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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

In vivo Functional Characterization of Hydrophilic X2 Modules in the Cellulosomal Scaffolding Protein

As part of free cellulases or scaffolding proteins in cellulosomes, the hydrophilic non-catalytic X2 module is widely distributed in cellulolytic Clostridia or other Firmicutes bacteria. Previous biochemical studies suggest that X2 modules might increase the solubility and substrate binding affinity of X2-bearing proteins. However, their in vivo biological functions remain elusive. Here we employed CRISPR-Cas9 editing to genetically modify X2 modules by deleting the conserved motif (NGNT) from the CipC scaffoldin. Both single and double X2 mutants (X2-N: near the N terminus of CipC; X2-C: near the C terminus of CipC) presented similar stoichiometric compositions in isolated cellulosomes as the wildtype strain (WT). These X2 mutants had an elongated adaptation stage during growth on cellulose compared to cellobiose. Compared to WT, the double mutant ΔX2-NC reduced cellulose degradation by 15% and the amount of released soluble sugars by 63%. Since single X2 mutants did not present such obvious physiological changes as ΔX2-NC, there seems to be a functional redundancy between X2 modules in CipC. The in vivo adhesion assay revealed that ΔX2-NC decreased cell attachment to cellulose by 70% but a weaker effect was also overserved in single X2 mutants. These results highlight the in vivo biological role of X2 in increasing cellulose degradation efficiency by enhancing the binding affinity between cells and cellulose, which provides new perspectives for microbial engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Development of Biological and Electrochemical Technologies for the Clean Extraction of Copper and Critical Materials from Low Grade Ores

As we transition toward renewable energy resources and electrification, there will be an increasing demand for critical materials, including copper. While copper is currently mined in the US, processing capacity is not sufficient, and intermediate mining products are shipped to Asia for further processing. The goal of this research was to develop a transformative hydrometallurgical process for the production of copper from low-grade ores that would eliminate the need for smelting and would increase the domestic processing capacity in the US, The project initially focused on the electrochemical reduction of copper concentrate using vanadium, followed by the biological oxidation to produce a stream compatible with existing solvent extraction and electrowinning operations. We discovered an efficient electrochemical process that could produce copper salts from concentrate without the need for biological oxidation, and this technology has been licensed and spun-off into a start-up company. The microbes involved in the current state-of-the-art bioleaching processes were genetically modified to introduced a number of new traits, including increased sulfur oxidation, salt-tolerance, and binding of other critical metals such as cobalt, molybdenum, rhenium, and the rare earth elements. The project was extended to also explore the electrochemical oxidation of copper concentrate using cerium which would potentially eliminate production of hydrogen sulfide that occurs with the reductive leaching process. This technology was found to have slower kinetics, however could it still be developed as an alternative process for domestic copper production and has been found to be applicable to other critical minerals.

42 ENGINEERING↗
DOE OSTI2023

Selecting Minimal Motion Primitive Libraries with Genetic Algorithms

Motion primitives allow for application of discrete search algorithms to rapidly produce trajectories in complex continuous space. The maneuver automaton (MA) provides an elegant formulation for creating a primitive library based on trims and maneuvers. However, performance is fundamentally limited by the contents of the primitive library. If the library is too sparse, performance can be poor in terms of path cost, whereas a library that is too large can increase run time. This work outlines new methods for using genetic algorithms to prune a primitive library. The proposed methods balance the path cost and planning time while maintaining the reachability of the MA. The genetic algorithm in this paper evaluates and mutates populations of motion primitive libraries to optimize both objectives. Here, we illustrate the performance of these methods with a simulated study using a nonlinear medium-fidelity F-16 model. We optimize a library with the presented algorithm for obstacle-free navigation and a nap-of-the-Earth navigation task. In the obstacle-free navigation task, we show a tradeoff of a 10.16% higher planning cost for a 96.63% improvement in run time. In the nap-of-the-Earth task, we show a tradeoff of a 9.712% higher planning cost for a 92.06% improvement in run time.

