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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Predictive models enhance feedstock quality of corn stover via air classification

Feedstock heterogeneity is a fundamental obstacle to cost-competitive biobased products. Agricultural products like corn stover have anatomical components that vary in their chemical composition, mechanical properties, structure, and response to chemical and biological treatments. A technique that can enrich streams in select anatomical fractions would allow a tailored deconstruction approach to increase overall process efficiency. Air classification can be leveraged for such refining, however, fundamental characterization and understanding of the particle properties that underly the physics of air classification are only modestly documented. Here, we determine fundamental particle properties including mass-to-area ratio, drag coefficient, and partition velocity that describe how anatomical tissues of corn stover behave during air classification. In this work, mass-to-area ratios of anatomical tissues vary by nearly two orders of magnitude from 2.3 mg/mm 2 for cob to 0.04 mg/mm 2 for leaf. Drag coefficients of longer, fibrous materials (i.e., rind, husk, and sheath) are shown to correlate with particle area (p-value < 0.001) whereas granular tissues (i.e., cob, pith, and leaf) correlate better with mass-to-area ratio (p-values < 0.001). When compared to experimental observations, a simulated two-stage air classification and size reduction scenario predicts the overall partitioning of anatomical tissues within 15% for pith, husk, rind, and cob tissues. The model predicts an air-classified fraction preferentially enriched in cob (purity = 20%), rind (purity = 74%), and pith (purity = 4.5%) with a mass yield of 47%. Empirical relations for these properties can be used to predict the partitioning of corn stover during air classification based on anatomical type and size.

09 BIOMASS FUELS↗
DOE OSTI2012

Current Research on Thermochemical Conversion of Biomass at the National Renewable Energy Laboratory

The thermochemical research platform at the National Bioenergy Center, National Renewable Energy Laboratory (NREL) is primarily focused on conversion of biomass to transportation fuels using non-biological techniques. Research is conducted in three general areas relating to fuels synthesis via thermochemical conversion by gasification: (1) Biomass gasification fundamentals, chemistry and mechanisms of tar formation; (2) Catalytic tar reforming and syngas cleaning; and (3) Syngas conversion to mixed alcohols. In addition, the platform supports activities in both technoeconomic analysis (TEA) and life cycle assessment (LCA) of thermochemical conversion processes. Results from the TEA and LCA are used to inform and guide laboratory research for alternative biomass-to-fuels strategies. Detailed process models are developed using the best available material and energy balance information and unit operations models created at NREL and elsewhere. These models are used to identify cost drivers which then form the basis for research programs aimed at reducing costs and improving process efficiency while maintaining sustainability and an overall net reduction in greenhouse gases.

09 BIOMASS FUELS↗
DOE OSTI2020

High-yield growth kinetics and spatial mapping of single-walled carbon nanotube forests at wafer scale

Emerging commercial applications of vertically aligned, single-walled carbon nanotube (SWCNT) “forests” require synthesis that minimizes nanotube diameter while maximizing number density across substrate areas exceeding centimeter scale. To address this need, we synthesized SWCNT forests on full silicon wafers with notable reproducibility and uniformity, and co-optimized growth for small diameters and high densities across large areas to access new territory in this 3D parameter space. We mapped the spatial uniformity of key structural features using Raman microscopy, synchrotron X-ray scattering, and Rutherford backscattering spectrometry. Low C 2 H 2 flux over sub-nm Fe/Mo catalysts produced small-diameter SWCNTs (2.1 nm) at high number densities (2.26 × 10 12 cm -2 ) on wafers up to 6 in. Although removing Mo resulted in larger SWCNT diameters and lower densities (<0.7 × 10 12 cm -2 ), mass conversion rates from C 2 H 2 to SWCNT product were high and remarkably invariant for catalyst compositions and densities (i.e., 47.7% or 1.30 × 10 6 % g-catalyst -1 on 4-in. wafers). These carbon conversion efficiencies far exceed typical benchtop reactors and are on par with the best reported literature values. Our detailed elucidation of correlations among structural characteristics within this resource-efficient process is expected to guide future scale-up efforts of SWCNT forest growth beyond wafer scale.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microwave-assisted depolymerization of PET over heterogeneous catalysts

