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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Dataset about Warming Effects on Carbon Cycling and Greenhouse Gas Fluxes in Permafrost Ecosystems

Field observations provide direct evidence of how does carbon cycling in permafrost ecosystems respond to climate change. This study provides a comprehensive dataset on the impact of warming on carbon cycling and greenhouse gas (GHG) fluxes in permafrost ecosystems. The dataset is extracted and integrated from 132 peer-reviewed studies with 1430 paired observations across eight major permafrost ecosystems, including Arctic and subarctic tundra and wetland, and alpine meadow, steppe, tundra and wetland. This dataset includes 17 variables from experiments conducted during the growing season, covering the plant and soil carbon pools, soil nitrogen pool, and GHG (i.e., CO 2 , CH 4 , and N 2 O) fluxes, among others. Background information on site climate conditions, vegetation and soil characteristics, and details of the warming experiments, including timing, methods, and warming magnitude, are also contained in the dataset. This dataset facilitates a comprehensive understanding of the impact of warming on carbon cycling and GHG fluxes in permafrost ecosystems, and provides supports for meta-analyses and literature reviews, remote sensing data validation, and land model development and parameterization.

Bao, Tao [Chinese Academy of Sciences (CAS), Beiji↗
DOE OSTI2020

Dynamics of Global Gene Expression and Regulatory Elements in Growing Brachypodium Root System

Root systems are dynamic and adaptable organs that play critical roles in plant development. However, how roots grow and accumulate biomass during plant life cycle and in relation to shoot growth phenology remains understudied. A comprehensive time-dependent root morphological analysis integrated with molecular signatures is then required to advance our understanding of root growth and development. Here we studied Brachypodium distachyon rooting process by monitoring root morphology, biomass production, and C/N ratios during developmental stages. To provide insight into gene regulation that accompanies root growth, we generated comprehensive transcript profiles of Brachypodium whole-root system at four developmental stages. Our data analysis revealed that multiple biological processes including trehalose metabolism and various families of transcription factors (TFs) were differentially expressed in root system during plant development. In particular, the AUX/IAA, ERFs, WRKY, NAC, and MADS TF family members were upregulated as plant entered the booting/heading stage, while ARFs and GRFs were downregulated suggesting these TF families as important factors involved in specific phases of rooting, and possibly in regulation of transition to plant reproductive stages. We identified several Brachypodium candidate root biomass-promoting genes and cis -regulatory elements for further functional validations and root growth improvements in grasses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Metagenomic features of bioburden serve as outcome indicators in combat extremity wounds

Abstract Battlefield injury management requires specialized care, and wound infection is a frequent complication. Challenges related to characterizing relevant pathogens further complicates treatment. Applying metagenomics to wounds offers a comprehensive path toward assessing microbial genomic fingerprints and could indicate prognostic variables for future decision support tools. Wound specimens from combat-injured U.S. service members, obtained during surgical debridements before delayed wound closure, were subjected to whole metagenome analysis and targeted enrichment of antimicrobial resistance genes. Results did not indicate a singular, common microbial metagenomic profile for wound failure, instead reflecting a complex microenvironment with varying bioburden diversity across outcomes. Genus-level Pseudomonas detection was associated with wound failure at all surgeries. A logistic regression model was fit to the presence and absence of antimicrobial resistance classes to assess associations with nosocomial pathogens. A. baumannii detection was associated with detection of genomic signatures for resistance to trimethoprim, aminoglycosides, bacitracin, and polymyxin. Machine learning classifiers were applied to identify wound and microbial variables associated with outcome. Feature importance rankings averaged across models indicated the variables with the largest effects on predicting wound outcome, including an increase in P. putida sequence reads. These results describe the microbial genomic determinants in combat wound bioburden and demonstrate metagenomic investigation as a comprehensive tool for providing information toward aiding treatment of combat-related injuries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Methods to capture proteomic and metabolomic signatures from cerebrospinal fluid and serum of healthy individuals

Discovery of reliable signatures for the empirical diagnosis of neurological diseases—both infectious and non-infectious—remains unrealized. One of the primary challenges encountered in such studies is the lack of a comprehensive database representative of a signature background that exists in healthy individuals, and against which an aberrant event can be assessed. For neurological insults and injuries, it is important to understand the normal profile in the neuronal (cerebrospinal fluid) and systemic fluids (e.g., blood). Here, we present the first comparative multi-omic human database of signatures derived from a population of 30 individuals (15 males, 15 females, 23–74 years) of serum and cerebrospinal fluid. In addition to empirical signatures, we also assigned common pathways between serum and CSF. Together, our findings provide a cohort against which aberrant signature profiles in individuals with neurological injuries/disease can be assessed—providing a pathway for comprehensive diagnostics and therapeutics discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Liquid lithium as divertor material to mitigate severe damage of nearby components during plasma transients

