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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations

Solubility of redox-active molecules is an important determining factor of the energy density in redox flow batteries. However, the advancement of electrolyte materials discovery has been constrained by the absence of extensive experimental solubility datasets, which are crucial for leveraging data-driven methodologies. In this study, we design and investigate a highly automated workflow that synergizes a high-throughput experimentation platform with a state-of-the-art active learning algorithm to significantly enhance the solubility of redox-active molecules in organic solvents. Our platform identifies multiple solvents that achieve a remarkable solubility threshold exceeding 6.20 M for the archetype redox-active molecule, 2,1,3-benzothiadiazole, from a comprehensive library of more than 2000 potential solvents. Significantly, our integrated strategy necessitates solubility assessments for fewer than 10% of these candidates, underscoring the efficiency of our approach. Our results also show that binary solvent mixtures, particularly those incorporating 1,4-dioxane, are instrumental in boosting the solubility of 2,1,3-benzothiadiazole. Beyond designing an efficient workflow for developing high-performance redox flow batteries, our machine learning-guided high-throughput robotic platform presents a robust and general approach for expedited discovery of functional materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Hierarchically ordered porous transition metal compounds from one-pot type 3D printing approaches

Solution-based soft matter self-assembly (SA) promises unique material structures and properties from approaches including additive manufacturing/three-dimensional (3D) printing. The 3D printing of periodically ordered porous functional inorganic materials through SA unfolding during printing remains a major challenge, however, due to the often vastly different ordering kinetics of separate processes at different length scales. Here, we report a “one-pot” direct ink writing process to produce hierarchically porous transition metal nitrides and precursor oxides from block copolymer (BCP) SA. Heat treatment protocols identified in various environments enable mesostructure retention in the final crystalline materials with periodic lattices on three distinct length scales. Moreover, embedded printing enables the first BCP directed mesoporous non-self-supporting helical oxides and nitrides. Resulting nitrides are superconducting, with record nanoconfinement-induced upper critical fields correlated with BCP molar mass and record surface areas for compound superconductors. Results suggest scalable porous functional inorganic material formation approaches for applications including catalysis, sensing, and microelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Intermediate polaronic charge transport in organic crystals from a many-body first-principles approach

Abstract Charge transport in organic molecular crystals (OMCs) is conventionally categorized into two limiting regimes − band transport, characterized by weak electron-phonon (e-ph) interactions, and charge hopping due to localized polarons formed by strong e-ph interactions. However, between these two limiting cases there is a less well understood intermediate regime where polarons are present but transport does not occur via hopping. Here we show a many-body first-principles approach that can accurately predict the carrier mobility in this intermediate regime and shed light on its microscopic origin. Our approach combines a finite-temperature cumulant method to describe strong e-ph interactions with Green-Kubo transport calculations. We apply this parameter-free framework to naphthalene crystal, demonstrating electron mobility predictions within a factor of 1.5−2 of experiment between 100 and 300 K. Our analysis reveals the formation of a broad polaron satellite peak in the electron spectral function and the failure of the Boltzmann equation in the intermediate regime.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗
DOE OSTI2025

An unsupervised machine learning based approach to identify efficient spin-orbit torque materials

Materials with large spin–orbit torque (SOT) hold considerable significance for many spintronic applications because of their potential for energy-efficient magnetization switching. Unfortunately, most of the existing materials exhibit an SOT efficiency factor that is much less than unity, requiring a large current for magnetization switching. The search for new materials that can exhibit an SOT efficiency much greater than unity is a topic of active research, and only a few such materials have been identified using conventional approaches. In this paper, we present a machine learning-based approach using a word embedding model that can identify new results by deciphering non-trivial correlations among various items in a specialized scientific text corpus. We show that such a model can be used to identify materials likely to exhibit high SOT and rank them according to their expected SOT strengths. The model captured the essential spintronics knowledge embedded in scientific abstracts within various materials science, physics, and engineering journals and identified 97 new materials to exhibit high SOT. Among them, 16 candidate materials are expected to exhibit an SOT efficiency greater than unity, and one of them has recently been confirmed with experiments with quantitative agreement with the model prediction.