The accumulation of plastic waste in the environment is rising and poses a significant environmental and health threat. Polyethylene terephthalate (PET), used mainly in single-use plastic bottles, accounts for a substantial fraction of this waste. The currently employed recycling strategies are inadequate to deal with the global plastic waste problem. PET glycolysis is a promising depolymerization method but has mainly been conducted using homogeneous catalysts; developing active and sustainable catalysts, and more energy-efficient processes remain challenges. Here, in this work, we implement microwave heating in PET glycolysis and identify ZnO as an excellent heterogeneous catalyst. We demonstrate the influence of ZnO's particle size and facet and hydrogen bonding on its activity. Finally, we demonstrate excellent performance for real-world post-consumer PET waste deconstruction with rapid depolymerization into bis(2-hydroxyethyl) terephthalate (BHET), achieving > 95% yields in less than 10 min. Cradle-to-gate life cycle assessment also indicates that BHET produced by ZnO-catalyzed glycolysis has lower global warming potential than the petrochemical-based production and homogeneously-catalyzed glycolysis.

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

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

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

A Performance analysis of the Claridge-Culp-Liu dehumidification process: A novel approach for drying moist air based on membrane separation, vacuum compression and sub-atmospheric condensation

This article covers a basic model for analyzing the performance of the Claridge-Culp-Liu dehumidification process. The fundamental process efficiency limit for dehumidification is close to COP Carnot , but for the eight dehumidification cases examined, the limiting or ideal energy use required is 26% to 56% that of a Carnot condensing system as shown in an earlier paper. The model presented in this paper is used to show the membrane system performance reduction caused by finite membrane area, finite water vapor permeance, non-zero air permeance, non-zero system air pressure drop, non-ideal compressors, vacuum pumps, and condensers. The performance of a “conservative” membrane system based on the use of existing components is computed for eight specific conditions along with that of a “target” system that assumes expected component performance after additional future component development. The “conservative” membrane system would use 36% to 66% as much energy as a system with a COP=7 chiller to produce the same dehumidification for the eight cases examined while the “target” system would use 15% to 40% the energy of a system with a COP=7 chiller. In addition to the significant energy reduction over conventional technology, the membrane system offers the advantages of: 1) no HFC refrigerant use; 2) direct isothermal control over humidity ratio setpoint; 3) maximum capacity occurs at design conditions; and 4) system generates pure water extracted from air as a by-product.

42 ENGINEERING↗
DOE OSTI2020

A new approach of reduction of carbon dioxide emission and optimal use of carbon and hydrogen content for the desired syngas production from coal

We report this research is designed to make progress in overcoming the challenges through the development of a two-stage coal processing. Specifically, a two-stage process was used to maximize the use of the carbon in coal or increase the carbon monoxide yield or lower carbon dioxide and methane yields. Carbon dioxide-char gasification in the absence of water can generate carbon monoxide with near-zero methane, which is desired to produce high-carbon and low-hydrogen chemicals such as oxalic acid through catalytic carbon monoxide coupling and hydrolysis. The technology is applicable to any coal, although Power River Basin (PRB) coal is used as an example feedstock in this research Also, the sodium-iron catalyst can accelerate not only the reaction kinetics in both stages but also increase the hydrogen/carbon monoxide ratio in the syngas produced in the second stage. In other words, the catalyst is a multifunctional agent, which can not only intensify the overall coal process efficiency but also improve the qualities of the desired syngas products and reduce carbon dioxide emission. Thus, In the 1st stage, the catalysts can significantly reduce the CO 2 -char gasification by as high as 75.00%. In the 2nd stage, the H 2 /CO ratio of ideal syngas is ~2:1 with near-0 CH 4 generation and the CH4 production can be reduced as high as 61.29% for the CO 2 –H 2 O-Char coal gasification. The activation energy for the 2nd stage is reduced by as high as 35.43% than that of raw coal without use Na–Fe catalyst. The overall carbon footprint reduction for the study is reduced by 87.33% in the 1st stage and 96.77% compared to the direct coal combustion.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

A “Sweet” Biorefinery: Sugar-derived ionic liquids for the pretreatment of lignocellulosic biomass

Sugar-based ionic liquids (ILs) derived from biomass-based cations (choline, betaine) and sugar-derived anions were developed as sustainable pretreatment solvents to advance the concept of self-reliant biorefinery. Cholinium gluconate ([Ch][GlcA]) enabled streamlined one-pot sorghum pretreatment without the need for pH adjustment or washing, while betainium gluconate ([Bet][GlcA]) achieved higher sugar yields, improving overall process efficiency and economics. Both ILs produced hydrolysates fully compatible with microbial fermentation, demonstrating their potential for efficient, simplified biomass conversion. This work positions sugar-based ILs as powerful platforms that unite sustainable chemistry with integrated bioprocessing, marking a pivotal step toward realizing the self-reliant biorefinery.