42 ENGINEERING↗
DOE OSTI2024

Stress intensity factor models using mechanics-guided decomposition and symbolic regression

The finite element method can be used to compute accurate stress intensity factors (SIFs) for cracks with complex geometries and boundary conditions. In contrast, handbook solutions act as surrogate SIF models that provide significantly faster evaluation times. However, the development of conventional surrogate SIF models relies on manual development based on low-order parameterizations. This limits surrogate model accuracy and generalizability. Here, in this paper, we develop a framework for the automated development of mechanics-guided handbook SIF solutions by using interpretable machine learning via genetic programming for symbolic regression (GPSR). Formalizing the mechanics-based approach of Raju and Newman, SIF training data is decomposed into multiple subsets. This decomposition enables parallel GPSR model development of subfunctions, each of which accounts for specific geometrical corrections with respect to a known analytical model. Using this mechanics-based approach with GPSR allows for equations to be learned with improved accuracy and reduced complexity relative to the Raju Newman equations while maintaining the inherent interpretability of mathematical expressions. In this paper, we present equations that match the complexity of the Raju Newman equations while having reduced error, as well as equations with similar errors and reduced complexity.

42 ENGINEERING↗
DOE OSTI2024

Regulation of Absorption and Emission in a Protein/Fluorophore Complex

Human cellular retinol binding protein II (hCRBPII) was used as a protein engineering platform to rationally regulate absorptive and emissive properties of a covalently bound fluorogenic dye. We demonstrate the binding of a thio-dapoxyl analog via formation of a protonated imine between an active site lysine residue and the chromophore’s aldehyde. Rational manipulation of the electrostatics of the binding pocket results in a 204 nm shift in absorption and a 131 nm shift in emission. The protein is readily expressed in mammalian systems and binds with exogenously delivered fluorophore as demonstrated by live-cell imaging experiments.

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

Solid-Binding Proteins: Bridging Synthesis, Assembly, and Function in Hybrid and Hierarchical Materials Fabrication

There is considerable interest in the development of hybrid organic–inorganic materials because of the potential for harvesting the unique capabilities that each system has to offer. Proteins are an especially attractive organic component owing to the high amount of chemical information encoded in their amino acid sequence, their amenability to molecular and computational (re)design, and the many structures and functions they specify. Genetic installation of solid-binding peptides (SBPs) within protein frameworks affords control over the position and orientation of adhesive and morphogenetic segments, and a path toward predictive synthesis and assembly of functional materials and devices, all while harnessing the built-in properties of the host scaffold. Furthermore, we review the current understanding of the mechanisms through which SBPs bind to technologically relevant interfaces, with an emphasis on the variables that influence the process, and highlight the last decade of progress in the use of solid-binding proteins for hybrid and hierarchical materials synthesis.

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

Numerical modeling based machine learning approach for the optimization of falling - film evaporator in thermal desalination application

Scale formation that drastically increases thermal resistance and reduces freshwater production remains a critical challenge in thermal desalination. Novel designs of falling film evaporator and optimal operating condition hold great promise to mitigate scale formation, and increase heat transfer performance and fresh water production. In this work, CFD simulation based machine learning and multi-objective optimization are performed to identify optimal conditions and tube arrangement for evaporator. Non-dominated sorting genetic algorithm is adopted to determine and analyze the optimal pareto front for multiple objectives in desalination criteria. The errors of training, validation, and testing set are computed to identify an optimal hyperparameter set. For performance ratio, fouling resistance, and water production rate, the average relative error is 2.26%, 3.67%, and 3.24%. At pareto front, both performance ratio and water production rate increase at high temperature with fouling resistance (thermal resistance of the fouling layer) increasing as well. Tradeoffs between mitigating scale formation and enhancing desalination performance are evaluated in optimizations for different objectives. Finally, potential optima are identified and can be applied as guidelines to determine evaporator design and system operating conditions.