Sayed, Shehrin↗
DOE OSTI2026

Viromics approaches for the study of viral diversity and ecology in microbiomes

Viruses are found across all ecosystems and infect every type of organism on Earth. Traditional culture-based methods have proven insufficient to explore this viral diversity at scale, driving the development of viromics, the sequence-based analysis of uncultivated viruses. Viromics approaches have been particularly useful for studying viruses of microorganisms, which can act as crucial regulators of microbiomes across ecosystems. They have already revealed the broad geographic distribution of viral communities and are progressively uncovering the expansive genetic and functional diversity of the global virome. Moving forward, large-scale viral ecogenomics studies combined with new experimental and computational approaches to identify virus activity and host interactions will enable a more complete characterization of global viral diversity and its effects.

Ecology↗
DOE OSTI2020

Integrated network modeling approach defines key metabolic responses of soil microbiomes to perturbations

The soil environment is constantly changing due to shifts in soil moisture, nutrient availability and other conditions. To contend with these changes, soil microorganisms have evolved a variety of ways to adapt to environmental perturbations, including regulation of gene expression. However, it is challenging to untangle the complex phenotypic response of the soil to environmental change, partly due to the absence of predictive modeling frameworks that can mechanistically link molecular-level changes in soil microorganisms to a community’s functional phenotypes (or metaphenome). Towards filling this gap, we performed a combined analysis of metabolic and gene co-expression networks to explore how the soil microbiome responded to changes in soil moisture and nutrient conditions and to determine which genes were expressed under a given condition. Our integrated modeling approach revealed previously unknown, but critically important aspects of the soil microbiomes’ response to environmental perturbations. Incorporation of metabolomic and transcriptomic data into metabolic reaction networks identified condition-specific signature genes that are uniquely associated with dry, wet, and glycine-amended conditions. A subsequent gene co-expression network analysis revealed that drought-associated genes occupied more central positions in a network model of the soil community, compared to the genes associated with wet, and glycine-amended conditions. These results indicate the occurrence of system-wide metabolic coordination when soil microbiomes cope with moisture or nutrient perturbations. Importantly, the approach that we demonstrate here to analyze large-scale multi-omics data from a natural soil environment is applicable to other microbiome systems for which multi-omics data are available.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Micro-continuum approach for mineral precipitation

Abstract Rates and extents of mineral precipitation in porous media are difficult to predict, in part because laboratory experiments are problematic. It is similarly challenging to implement numerical methods that model this process due to the need to dynamically evolve the interface of solid material. We developed a multiphase solver that implements a micro-continuum simulation approach based on the Darcy–Brinkman–Stokes equation to study mineral precipitation. We used the volume-of-fluid technique in sharp interface implementation to capture the propagation of the solid mineral surface. Additionally, we utilize an adaptive mesh refinement method to improve the resolution of near interface simulation domain dynamically. The developed solver was validated against both analytical solution and Arbitrary Lagrangian–Eulerian approach to ensure its accuracy on simulating the propagation of the solid interface. The precipitation of barite (BaSO 4 ) was chosen as a model system to test the solver using variety of simulation parameters: different geometrical constraints, flow conditions, reaction rate and ion diffusion. The growth of a single barite crystal was simulated to demonstrate the solver’s capability to capture the crystal face specific directional growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A new long-term sampling approach to viruses on surfaces

The importance of virus disease outbreaks and its prevention is of growing public concern but our understanding of virus transmission routes is limited by adequate sampling strategies. While conventional swabbing methods provide merely a microbial snapshot, an ideal sampling strategy would allow reliable collection of viral genomic data over longer time periods. This study has evaluated a new, paper-based sticker approach for collection of reliable viral genomic data over longer time periods up to 14 days and after implementation of different hygiene measures. In contrast to swabbing methods, which sample viral load present on a surface at a given time, the paper-based stickers are attached to the surface area of interest and collect viruses that would have otherwise been transferred onto that surface. The major advantage of one-side adhesive stickers is that they are permanently attachable to a variety of surfaces. Initial results demonstrate that stickers permit stable recovery characteristics, even at low virus titers. Stickers also allow reliable virus detection after implementation of routine hygiene measures and over longer periods up to 14 days. Overall, results for this new sticker approach for virus genomic data collection are encouraging, but further studies are required to confirm anticipated benefits over a range of virus types.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations

The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines ( Triticum aestivum , CDC Stanley x CDC Landmark), wheat-barley ( T . aestivum x Hordeum vulgare ) and wheat-wheatgrass ( Triticum durum x Thinopyrum intermedium ) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Interpreting Tafel behavior of consecutive electrochemical reactions through combined thermodynamic and steady state microkinetic approaches