The successful operation of thermonuclear fusion reactors such as ITER, DEMO, and future commercial plants is mainly determined by the optimum choice of materials for various components. The objective of this work is to accurately and comprehensively simulate the entire device in 3D to predict pros and cons of various materials, e.g., liquid lithium in comparison to tungsten and carbon to predict future ITER-like and DEMO divertor performances. We used our comprehensive HEIGHTS simulation package to investigate ITER-like components response during transient events in exact 3D geometry. Starting from the lost hot core plasma particles through SOL, deposition on the divertor surface, and the generation of secondary plasma of divertor materials. Our simulations predicted significant reduction in the heat loading and damage to the divertor nearby and internal components in the case when lithium is used on the divertor plates. While if tungsten or carbon are used on the divertor plate, significant melting areas and vaporization spots can occur (less for carbon) on the reflector, dome, and stainless steel tubes, and even parts of the first walls can melt due to the high radiation power of the secondary divertor plasma. Lithium photon radiation deposition into the divertor and nearby surfaces was decreased by two orders of magnitude compared to tungsten and by one order of magnitude compared to carbon. This analysis showed that using liquid lithium for ITER-like surfaces and future DEMO can lead to significant enhancement in components lifetime.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Energy-resolved neutron imaging and diffraction including grain orientation mapping using event camera technology

Time-of-flight neutron diffraction and energy-resolved imaging each provide unique perspectives into material properties. Neutron diffraction is useful for assessing microstructural parameters such as phase composition, texture, and dislocation densities, though it typically provides averaged data over the sampled volume. Energy-resolved imaging, on the other hand, offers both spatial and spectral information by detecting Bragg edges and neutron absorption resonances, which enables detailed mapping of microstructure and isotopic composition. When combined, these techniques have the potential to enrich our understanding of material behavior across different scales, enhancing our understanding of complex materials. Traditionally, these modalities are conducted on separate instruments, which is time-consuming and poses challenges for data integration. Here, we report the integration of the LumaCam, an event-mode energy-resolved neutron imaging camera with the HIPPO time-of-flight diffractometer at LANSCE. This integration enables simultaneous diffraction and imaging across the full spectrum, with analysis optimized for diffraction and Bragg-edge imaging in the thermal range (0.45–10 Å) and resonance imaging in the epithermal range (0.5–3000 eV), facilitating comprehensive multi-modal analysis. We demonstrate its capabilities through case studies, including spatial mapping of grain orientations in a steel sample and accurate thickness estimations for irregular samples including a depleted uranium cylinder and a natural silver-containing mineral specimen. The combined setup enhances real-time sample alignment and provides comprehensive data for crystal structure, texture, and isotopic composition analysis. This approach opens new possibilities for advanced applications in nuclear engineering, archaeology, and materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Functional metal–organic frameworks as effective sensors of gases and volatile compounds

Developing efficient sensor materials with superior performance for selective, fast and sensitive detection of gases and volatile organic compounds (VOCs) is essential for human health and environmental protection, through monitoring indoor and outdoor air pollutions, managing industrial processes, controlling food quality and assisting early diagnosis of diseases. Metal–organic frameworks (MOFs) are a unique type of crystalline and porous solid material constructed from metal nodes (metal ions or clusters) and functional organic ligands. They have been investigated extensively for possible use as high performance sensors for the detection of many different gases and VOCs in recent years, due to their large surface area, tunable pore size, functionalizable sites and intriguing properties, such as electrical conductivity, magnetism, ferroelectricity, luminescence and chromism. The high porosity of MOFs allows them to interact strongly with various analytes, including gases and VOCs, thus resulting in easily measurable responses to different physicochemical parameters. Although much of the recent work on MOF-based luminescent sensors have been summarized in several excellent reviews (up to 2018), a comprehensive overview of these materials for sensing gases and VOCs based on chemiresistive, magnetic, ferroelectric, and colorimertic mechanisms is missing. In this review, we highlight the most recent progress in developing MOF sensing and switching materials with an emphasis on sensing mechanisms based on electricity, magnetism, ferroelectricity and chromism. We provide a comprehensive analysis on the MOF–analyte interactions in these processes, which play a key role in the sensing performance of the MOF-based sensors and switches. We discuss in detail possible applications of MOF-based sensing and switching materials in detecting oxygen, water vapor, toxic industrial gases (such as hydrogen sulfide, ammonia, sulfur dioxide, nitrous oxide, carbon oxides and carbon disulfide) and VOCs (such as aromatic and aliphatic hydrocarbons, ketones, alcohols, aldehydes, chlorinated hydrocarbons and N,N'-dimethylformamide). Altogether, this review serves as a timely source of information and provides insight for the future development of advanced MOF materials as next-generation gas and VOC sensors.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Atomically dispersed metal–nitrogen–carbon catalysts for fuel cells: advances in catalyst design, electrode performance, and durability improvement