42 ENGINEERING↗
DOE OSTI2026

Hydrogen Production System Scaling Using a High-Fidelity Simulation-Optimization Framework

Proton exchange membrane (PEM) electrolyzers are widely used for hydrogen production, yet few validated, high-fidelity tools can reliably guide scale-up. Using measured performance from a 50-hour hardware-in-the-loop pilot test, a physics-based, plant-level model of a 1.25 MW PEM electrolyzer and its balance-of-plant (BoP) subsystems is developed and validated. The model couples electrochemistry and thermal/flow submodels and is calibrated against pilot test data via a genetic algorithm (GA) workflow. Validation yields a mean absolute percentage error (APE) of 0.43% for cell voltage and stack power. Two scale-out strategies are then benchmarked under a common 7-day wind-and-photovoltaic (PV) profile: (i) linear duplication of 1.25 MW blocks and (ii) shared-BoP architectures. Sharing BoP between stacks reduces BoP energy by 27% at 10 MW and 34% at 100 MW (vs. linear duplication) and improves system specific energy consumption (SEC) to 52.9 and 52.6 kWh/kg, respectively (from 54.0 kWh/kg with linear duplication). Partial-load studies (25-100% set-point) show that cumulative hydrogen production remains nearly constant down to 50% load because all cases use the same weekly renewable-energy input. Below 50%, the power cap limits how much energy can be used within 168 h, which reduces hydrogen output. The model further indicates that the practical operating optimum lies between 50% and 85% load, where efficiency gains begin to appear without significant loss in hydrogen output. Moreover, the efficiency gains at lower loads are offset by reduced production. The validated framework supports scenario-based engineering trade-off studies for large configurations (10-100 MW) and for operating policies under variable renewables.

08 HYDROGEN↗
DOE OSTI2023

Rare ribosomal RNA sequences from archaea stabilize the bacterial ribosome

The ribosome serves as the universally conserved translator of the genetic code into proteins and supports life across diverse temperatures ranging from below freezing to above 120°C. Ribosomes are capable of functioning across this wide range of temperatures even though the catalytic site for peptide bond formation, the peptidyl transferase center, is nearly universally conserved. Here we find that Thermoproteota, a phylum of thermophilic Archaea, substitute cytidine for uridine at large subunit rRNA positions 2554 and 2555 (Escherichia coli numbering) in the A loop, immediately adjacent to the binding site for the 3'-end of A-site tRNA. We show by cryo-EM that E. coli ribosomes with uridine to cytidine mutations at these positions retain the proper fold and post-transcriptional modification of the A loop. Additionally, these mutations do not affect cellular growth, protect the large ribosomal subunit from thermal denaturation, and increase the mutational robustness of nucleotides in the peptidyl transferase center. This work identifies sequence variation across archaeal ribosomes in the peptidyl transferase center that likely confers stabilization of the ribosome at high temperatures and develops a stable mutant bacterial ribosome that can act as a scaffold for future ribosome engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Benchmarking the Performance of Neuromorphic and Spiking Neural Network Simulators

Software simulators play a critical role in the development of new algorithms and system architectures in any field of engineering. Neuromorphic computing, which has shown potential in building brain-inspired energy-efficient hardware, suffers a slow-down in the development cycle due to a lack of flexible and easy-to-use simulators of either neuromorphic hardware itself or of spiking neural networks (SNNs), the type of neural network computation executed on most neuromorphic systems. While there are several openly available neuromorphic or SNN simulation packages developed by a variety of research groups, they have mostly targeted computational neuroscience simulations, and only a few have targeted small-scale machine learning tasks with SNNs. Evaluations or comparisons of these simulators have often targeted computational neuroscience-style workloads. In this work, we seek to evaluate the performance of several publicly available SNN simulators with respect to non-computational neuroscience workloads, in terms of speed, flexibility, and scalability. We evaluate the performance of the NEST, Brian2, Brian2GeNN, BindsNET and Nengo packages under a common front-end neuromorphic framework. Our evaluation tasks include a variety of different network architectures and workload types to mimic the computation common in different algorithms, including feed-forward network inference, genetic algorithms, and reservoir computing. We also study the scalability of each of these simulators when running on different computing hardware, from single core CPU workstations to multi-node supercomputers. Our results show that the BindsNET simulator has the best speed and scalability for most of the SNN workloads (sparse, dense, and layered SNN architectures) on a single core CPU. However, when comparing the simulators leveraging the GPU capabilities, Brian2GeNN outperforms the others for these workloads in terms of scalability. NEST performs the best for small sparse networks and is also the most flexible simulator in terms of reconfiguration capability NEST shows a speedup of at least 2x compared to the other packages when running evolutionary algorithms for SNNs. The multi-node and multi-thread capabilities of NEST show at least 2x speedup compared to the rest of the simulators (single core CPU or GPU based simulators) for large and sparse networks. We conclude our work by providing a set of recommendations on the suitability of employing these simulators for different tasks and scales of operations. We also present the characteristics for a future generic ideal SNN simulator for different neuromorphic computing workloads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Template and target-site recognition by human LINE-1 in retrotransposition

