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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cladding and Butt-Joining Dissimilar Aluminum Alloys Simultaneously via Shear Assisted Processing and Extrusion

Shear assisted processing and extrusion has been used to fabricate multi-metallic tubing that incorporates regions with clad and butt-joined structures. In this work, aluminum alloys 6061, 7075, and 1100 are combined to create a monolithic extruded tubing with weldable ends (6061), a high strength center (7075), and a corrosion-resistant cladding (1100). Optical microscopy shows a gradual, defect-free, transition between alloys at the butt-joint interfaces and a sharp transition between alloys at the clad interface. Tensile testing proved the mechanical coherency of the joint, with failure occurring outside the tri-metallic structure. In conclusion, smoothed particle hydrodynamic simulations identified a spiral pattern of the cladding alloy that leads to inconsistent cladding thickness, and identified a simplified 2-alloy combination of aluminum alloys 6082 and 2014 that accomplished the design objectives of the tri-metallic structure.

Coatings↗
DOE OSTI2018

Efficient Surrogate Model Development: Impact of Sample Size and Underlying Model Dimensions

This study compares eight surrogate-model construction approaches using computational experiments. Here, the construction approaches considered include: Artificial Neural Networks (ANNs), Automated Learning of Algebraic Models using Optimization (ALAMO), Radial Basis Networks (RBNs), Extreme Learning Machines (ELMs), Gaussian Progress Regression (GPR), Random Forests (RFs), Support Vector Regression (SVR), and Multivariate Adaptive Regression Splines. Each approach is used to construct surrogate models for predicting the outputs of thirty-four test functions. The data used to construct surrogate models were generated using Latin Hypercube Sampling (LHS), Halton and Sobol sampling methods. We studied the impact of sampling method, sample size, and challenge function characteristics on the accuracy of surrogate model predictions. Our results revealed that for smaller sample sizes, on average, surrogate models trained using the data points generated by Sobol sequences provided the best estimation for all surrogate model construction approaches. As the sample size increased, the impact of sampling methods diminished. The comparison of surrogate-modeling construction approaches at large sample sizes revealed that surrogate models trained using ANN, ALAMO, and ELM yielded smaller root mean squared error and higher adjusted R-squared values than the models trained using the rest of the approaches.

42 ENGINEERING↗
DOE OSTI2022

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO 2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10 6 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Biochemical Production with Purified Cell-Free Systems

Bioprocesses offer a powerful set of opportunities for current chemical process innovation. Such conversions provide opportunities to employ process intensification strategies and the right characteristics to scale down and harness the power of modular process design, but strain engineering and the use of microbial systems for industrial biochemicals production remain unpredictable and expensive. Purified cell-free technologies, in which complex enzyme reaction cascades are used to catalyze bioconversions of interest, can be designed to mitigate these limitations. Such systems allow predictive kinetic modeling and full control of reaction conductions, enabling a dramatically increased design-built-test cycle iteration rate. When sufficiently robust engineered enzymes are used, opportunities to leverage harsh reaction conditions, long-term recycling strategies, and hybridization with chemical catalysis can be used to dramatically improve the value proposition of biochemicals production. As a result, further research is required to realize the potential of these approaches and to address the current platform-limiting challenges around cofactor cost and reuse.

09 BIOMASS FUELS↗
DOE OSTI2023

Evaluation of enzymatic depolymerization of PET, PTT, and PBT polyesters

Millions of tons of waste polyester plastics, including polyethylene terephthalate (PET), polytrimethylene terephthalate (PTT), and polybutylene terephthalate (PBT), end up in the environment as soil and water contaminants. Recent advances in enzymatic polyester degradation have motivated researchers toward biorecycling of plastic wastes. Leaf-branch compost cutinase (LCC) enzymes have been proven to be effective in the biodegradation of PET. This study focuses on enzymatic depolymerization of waste PET, PTT, and PBT materials by using an ICCG variant of LCC (LCC ICCG ) produced from Escherichia coli BL21(DE3). The degradation efficiency of the polyesters was determined by the monomer terephthalic acid (TPA) released from the depolymerization reaction. It was found that the most efficient depolymerization was achieved for PET, followed by PTT and PBT. A kinetic model based on Langmuir adsorption and the Michaelis-Menten equation was developed to describe the enzymatic depolymerization of PET, PTT, and PBT with various enzyme and substrate loadings. The model simulation results revealed that the LCCICCG enzyme loading should be linearly increased as the work capacity of the polyester substrate increases. A specific enzyme loading of 0.91 mg/g PET is suggested to achieve 90% depolymerization of PET within three days. Furthermore, the experimental data and model simulation results can be used to help further engineer the enzyme and process to achieve a complete biodegradation of polyester wastes at large scale.

