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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Techno-Economic Case Study: Low-Temperature Conversion Performance Based on Isolated Anatomical Fractions of Corn Stover

This report summarizes analysis conducted to support a case study under the Feedstock Conversion Interface Consortium (FCIC) focused on techno-economic analysis (TEA) modeling to quantify the process yield and resulting process cost impacts for processing isolated anatomical fractions of corn stover through a low-temperature conversion (biochemical) pathway. It is hypothesized that different individual anatomical fractions of corn stover vary in both composition and recalcitrance, giving biorefineries options in whether and how to deal with fractionated or whole biomass feedstock. By quantifying the techno-economic impacts of this variability, we provide actionable information for end users to understand tradeoffs in conversion system yields and economics in considering feedstock processing decisions at the biorefinery gate. For this study, we worked with FCIC researchers to obtain data on the compositional analysis and conversion performance of whole corn stover alongside three individual anatomical fractions (cobs, husks, and stalks) across key steps of the biorefinery conversion process within FCIC’s research scope—pretreatment and enzymatic hydrolysis. This TEA screening assessment highlighted biorefinery economic trade-offs observed through this approach. Namely, relative to processing whole stover biomass, two of the three anatomical fractions for which composition/conversion data were available (cobs and husks) demonstrated the ability to achieve higher fuel yields and lower minimum fuel selling prices (MFSPs), while the third fraction (stalks) led to the opposite result, as a composite reflection of compositional differences and process convertibility.

cost impacts↗
DOE OSTI2024

Demystifying In Situ Pyrolysis Chemistry for High-Performance Polyanionic Cathodes in Sodium-Ion Batteries

In this article, the carbon coating strategy has emerged as an indispensable approach to improve the conductivity of polyanionic cathodes. However, owing to the complex reaction process between precursors of carbon and cathode, establishing a unified screening principle for carbonaceous precursors remains a technical challenge. Herein, we reveal that carbonaceous precursor pyrolysis chemistry undeniably influences the formation process and performance of Na 3 V 2 (PO 4 ) 3 (NVP) cathodes from in situ insights. By investigating three types of carbonaceous precursors, it is found that O/H-containing functional groups can provide more bonding sites for cathode precursors and generate a reducing atmosphere by pyrolysis, which is beneficial to the formation of polyanionic materials and a uniform carbon coating layer. Conversely, excessive pyrolysis of functional groups leads to a significant amount of gas, which is detrimental to the compactness of the carbon layer. Furthermore, the substantial presence of residual heteroatoms diminishes graphitization. In this case, it is demonstrated that carbon dots (CDs) precursors with suitable functional groups can comprehensively enhance the Na+ migration rate, reversibility, and interface stability of the cathode material. As a result, the NVP/CDs cathode displays outstanding capacity retention, maintaining 92% after 10,000 cycles at a high rate of 50 C. Altogether, these findings provide a valuable benchmark for carbon source selection for polyanionic cathodes.

25 ENERGY STORAGE↗
DOE OSTI2025

Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design

The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.

Mohan, Mood [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2023

Investigation of the Effect of Chrome and Nickel Concentrations During Two-Piston Splat Quenching of Austenitic Stainless Steels

For solidification rates at or near equilibrium solidification conditions the effects of chrome (Cr) and nickel (Ni) on stainless steel solidification modes and microstructures are well detailed. However, fusion-based additive manufacturing (FBAM) processes that rely on faster, more rapid solidification rates call for a more in-depth understanding of the effects of rapid solidification on the solidification behavior and how these change with variations in alloy composition. Eleven custom stainless steel (SS) alloys with unique compositions based on 316L were made using targeted alloying element additions to generate feedstock with Cr/Ni eq ratios ranging from 0.9 to 2.1. Two-piston splat quenching (SQ) was used to produce rapid solidification conditions similar to those achieved in powder bed fusion processes in a fraction of the time and cost of traditional methods. Employing heat transfer simulations, SEM, TEM, and STEM characterization techniques, the SQ process was found to consistently produce solidification rates estimated to be between ~ 0.4 and 1.6 m/s. Five unique solidification microstructures were identified within the rapidly solidified SQ samples (primary austenite, massively transformed ferrite to austenite, primary austenite and massively transformed ferrite to austenite, primary ferrite, and primary ferrite and massively transformed ferrite to austenite). Both the primary ferrite solidification mode and primary ferrite phase were found to form at lower Cr/Ni eq values than previously predicted. SQ samples with compositions that were within the compositional specifications for 316L SS, an alloy which is commonly used in FBAM processes, demonstrated a wider range of potential solidification modes and microstructures that could form at rapid solidification rates than expected. Finally, by using SQ as a means to simulate rapid solidification conditions similar to those observed in FBAM processes, potential new alloy compositions can be screened faster and more cost effectively than purchasing and running test batches of the metallic powder.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Physics-coupled data-driven design of high-temperature alloys