Biomass-based ions↗
DOE OSTI2019

Validation of an alloy design strategy for stable Fe–Cr–Al–Nb-X ferritic alloys using electron microscopy and atom probe tomography

The Fe–Cr–Al–Nb ferritic alloys strengthened by A₂B Laves phases, with superior oxidation and corrosion resistance, are being considered for high temperature operation within fossil-fired steam power plants to increase process efficiency and reduce CO₂ emissions. In this paper, new sets of alloys based on Fe–30Cr–3Al–1Nb (in weight percent) were designed with (i) a high microstructural stability of Laves phase precipitates in a BCC-Fe matrix and (ii) reduced precipitate free zones along the grain boundaries, targeting an operating temperature of 700 °C. Two alloys with titanium or tungsten additions were down-selected through thermodynamic calculations with a design strategy to maximize the amount of Laves phase at 700 °C and lower the BCC solvus temperature. To validate this design strategy, the candidate alloys were cast, heat treated and aged at 700 °C, and the resulting microstructure was characterized using scanning electron microscopy and atom probe tomography. The alloys with titanium addition (1 wt%) showed monotonic precipitate coarsening. On the other hand, the alloys with tungsten addition (6 wt%) showed a reduced coarsening rate of Laves phase precipitates in the matrix up to 1008 h aging, whereas an acceleration of the precipitate coarsening after that. Atom probe tomography of this alloy revealed the formation of a core-shell precipitate structure with a Nb/Si rich core and W-rich shell, while the alloy with Ti-addition did not show the core-shell structure. The detailed characterization results revealed that the core-shell structure was strongly correlated with the observed two-step coarsening mode in the tungsten containing alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2011

In situ derivation of sulfur activated TiO{sub 2} nano porous layers through pulse-micro arc oxidation technology

Highlights: {yields} S-TiO{sub 2} layers were grown by MAO technique under pulse current for the first time. {yields} Effect of growth parameters on chemical composition, topography, and morphology of the layers was studied. {yields} A correlation between photocatalytic performance and growth conditions was proposed. -- Abstract: Micro arc oxidation technique, as a facile and efficient process, was employed to grow sulfur doped titania porous layers. This research sheds light on the photocatalytic performance of the micro arc oxidized S-TiO{sub 2} nano-porous layers fabricated under pulse current. Morphological and topographical studies, performed by SEM and AFM techniques, revealed that increasing the frequency and/or decreasing the duty cycle resulted in formation of finer pores and smoother surfaces. XRD and XPS results showed that the layers consisted of anatase and rutile phases whose fraction was observed to change depending on the synthesis conditions. The highest anatase relative content was obtained at the frequency of 500 Hz and the duty cycle of 5%. Furthermore, photocatalytic activity of the layers was examined by measuring the decomposition rate of methylene blue under both ultraviolet and visible photo irradiations. Maximum photodegradation reaction rate constants over the pulse-grown S-TiO{sub 2} layers were respectively measured as 0.0202 and 0.0110 min{sup -1} for ultraviolet and visible irradiations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Simplifying activations with linear approximations in neural networks

A key step in Neural Networks is activation. Among the different types of activation functions, sigmoid, tanh, and others involve the usage of exponents for calculation. From a hardware perspective, exponential implementation implies the usage of Taylor series or repeated methods involving many addition, multiplication, and division steps, and as a result are power-hungry and consume many clock cycles. We implement a piecewise linear approximation of the sigmoid function as a replacement for standard sigmoid activation libraries. This approach provides a practical alternative by leveraging piecewise segmentation, which simplifies hardware implementation and improves computational efficiency. In this paper, we detail piecewise functions that can be implemented using linear approximations and their implications for overall model accuracy and performance gain. Our results show that for the DenseNet, ResNet, and GoogLeNet architectures, the piecewise linear approximation of the sigmoid function provides faster execution times compared to the standard TensorFlow sigmoid implementation while maintaining comparable accuracy. Specifically, for MNIST with DenseNet, accuracy reaches 99.91% (Piecewise) vs. 99.97% (Base) with up to 1.31x speedup in execution time. For CIFAR-10 with DenseNet, accuracy improves to 98.97% (Piecewise) vs. 99.40% (Base) while achieving 1.24x faster execution. Similarly, for CIFAR-100 with DenseNet, the accuracy is 97.93% (Piecewise) vs. 98.39% (Base), with a 1.18x execution time reduction. These results confirm the proposed method’s capability to efficiently process large-scale datasets and computationally demanding tasks, offering a practical means to accelerate deep learning models, including LSTMs, without compromising accuracy.