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

Exploring the potential and impact of single-crystal active materials on dry-processed electrodes for high-performance lithium-ion batteries

Roll-to-roll powder-to-film dry processing (DP) and single-crystal (SC) active materials (AMs) with many advantages are two hot topics of lithium-ion batteries (LIBs). However, DP of SC AMs for LIBs is rarely reported. Consequently, the impact of SC AMs on dry-processed LIBs is not well understood. Herein, for the first time, via a set of experimental and theoretical studies of the conventional polycrystalline-AM- and SC-AM-based DPed electrodes (DPEs), this work not only reports a high-performance dry SC-AM cathode for LIB manufacturing, but also establishes some fundamental understanding of SC-based dry-processed electrodes, including their morphology, structure, mechanical strength, electronic conductivity and LIB electrochemical behavior. Further, the results suggest that DP of SC AMs is promising, which can dramatically improve the electrochemical kinetics at electrode level and particle level. Specifically, for the rate capability and long-term cyclability in full cells, SC DPEs exhibit a discharge specific capacity of 152.1 mAh g -1 at 1C and a capacity retention rate of 79.9 % at C/3 over 500 cycles, which are superior to those of PC DPEs (135.6 mAh g -1 and 68.3 %) at the same conditions and are further confirmed by the simulation data from the theoretical modelling study. Therefore, this comprehensive work marks a significant milestone for DP strategy and SC AMs, enlightening future research and development of LIB manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2018

A superstructure optimization approach for process synthesis under complex reaction networks

Here in this work, we present mixed integer linear programming methods for the synthesis of processes that involve complex reaction networks. Specifically, we consider the modeling of reactors and interconnecting streams in systems where the composition of the reactor inlet streams can vary substantially, thereby making the determination of the limiting component as well as the calculation of the stream heating/cooling and power requirements challenging. First, towards the modeling of reactors, we develop an extent-based method which detects the limiting reactant of each reaction occurring in parallel with others, based on the inlet flows of the reactants. Second, we develop a computationally tractable method for the calculation of the work and heating/cooling duty needed to condition any stream of a process based on simple calculations that can be performed offline. Finally, we present how the two aforementioned components can be integrated in an optimization model generated based on a process superstructure. We demonstrate the application of the developed methods for the synthesis of a biorefinery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

A techno-economic assessment of membrane distillation for treatment of Marcellus shale produced water

Membrane distillation (MD) is a promising desalination technology for treatment of high salinity shale gas produced water. Techno-economic assessment (TEA) is necessary for evaluating the economic feasibility of MD for produced water treatment as compared to other shale gas produced water management strategies. A detailed TEA for a hypothetical 0.5 million gallons per day (MGD) direct contact MD (DCMD) that concentrates produced water from 10% (100,000 mg/L) Total Dissolved Solids (TDS) to 30% salinity is presented in this study. The model is developed based on a combination of experimental results, ASPEN Plus process model, best available engineering knowledge, and cost estimates. Analysis reveals that thermal energy cost for MD operation contributes the most to total cost of treating produced water in an MD plant. Additionally, the results of sensitivity analysis reveal that feed TDS level and thermal energy price have a significant impact on total cost of treating produced water. We also explore the implications of utilizing waste heat on the economics of the MD technology for produced water treatment. Here the results reveal that the total cost of treating produced water using MD is $\$$5.70/m 3 feed which decreases significantly to $\$$0.74/m 3 feed when MD is integrated with a source of waste heat.