We present a materials design loop, which streamlines physics-coupled machine learning (ML) surrogate models to discover new alloy chemistries with improved properties. The efficacy is demonstrated by discovering a high-temperature alumina-forming austenitic (AFA) stainless steel with enhanced creep, followed by experimental validation. The ML models have been trained using a well-curated, highly consistent experimental dataset augmented with synthetic microstructural features from a computational thermodynamic approach. We have populated a large number of hypothetical AFA alloys to explore the high-dimensional composition space and have predicted their creep properties by providing the same synthetic input features obtained from the trained ML models. Uncertainties from the ML training were taken as thresholds for truncating predicted results to identify alloys with improved or deteriorated creep. Individual elemental compositions have been determined via probability density distribution analysis from the group of alloys at the top and bottom of the predicted creep values for further virtual and experimental validations. In conclusion, we anticipate that this workflow can be applied to screen desired conditions, such as chemistry and processing parameters, in high-dimensional space through physics-guided data analytics.

Alloy design↗
DOE OSTI2022

Screening method for Enzyme-based liquefaction of corn stover pellets at high solids

Liquefaction of high solid loadings of unpretreated corn stover pellets has been demonstrated with rheology of the resulting slurries enabling mixing and movement within biorefinery bioreactors. However, some forms of pelleted stover do not readily liquefy, so it is important to screen out lots of unsuitable pellets before processing is initiated. This work reports a laboratory assay that rapidly assesses whether pellets have the potential for enzyme-based liquefaction at high solids loadings. Twenty-eight pelleted corn stover (harvested at the same time and location) were analyzed using 20 mL enzyme solutions (3 FPU cellulase/ g biomass) at 30 % w/v solids loading. Imaging together with measurement of reducing sugars were performed over 24-hours. Further, some samples formed concentrated slurries of 300 mg/mL (dry basis) in the small-scale assay, which was later confirmed in an agitated bioreactor. Also, the laboratory assay showed potential for optimizing enzyme formulations that could be employed for slurry formation.

Laboratory assay↗
DOE OSTI2023

Limitations of cetane number to predict transient combustion phenomena in high-pressure fuel sprays

Fundamental understanding of in-cylinder processes in diesel engines is important to screen emerging biofuels and advanced combustion modes that can reduce greenhouse gas emissions and regulated pollutants including soot. In this study, the role of fuel properties on spray development and combustion is investigated by systematically isolating chemical and thermophysical effects. Three different fuels are considered, two with similar chemical properties and two with similar thermophysical properties with one fuel common to both groups. Experiments are performed in a constant-pressure flow chamber de-signed to provide stable test conditions and facilitate acquisition of at least 150 injections in quick succession for each fuel under reacting conditions at high-pressure, high-temperature ambient conditions using a modified conventional diesel engine injector. Further, high speed optical diagnostics including rainbow schlieren deflectometry, OH* chemiluminescence, and a two-color pyrometry system are employed to simultaneously image the transient spray and reacting jets. Image analysis is performed to determine liquid length, vapor penetration length, timing and location of first stage and main ignition events, lift-off location, total soot mass, and more. Results show that fuels with similar chemical properties or cetane number (CN) exhibit similar delay times for first stage and main ignition events as may be expected, but very different liquid length, first stage and main ignition locations, lift-off length, apparent turbulent flame speed, and soot formation. As such, the ability to characterize candidate biofuels with CN or other parameters derived from simple flame configurations is called into question. In this study, thermo-physical properties controlling the liquid length are identified as the main contributing factor for the observed differences.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic’s evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (M pro ), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. Here, this work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic “warhead” to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of M pro with micromolar affinities (K I of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Low-Threshold Visible-to-Ultraviolet Solid-State Triplet-Fusion Upconversion

Visible-to-ultraviolet (UV) triplet-triplet annihilation (TTA) upconversion is an energy-relevant photon-management strategy that converts visible photons into UV photons capable of driving high-energy photochemical processes. Practical implementation requires solid-state thin films, which often require high excitation power. Here, we demonstrate the first solid-state visible-to-UV TTA upconversion thin-film device, using 1,4-bis((tricyclopentylsilyl)ethynyl)naphthalene (TCPS-NAP) as the annihilator and tris(2-phenylpyridine)iridium(III) (Ir(ppy) 3 ) as the sensitizer on silver. Under incoherent 455 nm excitation, the device exhibits blue-to-UV upconversion with a threshold of 4.8 mW/cm 2 . Using surface plasmons in the planar silver film, we achieve green-to-UV upconversion with a 1.11 eV anti-Stokes shift and 6.9 mW/cm 2 threshold, a 12.2 × threshold enhancement over far-field 532 nm excitation. In solution, TCPS-NAP shows excimer-dominated anti-Stokes emission rather than UV upconversion. These results establish a platform for harvesting low-intensity visible light to drive UV processes while demonstrating that solution-phase screening does not predict solid-state upconversion performance.