Assessing the reaction pathway of multi-electron-transfer reactions is an essential yet difficult task for the rational design of electrocatalysts. In this work, we develop a heuristic approach that combines thermodynamic adsorption energetics calculated through density functional theory with microkinetic modeling using the steady state approximation to interpret the potential-dependent Tafel behavior of consecutive electrochemical reactions. In doing so, we introduce a kinetic framework for ab initio calculations that ensures self-consistent adsorption energetics based on kinetically limited adsorbate coverages. The approach is applied to experimental results on CoO x (OH) 2-x single crystal electrocatalyst particles yielding coverage dependent mechanistic information and identification of the rate-limiting step with standard rate constants for the oxygen evolution reaction on the ($11\bar{2}0$) surfaces of the β-Co(OH) 2 , β-CoOOH, and CoO 2 bulk phases. Finally, this generalizable method enables catalyst benchmarking based on determining the active species involved and associated intrinsic reaction rate constants in consecutive multi-electron-transfer reactions.

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

Predicting the chemical reactivity of organic materials using a machine-learning approach

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials in configuring a system. In configuring devices such as batteries or solar cells, not only the functionality of individual constituting materials such as electrodes or electrolyte but also an appropriate combination of materials which do not undergo unwanted side reactions is critical in ensuring their reliable performance in long-term operation. While the universal theory that can predict the general chemical reactivity between materials is long awaited and has been the subject of studies with a rich history, traditional ways proposed to date have been mostly based on simple electronic properties of materials such as electronegativity, ionization energy, electron affinity and hardness/softness, and could be applied to only a small group of materials. Moreover, prediction has often been far from accurate and has failed to offer general implications; thus it was practically inadequate as a selection criterion from a large material database, i.e. data-driven material discovery. Herein, we propose a new model for predicting the general reactivity and chemical compatibility among a large number of organic materials, realized by a machine-learning approach. As a showcase, we demonstrate that our new implemented model successfully reproduces previous experimental results reported on side-reactions occurring in lithium–oxygen electrochemical cells. Furthermore, the mapping of chemical stability among more than 90 available electrolyte solvents and the representative redox mediators is realized by this approach, presenting an important guideline in the development of stable electrolyte/redox mediator couples for lithium–oxygen batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Siderocalin fusion proteins enable a new 86 Y/ 90 Y theranostic approach

The mammalian protein siderocalin binds bacterial siderophores and their iron complexes through cation-π and electrostatic interactions, but also displays high affinity for hydroxypyridinone complexes of trivalent lanthanides and actinides. In order to circumvent synthetic challenges, the use of siderocalin-antibody fusion proteins is explored herein as an alternative targeting approach for precision delivery of trivalent radiometals. We demonstrate the viability of this approach in vivo, using the theranostic pair 90 Y (β − , t 1/2 = 64 h)/ 86 Y (β + , t 1/2 = 14.7 h) in a SKOV-3 xenograft mouse model. Ligand radiolabeling with octadentate hydroxypyridinonate 3,4,3-LI(1,2-HOPO) and subsequent protein binding were achieved at room temperature. The results reported here suggest that the rapid non-covalent binding interaction between siderocalin fusion proteins and the negatively charged Y(III)-3,4,3-LI(1,2-HOPO) complexes could enable purification-free, cold-kit labeling strategies for the application of therapeutically relevant radiometals in the clinic.

Cosby, Alexia G.↗
DOE OSTI2024

Integrating crystallographic and computational approaches to carbon-capture materials for the mitigation of climate change

Here, this article presents an overview of the current state of the art in the structure determination of microporous carbon-capture materials, as discussed at the recent NIST workshop “Integrating Crystallographic and Computational Approaches to Carbon-Capture Materials for the Mitigation of Climate Change”. The continual rise in anthropogenic CO 2 concentration and its effect on climate change call for the implementation of carbon capture technologies to reduce the CO 2 concentration in the atmosphere. Porous solids, including metal–organic frameworks (MOFs), are feasible candidates for gas capture and storage applications. However, determining the structure of these materials represents a significant obstacle in their development into advanced sorbents. The existing difficulties can be overcome by integrating crystallographic methods and theoretical modeling. The workshop gathered experimentalists and theorists from academia, government, and industry to review this field and identify approaches, including collaborative opportunities, required to develop tools for rapid determination of the structures of porous solid sorbents and the effect of structure on the carbon capture performance. We highlight the findings of that workshop, especially in the need for reference materials, standardized procedures and reporting of sorbent activation and adsorption measurements, standardized reporting of theoretical calculations, and round-robin structure determination.