The long interspersed element-1 (LINE-1, hereafter L1) retrotransposon has generated nearly one-third of the human genome and serves as an active source of genetic diversity and human disease. L1 spreads through a mechanism termed target-primed reverse transcription, in which the encoded enzyme (ORF2p) nicks the target DNA to prime reverse transcription of its own or non-self RNAs. Here we purified full-length L1 ORF2p and biochemically reconstituted robust target-primed reverse transcription with template RNA and target-site DNA. We report cryo-electron microscopy structures of the complete human L1 ORF2p bound to structured template RNAs and initiating cDNA synthesis. The template polyadenosine tract is recognized in a sequence-specific manner by five distinct domains. Among them, an RNA-binding domain bends the template backbone to allow engagement of an RNA hairpin stem with the L1 ORF2p C-terminal segment. Moreover, structure and biochemical reconstitutions demonstrate an unexpected target-site requirement: L1 ORF2p relies on upstream single-stranded DNA to position the adjacent duplex in the endonuclease active site for nicking of the longer DNA strand, with a single nick generating a staggered DNA break. Our research provides insights into the mechanism of ongoing transposition in the human genome and informs the engineering of retrotransposon proteins for gene therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Tutorial: Machine-Learning-Based CREASE-2D Analysis of 2D SAXS Profiles to Characterize Anisotropic Nanostructures in Soft Materials

We present a tutorial to guide users on how to extend the Computational Reverse Engineering Analysis of Scattering Experiments-2D (CREASE-2D) framework to interpret their experimental two-dimensional small-angle scattering (SAS) data from soft materials (e.g., polymers, peptide amphiphiles, biomolecular fibrils). Unlike most traditional SAS analysis approaches, which typically rely on azimuthally averaged onedimensional (1D) profiles, CREASE-2D utilizes the complete 2D scattering profile to reveal information about anisotropy in the structure. In past applications, CREASE has provided insights into complex structural features, including the cross-sectional shapes of assembled nanostructures and dispersity in these features, which are difficult to discern with existing analytical models. While (1D- ) CREASE has been applied to SANS and SAXS data, this tutorial shares the steps for implementing CREASE-2D using an example of a dipeptide solution system, for which we have SAXS data. We present details for these steps involved in using CREASE-2D to interpret SAXS profiles: how to preprocess SAXS data, define relevant structural features, generate three-dimensional real-space structures for specific values of these features, train a machine learning (ML) surrogate model to predict scattering profiles for given structural features, and optimize these features using genetic algorithms (GA). Then, we use these steps to interpret complex 2DSAXS data collected from dipeptide solutions that, in microscopy images, exhibit nanoscale structures that could be elliptical tubes/ flat tapes/cylinders or a combination of these cross sections. Open-source codes, computational hardware, and software requirements, as well as the strengths and limitations of this protocol, are also presented. We expect researchers working with (soft) biomaterials, peptide amphiphiles, amphiphilic polymer solutions, polymer nanocomposites, and blends of particles/polymers will find this CREASE-2D method and this tutorial of use.