Luminescence↗
DOE OSTI2023

SARS-CoV2 billion-compound docking

Abstract This dataset contains ligand conformations and docking scores for 1.4 billion molecules docked against 6 structural targets from SARS-CoV2, representing 5 unique proteins: MPro, NSP15, PLPro, RDRP, and the Spike protein. Docking was carried out using the AutoDock-GPU platform on the Summit supercomputer and Google Cloud. The docking procedure employed the Solis Wets search method to generate 20 independent ligand binding poses per compound. Each compound geometry was scored using the AutoDock free energy estimate, and rescored using RFScore v3 and DUD-E machine-learned rescoring models. Input protein structures are included, suitable for use by AutoDock-GPU and other docking programs. As the result of an exceptionally large docking campaign, this dataset represents a valuable resource for discovering trends across small molecule and protein binding sites, training AI models, and comparing to inhibitor compounds targeting SARS-CoV-2. The work also gives an example of how to organize and process data from ultra-large docking screens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The CCR4‐NOT complex component NOT1 regulates RNA‐directed DNA methylation and transcriptional silencing by facilitating Pol IV‐dependent siRNA production

Summary Small interfering RNAs (siRNAs) are responsible for establishing and maintaining DNA methylation through the RNA‐directed DNA methylation (RdDM) pathway in plants. Although siRNA biogenesis is well known, it is relatively unclear about how the process is regulated. By a forward genetic screen in Arabidopsis thaliana , we identified a mutant defective in NOT1 and demonstrated that NOT1 is required for transcriptional silencing at RdDM target genomic loci. We demonstrated that NOT1 is required for Pol IV‐dependent siRNA accumulation and DNA methylation at a subset of RdDM target genomic loci. Furthermore, we revealed that NOT1 is a constituent of a multi‐subunit CCR4‐NOT deadenylase complex by immunoprecipitation combined with mass spectrometry and demonstrated that the CCR4‐NOT components can function as a whole to mediate chromatin silencing. Therefore, our work establishes that the CCR4‐NOT complex regulates the biogenesis of Pol IV‐dependent siRNAs, and hence facilitates DNA methylation and transcriptional silencing in Arabidopsis.

Zhou, Hao‐Ran↗
DOE OSTI2021

Aspenplus Model For Msw Sorting Process

Unit operations for the MSW sorting process include shredding, magnets for ferrous separation, screening, air classification, eddy current for non-ferrous separation, and near-infrared detection for plastic recovery. The MIXCINC module within Aspen Plus was selected to handle mixed solid streams containing inert (metals and glass) and non-conventional materials (paper, plastics, biomass, organic waste). The Peng-Robinson equation of state with Boston-Mathias modifications was applied to predict properties for a nonconventional solids mix. Enthalpy and density of the non-conventional materials were defined using non-conventional material properties.

Hu, Hongqiang↗
DOE OSTI2026

Tephrite [SWR-26-061]

Tephrite is a Rust-based immersive visualization renderer built on top of Bevy. It's designed for multi-display / CAVE-style rendering by running your Bevy app as a logic process that spawns one or more render processes. World state is replicated from the logic process to render processes, and render processes present the scene to the configured screens. Tephrite provides the graphical support for the National Laboratory of the Rockies (NLR) Insight Center's immersive space.

Brunhart-Lupo, Nicholas [National Laboratory of th↗
DOE OSTI2025

Evaluating High-Halide Waste Form Options for Salt-Based Nuclear Waste Simulants

In this study, waste forms were being explored for an electrochemical salt simulant, , referred to as ERV3, which is a high-LiCl/KCl salt containing simulated fission products (i.e., Sr, Cs, Nd) and Na to represent bond sodium from Experimental Breeder Reactor-II metallic fast reactor fuel. The goal was to find glassy systems that could be used to immobilize the salt in a single-step process. The envisionment of this process would be find a frit glass that could be added to the salt waste, heat treated, poured into waste canisters, and then stored for disposal. To perform this study, a literature review was conducted, the most promising seven systems were fabricated without the salt, and then mixed with salt simulant and heat treated under different processes. The criteria that were used to screen potential compositions included demonstrated alkali incorporation, could be melted at reasonably low temperature ( T ≤ 1000°C), and if the compositions had some demonstrated data for waste-form-related properties, that was a benefit. High marks were given for compositions that showed amorphous nature after slow cooling of samples containing ERV3.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