42 ENGINEERING↗
DOE OSTI2019

Biogenic coal-to-methane conversion can be enhanced with small additions of algal amendment in field-relevant upflow column reactors

Subsurface coal environments, where biogenic coal-to-methane conversion occurs, are difficult to access, resulting in inherent challenges and expenses for in situ experiments. Previous batch reactor studies provided insights into specific processes, pathways, kinetics, and engineering strategies, but field-relevance is restricted due to limited substrate availability or byproduct accumulation that may influence reactions or metabolisms. In this study, continuous-flow column reactors were used to overcome some batch limitations, improve the understanding of in situ conditions, and increase field-relevance for subsurface engineering technology development. The bench-scale reactor system was constructed to investigate the addition of algal amendment for enhancing microbial coal-to-methane conversion previously developed in batch systems. Four reactor columns were packed with coal and inoculated with a microbial consortium from the same Flowers-Goodale coal bed. Two reactors were amended with 13 C-labeled algal amendment on day 0, and two were unamended. On day 61, one previously amended and one previously unamended reactor were re-amended. Produced gases were captured in a gas trap, and CH 4 and CO 2 were quantified. The reactor amended twice produced 1712.6 µmol CH 4 (4.6% as 13 CH 4 ). The reactor amended only on day 0 produced 1485.5 µmol CH 4 (2.6% as 13 CH 4 ). The reactor amended only on day 61 produced 278.9 µmol CH 4 (3.9% as 13 CH 4 ). The reactor with no amendment produced no measurable gases for the duration of the 172-day experiment. Amendment increased the rate of coal-to-methane conversion and total gas production; most of the produced gases were due to coal conversion with only small contributions (<7%) from amendment conversion.

09 BIOMASS FUELS↗
DOE OSTI2025

Leveraging large language models to automate the identification of healthcare access barriers for veterans

Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.

Healthcare access barriers↗
DOE OSTI2019

Structural representation of additively manufactured 316L austenitic stainless steel

Three 316L stainless steel materials are studied and reported upon; wrought, as-built additively manufactured (AM), and heat-treated AM material. The AM material was produced from the laser engineered net shaping (LENS) process. Macroscopic uniaxial compression stress-strain curves were obtained for all three materials. The curves were similar for the wrought and heat-treated AM materials but the as-built AM material demonstrated approximately 1.7 times greater flow stress at any given level of strain than the other two materials. Electron-backscatter diffraction analysis of the materials also showed that the microstructures of the three materials differed; with complex grain morphology for the as-built AM material. The mean grain size of each of the three materials also differed. Furthermore, the initial dislocation density was also measured with neutron diffraction and line-profile analysis for both wrought and as-built AM materials with the density in the AM material approximately 2.5 times greater.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Unraveling the impact of the degree of dry mixing on dry-processed lithium-ion battery electrodes

Dry processing of lithium-ion battery electrodes facilely realizes the powder-to-film manner, which is thus regarded as a highly promising strategy for lithium-ion battery manufacturing. However, a fundamental understanding of the impact of the involved dry mixing is still rarely reported. In this work, the degree of dry mixing is monitored by the dry mixing time, and a set of dry-processed electrodes with different degree of dry mixing is accordingly prepared and comprehensively studied. This work novelly reveals that the degree of dry mixing exhibits pronounced impact on the morphology, the homogeneity of electrode components and the degree of PTFE fiberization, which leads to difference in the mechanical strength and electrochemical performance of dry-processed electrodes. Accordingly, it is suggested that a moderate degree of dry mixing is preferred for high-performance dry-processed lithium-ion battery electrodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Carbon molecular sieve hollow fiber membranes derived from dip-coated precursor hollow fibers comprising nanoparticles

We report a novel approach to economically fabricate dual-layer composite nanoparticle-containing carbon molecular sieve (CMS) hollow fiber membranes having excellent gas separation performance. The economically favored process comprises dip coating engineered support layer fibers to form a dense skin layer in a CMS precursor. The coated fibers are then pyrolyzed to form a high-performance CMS hollow fiber membrane. The nano-particle containing composite carbon molecular sieve hollow fiber membranes showed very attractive selectivities and productivities. As a result, this work shows how to produce high performance CMS hollow fiber membrane by coating, which exceeds that achieved with the standard sol-gel support stabilization technique.