Activation function↗
DOE OSTI2026

The high level trigger and express data production at STAR

To meet the demands of the Beam Energy Scan phase-II (BES-II) program, the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) developed a dual real-time framework consisting of a High Level Trigger (HLT) and an Express Data Production system (xProduction). The HLT operates online within the Data Acquisition (DAQ) chain on a dedicated multi-core CPU cluster with the option to offload compute-intensive kernels to Xeon Phi coprocessors. It uses parallelized algorithms, such as the Cellular Automaton (CA) Track Finder, to perform rapid tracking, vertexing, and event filtering. This allows it to select events of interest in real time and provide immediate feedback on detector and beam conditions. In contrast, the xProduction workflow runs concurrently and independently of the DAQ loop. It applies near offline-quality calibration and reconstruction within hours of data collection. The xProduction input is the express data stream, whose content can be enriched by HLT trigger/priority selections under DAQ/HLT resource constraints, and it uses the STAR calibration/conditions framework, incorporating online calibration/QA information when available. This enables early preliminary physics analysis, including the reconstruction of rare signals, such as hyperons and hypernuclei. It also provides collaboration-wide access to analysis-ready datasets. Together, the HLT and xProduction systems form a complementary architecture: the HLT performs online event selection while the xProduction chain delivers high-quality results within a short amount of time. This integrated framework has enabled the prompt reconstruction of the $^5_Λ$ He hypernucleus with high statistical significance and the efficient processing of hundreds of millions of heavy-ion collision events. In conclusion, its demonstrated scalability and robustness establish a model for future high-luminosity experiments requiring both online event filtering and rapid access to analysis-quality data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

A novel peridynamics-based approach to predict pharmaceutical tablet robustness

The pharmaceutical drug product development process can be greatly accelerated through the use of modeling and simulation techniques to predict the manufacturability and performance of a given formulation. The anticipation and possible mitigation of tablet damage due to manufacturing stresses represents a specific area of interest in the pharmaceutical industry for predicting formulation and tableting performance. While the finite element method (FEM) has been extensively used for predicting the mechanical behavior of powder material in the compaction processes, a shortcoming of the approach is the inherent difficulty to predict discontinuities (e.g., damage or cracking) within a tablet as FEM is a continuum-based approach. In this work, we propose a novel method utilizing peridynamics (PD), a numerical method that can capture discontinuities such as tablet fracture, to predict the evolution of damage and breakage in pharmaceutical tablets. The approach links (1) the finite element method – to elucidate the behavior of powders during die compaction – with (2) the peridynamics modeling technique – to model the discontinuous nature of damage and predict tablet breakage during the critical stages of unloading and ejection from the compression die. This short communication presents a proof of concept including a workflow to calibrate the linked FEM-PD simulation models. Further, it demonstrates promising results from a preliminary experimental validation of the approach. Following further development, this approach could be used to guide the optimization of compression processes through targeted changes to formulation material properties, compression process conditions, and/or tooling geometries to deliver improved process efficiency and tablet robustness.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Mature landfill leachate utilization using a cost-effective hybrid method

Highlights: • Co-digestion of sewage sludge and cavitated mature landfill leachate is studied. • The use of a small CL dose and optimal co-digestion HRT of 20 days is suggested. • Biogas production rate increases when optimal HRT is ensured. • FT-IR/PAS analysis seems to indicate that no new toxic compounds are formed. • The method is recommended as cost-effective for utilizing mature leachate. - Abstract: The main goal of this study was to investigate the co-digestion of sewage sludge and mature landfill leachate pretreated through hydrodynamic cavitation. The process efficiency was analyzed from the aspects of organics removal, biogas production, kinetics and digestate quality. Energy efficiency of the overall treatment was evaluated as well. A dose of hydrodynamically cavitated leachate of 5% v/v was used as a substrate for co-digestion with sewage sludge at a hydraulic retention time of 20 days. This improved the kinetics, biogas production and corresponding yields, as well as digestate quality. Fourier transform infrared photoacoustic spectroscopy analysis seemed to indicate that no new toxic compounds were formed with co-digestion. The investigated two-stage treatment is recommended as a safe and cost-effective method of utilizing mature leachate.

