modeling of radiation damage.

36 MATERIALS SCIENCE↗