CREASE↗
DOE OSTI2024

A novel electro-hydraulic unit design based on a shaftless integration of an internal gear machine and a permanent magnet electric machine

In recent years, increasingly stringent emission regulations have spurred an electrification trend in off-highway vehicle technology. To address challenges such as the high cost of power electronics components, limited battery capacity, and the substantial modifications required for the vehicles, there is a pressing need to develop high-speed, cost-effective, compact, and efficient electro-hydraulic units capable of powering vehicle functions. In response to these demands, this paper introduces an innovative morphology for an electro-hydraulic unit and outlines the integration method for a crescent-type internal gear machine with a permanent magnet synchronous electric machine. The proposed morphology aims to minimize component count through a shaftless solution while incorporating a cooling system that utilizes the same working fluid as the hydraulic machine. The design approach utilizes a genetic algorithm optimization process to maximize overall energy efficiency and compactness. Insights gained from the optimization results shed light on the relationship between key design parameters and unit performance, enhancing the understanding of this electro-hydraulic unit. A prototype of the unit was manufactured and tested, demonstrating a volumetric efficiency ranging from 81 % to 97 % at a maximum rotational velocity of the pinion of 6000 rpm. Finally, these results validate both the morphology and the design approach, indicating the feasibility of designing compact electro-hydraulic units that leverage hydraulic machines with higher maximum rotational velocities than commercially available counterparts as a mean to enhance efficiency and compactness.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Transcriptomic Profiles of Zymomonas mobilis 8b to Furfural Acute and Long-Term Stress in Both Glucose and Xylose Conditions

Zymomonas mobilis 8b is an ethanologenic bacterium engineered to utilize both glucose and xylose. The impacts of lignocellulosic hydrolyzate inhibitors on the growth of Zymomonas mobilis 8b have been investigated. However, the molecular responses of these inhibitors have not been completely elucidated yet. In this study, molecular responses to furfural were investigated using transcriptomic approaches of both chip-based microarray and a directional mRNA-Seq. Furfural acute shock time-course experiment with 3 g/L furfural supplemented when cells reached exponential phase and stress response experiment in the presence of 2 g/L furfural from the beginning of fermentation were carried out to study the physiological and transcriptional profiles of short-term and long-term effects of furfural on 8b. Furfural negatively affected 8b growth in terms of final biomass and the fermentation time. Transcriptomic studies indicated that the response of 8b to furfural was dynamic and complex, and differences existed between short-term shock and long-term stress responses. However, the gene function categories were similar with most down-regulated genes related to translation and biosynthesis, while the furfural up-regulated genes were mostly related to general stress responses. Several gene candidates have been identified and genetic studies indicated that expression of ZMO0465 and cysteine synthase operon ZMO0003-0006 driven by its native promoter in a shuttle vector enhanced the furfural tolerance of 8b. In addition, the relationship between microarray and mRNA-Seq was compared with good correlations. The directional mRNA-Seq data not only provided the gene expression profiling, but also can be applied for transcriptional architecture improvement to identify and confirm operons, novel transcripts, hypothetical gene functions, transcriptional start sites, and promoters with different strength.

09 BIOMASS FUELS↗
DOE OSTI2021

Enhancing transcription in Escherichia coli and Pseudomonas putida using bacteriophage lambda anti-terminator protein Q

Functional characterization of metagenomic DNA often involves expressing heterologous DNA in genetically tractable microorganisms such as Escherichia coli . Functional expression of heterologous genes can suffer from limitations due to the lack of recognition of foreign promoters or presence of intrinsic terminators on foreign DNA between a vector-based promoter and the transcription start site. Anti-terminator proteins are a possible solution to overcome this limitation. When bacteriophage lambda infects E. coli , it relies on the host transcription machinery to transcribe and express phage DNA. Lambda anti-terminator protein Q (λQ) regulates the expression of late-genes of phage lambda. E. coli RNA polymerase recognizes the P R ' promoter on the lambda genome and forms a complex with λQ, to overcome the terminator t R '. In this study we show the use of λQ to efficiently transcribe a capsular polysaccharide cluster, cps3 , from Lactobacillus plantarum containing intrinsic terminators in Escherichia coli . In addition, we expand the use of anti-terminator λQ in Pseudomonas putida . The results show ~ fivefold higher expression of a fluorescent reporter locate ~ 12.5kbp downstream from the promoter, when the transcription is driven by P R ' promoter in presence of λQ compared to a lac promoter. These results suggest that λQ could be used in metabolic engineering to enhance expression of heterologous DNA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Constitutive model development of aluminum alloy 1100 for elevated temperature forming process