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

Understanding the ignition process and flame structure of conventional and oxygenated fuels under engine relevant conditions – An optical study

Renewably generated synthetic fuels such as poly-oxymethylene ethers (OME) have a significant potential to effectively break the soot-NOX trade-off in compression ignition engines by using exhaust gas recirculation (EGR) to maintain low nitrogen oxide (NOX) emissions while maintaining good efficiency and simultaneously contributing to circular carbon economy. However, owing to the fundamental differences in properties of OME when compared to fossil-based diesel fuels, it is critical to fully understand its ignition and combustion phenomenology to take advantage of this fuel to its utmost potential. In this context, this work outlines the results of a systematic experimental study performed in a heavy-duty, single-cylinder, optical engine probing the spatial and temporal progression of fuel decomposition and ignition behavior of OME when compared to n-dodecane, a diesel-fuel surrogate. Thermodynamic analysis and optical diagnostics techniques including simultaneous HCHO-PLIF and OH-PLIF complemented by high-speed OH* chemiluminescence were employed along with parametric sweeps of intake temperature and EGR dilution rates. OME does not exhibit any observable low temperature heat release irrespective of the ambient oxygen concentration. Differences in the observed diffusive flame structure such as longer flame lift-off length, less pronounced combustion recession, faster premixed burn at ignition (“volumetric” ignition), non-sooting behavior suggest that the inherent presence of fuel-bound oxygen in OME can skew the air-fuel ratio (AFR) distribution within the jet thereby reducing the reliance of combustion on mixing and air entrainment. This leads to rapid late-cycle oxidation leading to shorter combustion duration and favorable combustion phasing. Results also suggest that OME exhibits relatively weak negative temperature coefficient (NTC) behavior, however, the OME fuel-decomposition kinetic-pathways produce significant concentration of HCHO, which might be erroneously interpreted as a product of cool-flames.

Air-fuel ratio↗
DOE OSTI2016

Copper Nanoparticle Induced Cytotoxicity to Nitrifying Bacteria in Wastewater Treatment: A Mechanistic Copper Speciation Study by X-ray Absorption Spectroscopy

With the inclusion of engineered nanomaterials in industrial processes and consumer products, wastewater treatment plants (WWTPs) could serve as a major sink for these emerging contaminants. Previous research has demonstrated that nanomaterials are potentially toxic to microbial communities utilized in biological wastewater treatment (BWT). Copper-based nanoparticles (CuNPs) are of particular interest based on their increasing use in wood treatment, paints, household products, coatings, and byproducts of semiconductor manufacturing. A critical step in BWT is nutrient removal through nitrification. This study examined the potential toxicity of uncoated and polyvinylpyrrolidone (PVP)-coated CuO, and Cu 2 O nanoparticles, as well as Cu ions to microbial communities responsible for nitrification in BWT. Inhibition was inferred from changes to the specific oxygen uptake rate (sOUR) in the absence and presence of Cu ions and CuNPs. X-ray absorption fine structure spectroscopy, with linear combination fitting (LCF), was utilized to track changes to Cu speciation throughout exposure. Results indicate that the dissolution of Cu ions from CuNPs drive microbial inhibition. Here, the presence of a PVP coating on CuNPs has little effect on inhibition. LCF analysis of the biomass combined with metal partitioning analysis supports the current hypothesis that Cu-induced cytotoxicity is primarily caused by reactive oxygen species formed from ionic Cu in solution via catalytic reaction intermediated by reduced Cu(I) species.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Side Chain Engineering of Near-IR Aza-BODIPY Dyes Enables Processable Films with High Hole Mobility in Diodes

Boron chelated azadipyrromethene (Aza-BODIPY) derivatives are promising candidates for near-infrared optoelectronic applications due to their tunable absorption, electron-deficient core, and strong π-conjugation. Here, in this work, we synthesized and studied a series of 1-naphthylethynyl-substituted aza-BODIPY molecules featuring hexyl or hexyloxy solubilizing groups at either the proximal or the distal phenyl positions. These substitutions were designed to improve the solubility, film formation, and optoelectronic properties. We found that solubilizing groups not only enabled uniform solution-processed films but also fine-tuned key properties, such as absorption onset, oxidation potential, and energy levels. Charge transport analysis showed consistently high hole mobilities (∼10 –3 cm 2 /V s) across all substituted derivatives when CuI was used as the hole transport layer, regardless of film morphology. In contrast, electron mobilities tended to be low and inconsistent, partly because of the relatively poor-quality films on the ZnO electron transport layer. Overall, we have shown that solubilizing groups enable film solution-casting and can fine-tune the properties of these near-infrared aza-BODIPY derivatives.

36 MATERIALS SCIENCE↗