09 BIOMASS FUELS↗
DOE OSTI2026

Effects of B 2 O 3 on the Growth, Structural, and Magneto-Optical Properties of Yttrium Iron Garnet Single-Crystal Fibers

This study explores the fabrication of yttrium iron garnet (YIG) single crystal fibers using the laser heated pedestal growth (LHPG) method with the experimental addition of B 2 O 3 . The incorporation of B 2 O 3 facilitates the fiber fabrication process by lowering the required growth temperatures and likely modifying melt viscosity behavior, consistent with the established fluxing behavior of B2O3 and the comparative viscosity trend observed in the TMA−VFT analysis, thereby improving process efficiency while maintaining fiber quality. Structural characterization using EBSD and SC-XRD reveals a transition from polycrystalline to single-crystal behavior, with improved alignment along the [111] direction without altering the garnet structure. Magnetic measurements show increases in saturation magnetization in B 2 O 3 -assisted fibers. Three-dimensional anisotropy energy modeling, based on EBSD-derived Euler angles, indicates that the enhanced crystallinity and orientation contribute to reorientation of MCA energy distribution due to improved crystallographic alignment. Faraday rotation measurements show that the B 2 O 3 -assisted sample exhibits a rotation angle closer to reported values for high-quality YIG, suggesting improved phase purity and crystallographic quality. These findings demonstrate that B 2 O 3 -assisted LHPG growth is a scalable and nontoxic approach to producing high-performance YIG fibers for integrated photonic and magnetic field sensing applications.

Crystal structure↗
DOE OSTI2025

Modifying the Optical Emission of Vanadyl Phthalocyanine via Molecular Self-Assembly on van der Waals Materials

Vanadyl phthalocyanine (VOPc) is a promising organic molecule for applications in quantum information because of its thermal stability, efficient processing, and potential as a spin qubit. The deposition of VOPc in different molecular orientations allows the properties to be customized for integration into various devices. However, such customization has yet to be fully leveraged to alter its intrinsic properties, particularly optical emission. Normally, VOPc films on dielectric substrates emit a broad photoluminescence peak in the near-infrared range, attributed to transitions in the Pc ring from its π orbital structure. Here, in this work, we demonstrate that the dominant optical transition of VOPc can be shifted by ∼250 meV through the controlled deposition of thin films on van der Waals material substrates. The weak interactions with van der Waals materials allow the molecules to uniquely self-assemble, resulting in modified optical behavior modulated by the molecular phase and thickness. This work connects the self-assembling properties of molecules to their altered electronic structures and the resulting optical emission.

anions↗
DOE OSTI2026

Effects of a Small Fraction of Norbornene Comonomers on the Frontal Ring Opening Metathesis Polymerization of Dicyclopentadiene and Dihydrofuran

Frontal ring-opening metathesis polymerization (FROMP) is an energy-efficient process for the construction of high performance polydicyclopentadiene (pDCPD) thermosets. A small fraction of norbornene (NBE) comonomer is often added to DCPD to liquify the resin, but their effects on the resin chemistry and frontal curing characteristics are not quantitatively understood, especially for recently developed deconstructable DCPD resins containing cleavable comonomers. 2,3-Dihydrofuran (DHF) is a commercially available, effective cleavable comonomer for ROMP of norbornenes, and it has been recently demonstrated to enable full deconstruction of pDCPD thermosets at 5% loading. Herein, we investigated deconstructable DCPD resins containing 5% of different (oxa)NBE derivatives and 5% DHF. We report the effects of such a small fraction of (oxa)NBE comonomers on the resin pot life (room temperature resin reactivity), front curing characteristics, degree of cure, crosslinking density, comonomer distribution, and deconstruction kinetics of the resulting pDCPD thermosets. These effects can be correlated to the metathesis reactivity of these comonomers. As a result, this study provides quantitative understanding that will guide the future development of new deconstructable FROMP resins with desired construction and deconstruction characteristics.

Luo, Xuyi [Stanford University, CA (United States)↗