Commercially pure aluminum alloy, AA1100, presents good electrical and thermal conductivity, high formability, and low cost. Those favorable characteristics have the potential to enable bipolar plates with improved economics and enhanced performance compared to current stainless steel bipolar plates for proton exchange membrane fuel cells. An accurate constitutive model is essential to develop and optimize processing parameters and effectively control the forming process. Here, the objective of this work is to develop a constitutive model of AA1100 that is able to simulate stress-strain relation, formed geometry, and predict the onset of fracture strain to avoid forming failure. Initially, a set of tensile tests at temperature between 300 and 500°C and strain rate between 0.005 and 1.0/s were conducted to examine the deformation behavior. Then, a set of damage-based unified visco-plastic constitutive equations is proposed and calibrated based on the results of stress-strain data. A genetic algorithm optimization method is applied to search for best fitting material constants in constitutive equations. The proposed model shows good predictability of both the stress-strain relation and fracture strain at low strain rate and high temperature conditions. The accuracy of proposed model is also evaluated statistically. A comparison of the proposed model with three popular models (Arrhenius-type mode, Johnson-Cook model and Zerilli-Armstrong model) was made. The proposed model shows the best experimental agreement with correlation coefficient of 0.96 in contrast to 0.25, 0.38 and 0.75 for the popular models, respectively. The proposed model can help to optimize the elevated temperature forming process and guide die design to enable optimal geometric features in the formed components.

08 HYDROGEN↗
DOE OSTI2021

Structural engineering of chimeric antigen receptors targeting HLA-restricted neoantigens

Chimeric antigen receptor (CAR) T cells have emerged as a promising class of therapeutic agents, generating remarkable responses in the clinic for a subset of human cancers. One major challenge precluding the wider implementation of CAR therapy is the paucity of tumor-specific antigens. Here, we describe the development of a CAR targeting the tumor-specific isocitrate dehydrogenase 2 (IDH2) with R140Q mutation presented on the cell surface in complex with a common human leukocyte antigen allele, HLA-B*07:02. Engineering of the hinge domain of the CAR, as well as crystal structure-guided optimization of the IDH2 R140Q- HLA-B*07:02-targeting moiety, enhances the sensitivity and specificity of CARs to enable targeting of this HLA-restricted neoantigen. This approach thus holds promise for the development and optimization of immunotherapies specific to other cancer driver mutations that are difficult to target by conventional means.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Planar Transformer With Asymmetric Integrated Leakage Inductance Using Horizontal Air Gap

This article presents a novel planar-based transformer winding and core structure with controllable leakage inductance generation for integrated magnetics applications. As a result of limitations in integrated magnetics from the literature, a new approach is proposed utilizing semi-interleaved windings and controllable leakage via a leakage flux core leg featuring a horizontal air gap. Here, the proposed transformer design is analyzed via detailed reluctance modeling to determine closed-form equations for the magnetizing and asymmetrically distributed leakage inductances. Next, a genetic-algorithm-based multiobjective design optimization problem is developed, seeking to minimize the core and winding losses of the proposed transformer subject to a set of parametric and geometric constraints in a dc-ac dual-active-bridge topology for microinverter applications. The optimization was extended to include other integrated magnetic structures from the literature, where it is determined that the proposed transformer is superior from the perspectives of efficiency, footprint area, and parasitic capacitance. Based on the results of the optimization analysis, two designs with theoretical transformer California Energy Commission (CEC) efficiency drops (i.e., CEC efficiency reduction specifically due to the transformer loss mechanisms) of 1.19% and 0.83% were fabricated and evaluated for electrical and thermal performance in the proposed 40 V, 400 W microinverter.

42 ENGINEERING↗