A Step-by-Step Protocol from METASPACE to Biological Interpretation

Mass spectrometry imaging (MSI) represents an exceptional tool for exploring complex biological systems spatially at the molecular level. However, due to its multidimensional nature and large-scale data output, it presents considerable challenges when it comes to extracting meaningful biological insights. Recent advancements, such as the METASPACE platform, have enabled researchers to efficiently process, annotate, and interpret MSI datasets by leveraging machine learning and cloud-based infrastructure. In this tutorial, we present a detailed and user-friendly R-pipeline designed to help METASPACE users navigate untargeted metabolomic annotations and transform them into practical insights about their biological systems. By combining METASPACE annotations with rapid R-based screening, this workflow not only streamlined the analytical process but also enhanced the understanding of spatial molecular distribution, especially for complex systems. Here, this easy-to-follow approach has the potential for applications in diagnostics, drug discovery, environmental and ecological processes, and more. We envision this pipeline to be particularly useful for newcomers to the field of MSI and

Moreno Pedraza, Abigail↗
DOE OSTI2024

Feedstock/pretreatment screening for bioconversion of sugar and lignin streams via deacetylated disc-refining

Recent publications have shown the benefits of deacetylation disc-refining (DDR) as a pretreatment process to deconstruct biomass into sugars and lignin residues. Major advantages of DDR pretreatment over steam and dilute acid pretreatment are the removal of acetyl and lignin during deacetylation. DDR does not generate hydroxymethylfurfural (HMF) and furfural which are commonly produced from steam and dilute acid pretreatments. Acetate, lignin, HMF, and furfural are known inhibitors during enzymatic hydrolysis and fermentation. Another advantage of deacetylation is the production of lignin-rich black liquor, which can be upgraded to other bioproducts. Furthermore, due to the lack of sugar degradation during deacetylation, DDR has significantly less sugar loss than other pretreatment methods. Previous studies for DDR have primarily focused on corn stover, but lacked the investigative studies of other feedstocks. This study was designed to screen various DDR process conditions at pilot scale using three different feedstocks, including corn stover, poplar, and switchgrass. The impact of the pretreatment conditions was evaluated by testing hydrolysates for bioconversion to 2,3-butanediol. Pretreatment of biomass by DDR showed high-conversion-yields and 2,3-BDO fermentation production yields. Techno-economic analysis (TEA) of the pretreatment for biomass to sugar was also developed based on NREL’s Aspen Model. This study shows that the cellulose and hemicellulose in poplar was more recalcitrant than herbaceous feedstocks which ultimately drove up the sugar cost. Switchgrass was also more recalcitrant than corn stover but less than poplar.

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

Spray‐induced gene silencing to identify powdery mildew gene targets and processes for powdery mildew control

Abstract Spray‐induced gene silencing (SIGS) is an emerging tool for crop pest protection. It utilizes exogenously applied double‐stranded RNA to specifically reduce pest target gene expression using endogenous RNA interference machinery. In this study, SIGS methods were developed and optimized for powdery mildew fungi, which are widespread obligate biotrophic fungi that infect agricultural crops, using the known azole‐fungicide target cytochrome P450 51 (CYP51) in the Golovinomyces orontii–Arabidopsis thaliana pathosystem. Additional screening resulted in the identification of conserved gene targets and processes important to powdery mildew proliferation: apoptosis‐antagonizing transcription factor in essential cellular metabolism and stress response; lipid catabolism genes lipase a , lipase 1 , and acetyl‐CoA oxidase in energy production ; and genes involved in manipulation of the plant host via abscisic acid metabolism ( 9‐cis‐epoxycarotenoid dioxygenase , xanthoxin dehydrogenase , and a putative abscisic acid G‐protein coupled receptor ) and secretion of the effector protein, effector candidate 2 . Powdery mildew is the dominant disease impacting grapes and extensive powdery mildew resistance to applied fungicides has been reported. We therefore developed SIGS for the Erysiphe necator–Vitis vinifera system and tested six successful targets identified using the G. orontii–A. thaliana system. For all targets tested, a similar reduction in powdery mildew disease was observed between systems. This indicates screening of broadly conserved targets in the G. orontii–A. thaliana pathosystem identifies targets and processes for the successful control of other powdery mildew fungi. The efficacy of SIGS on powdery mildew fungi makes SIGS an exciting prospect for commercial powdery mildew control.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Leveraging Afterglow in Scintillation-based x-ray detectors for spacetime-resolved computed tomography for accelerated acquisition and high-speed event capture

Afterglow in x-ray imaging for high-speed radiography is a constraint that limits imaging systems to low-light/fast decay screens which create poor data. Current approaches focus purely on using low-light yield screens with fast decay to avoid multiple exposure pileup due to afterglow. The goal of this work is to develop a statistical estimation approach to leverage afterglow to improve image quality thus allowing for higher quality imaging components to be used. This will allow for bright screens will slow decay to be used, and then a post-processing step applies the statistical estimation to separate each frame with superior signal compared to low-light/fast decay screens.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