36 MATERIALS SCIENCE↗
DOE OSTI2025

From lignin to market: a technical and economic perspective of reductive depolymerization approaches

Lignin has grown into one of the main candidates to replace fossil-based resources as it is the largest renewable source of aromatic building blocks. The complex structure of polymeric lignin, however, requires depolymerization to simpler building blocks for the chemical industry. One of the most promising depolymerization approaches is reductive depolymerization of which two process configurations are currently studied in pilot scale installations for upscaling to industrial scale: (i) reductive catalytic fractionation (RCF), and (ii) reductive catalytic depolymerization (RCD). Both technical and techno-economic aspects will be covered within this review, discussing the advantages and challenges of both approaches regarding processing, production costs, product output, and applications. In this regard, RCF benefits from its decreased energy and solvent consumption linked with being a one-step process and delivers a product with a high monomer content (∼25–45 wt%). RCD, on the other hand, has the advantage of continuous processing and reduced catalyst fouling and delivers a product that mainly consists of oligomers (<10 wt% monomers). The complete overview of both processes presented here addresses their potential, and can guide future researchers, policy makers and companies to make thoughtful decisions on lignin valorization.

09 BIOMASS FUELS↗
DOE OSTI2024

A graph embedding‐based approach for automatic cyber‐physical power system risk assessment to prevent and mitigate threats at scale

Abstract Power systems are facing an increasing number of cyber incidents, potentially leading to damaging consequences to both physical and cyber aspects. However, the development of analytical methods for the study of large‐scale power infrastructures as cyber‐physical systems is still in its early stages. Drawing inspiration from machine‐learning techniques, the authors introduce a method inspired by the principles of graph embedding that is tailored for quantitative risk assessment and the exploration of possible mitigation strategies of large‐scale cyber‐physical power systems. The primary advantage of the graph embedding approach lies in its ability to generate numerous random walks on a graph, simulating potential access paths. Meanwhile, it enables capturing high‐dimensional structures in low‐dimensional spaces, facilitating advanced machine‐learning applications, and ensuring scalability and adaptability for comprehensive network analysis. By employing this graph embedding‐based approach, the authors present a structured and methodical framework for risk assessment in cyber‐physical systems. The proposed graph embedding‐based risk analysis framework aims to provide a more insightful perspective on cyber‐physical risk assessment and situation awareness for power systems. To validate and demonstrate its applicability, the method has been tested on two cyber‐physical power system models: the Western System Coordinating Council (WSCC) 9‐Bus System and the Illinois 200‐Bus System , thereby showing its advantages in enhancing the accuracy of risk analysis and comprehensiveness of situational awareness.

Sun, Shining↗
DOE OSTI2021

A data-driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems

The increasing integration of distributed energy resources, including demand-side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data-driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data-driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value-at-risk is incorporated into the Wasserstein-based DRO model and converted into a computationally tractable mixed-integer convex optimisation problem. Benchmarked with robust optimisation and chance-constrained programming, the proposed data-driven model can give a less conservative robust solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A new approach to the design of surface subsystems of polymeric insulators for HV and MV apparatus under AC voltage

The design of high voltage and medium voltage AC and DC standoff insulators, and, in general, of insulation systems where surface and interface properties are predominant factors, is based on specifications and standards that rely upon long-term experiences and lab testing. Surface insulating properties depend on electrical and thermal stresses, besides environmental factors, and the inception of extrinsic ageing phenomena, such as partial discharges, can be already a cause of reduced reliability and premature failure (for organic insulating materials). An innovative approach, based on field simulation, discharge modelling, and partial discharge inception measurements is presented by the authors, which can establish a solid basis to optimise insulation design for any type of supply voltage waveform. This method, the three-leg approach, is applied here to a cable system under AC sinusoidal voltage, but it can work for any type of insulation system, such as bushings, switch gears, transformers, rotating machines, and power electronics boards.

42 ENGINEERING↗