The urgent need to address the high-cost issue of proton-exchange membrane fuel cell (PEMFC) technologies, particularly for transportation applications, drives the development of simultaneously highly active and durable platinum group metal-free (PGM-free) catalysts and electrodes. The past decade has witnessed remarkable progress in exploring PGM-free cathode catalysts for the oxygen reduction reaction (ORR) to overcome sluggish kinetics and catalyst instability in acids. Among others, scientists have identified the newly emerging atomically dispersed transition metal (M: Fe, Co, or/and Mn) and nitrogen co-doped carbon (M–N–C) catalysts as the most promising alternative to PGM catalysts. Here, we provide a comprehensive review of significant breakthroughs, remaining challenges, and perspectives regarding the M–N–C catalysts in terms of catalyst activity, stability, and membrane electrode assembly (MEA) performance. A variety of novel synthetic strategies demonstrated effectiveness in improving intrinsic activity, increasing active site density, and attaining optimal porous structures of catalysts. Rationally designing and engineering the coordination environment of single metal MN x sites and their local structures are crucial for enhancing intrinsic activity. Increasing the site density relies on the innovative strategies of restricting the migration and agglomeration of single metal sites into metallic clusters. Relevant understandings provide the correlations among the nature of active sites, nanostructures, and catalytic activity of M–N–C catalysts at the atomic scale through a combination of experimentation and theory. Current knowledge of the transferring catalytic properties of M–N–C catalysts to MEA performance is limited. Rationally designing morphologic features of M–N–C catalysts play a vital role in boosting electrode performance through exposing more accessible active sites, realizing uniform ionomer distribution, and facilitating mass/proton transports. We outline future research directions concerning the comprehensive evaluation of M–N–C catalysts in MEAs. The most considerable challenge of current M–N–C catalysts is the unsatisfied stability and rapid performance degradation in MEAs. Therefore, we further discuss practical methods and strategies to mitigate catalyst and electrode degradation, which is fundamentally essential to make M–N–C catalysts viable in PEMFC technologies.

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

Plasmonic nanorod probes’ journey inside plant cells for in vivo SERS sensing and multimodal imaging

Nanoparticle-based platforms are gaining strong interest in plant biology and bioenergy research to monitor and control biological processes in whole plants. However, in vivo monitoring of biomolecules using nanoparticles inside plant cells remains challenging due to the impenetrability of the plant cell wall to nanoparticles beyond the exclusion limits (5-20 nm). To overcome this physical barrier, we have designed unique bimetallic silver-coated gold nanorods (AuNR@Ag) capable of entering plant cells, while conserving key plasmonic properties in the near-infrared (NIR). To demonstrate cellular internalization and tracking of the nanorods inside plant tissue, we used a comprehensive multimodal imaging approach that included transmission electron microscopy (TEM), confocal fluorescence microscopy, two-photon luminescence (TPL), X-ray fluorescence microscopy (XRF), and photoacoustics imaging (PAI). We successfully acquired SERS signals of nanorods in vivo inside plant cells of tobacco leaves. On the same leaf samples, we applied orthogonal imaging methods, TPL and PAI techniques for in vivo imaging of the nanorods. This study first demonstrates the intracellular internalization of AuNR@Ag inside whole plant systems for in vivo SERS analysis in tobacco cells. This work demonstrates the potential of this nanoplatform as a new nanotool for intracellular in vivo biosensing for plant biology.

Cupil-Garcia, Vanessa↗
DOE OSTI2022

Experimental-based mechanistic study and optimization of hydrothermal liquefaction of anaerobic digestates

Valorization of agricultural and food waste digestates is crucial for sustainable waste management to reduce environmental impacts and improve the economics of commercial farms. Hydrothermal liquefaction (HTL) of anaerobic digestates was evaluated to recover resources by converting them into carbon-dense biocrude oil and a nutrient-rich HTL aqueous phase (HTL-AP) coproduct. The effects of HTL temperature (280–360 °C), reaction time (10–50 min), feedstock pH (2.5–8.5), digestate salt content (1–5 wt%), and digestate cellulose-to-lignin ratio (0.2–1.8) on energy and nutrient recovery were systematically investigated in a set of well-designed experiments following a half-fractional central composite protocol. Response surface analysis combined with HTL product characterization and comparative literature study produced a comprehensive reaction pathway for HTL of anaerobic digestates. Moreover, this analysis revealed the importance of acidic feedstocks (pH 3.00–5.53), high reaction temperatures (337–360 °C), and reaction times <45 or 45–50 min for digestates with Cel/Lig >1 or <1, for maximizing the energy recovered in biocrude (high carbon yield and low heteroatom content) and the amounts of P, NH 3 –N, and Mg distributed in the HTL-AP. Acidic conditions catalyzed biocrude production, inhibited the Maillard reaction (lowering the nitrogen content in biocrude), and partitioned nutrients into the HTL-AP. Higher reaction temperatures coupled with longer reaction times activated hydro-denitrogenation and deoxygenation reactions to improve biocrude quality. Finally, this work provides not only validated methods to achieve targeted resource recovery for specific feedstock compositions using HTL, but also a comprehensive mechanistic understanding of the HTL of biomass waste for controlling target product characteristics.

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

Synergistic effect of polyaniline on stabilizing Pt nanoparticles in PEMFCs†

The stability of Pt nanoparticles on a carbon support is crucial for the lifespan of polymer electrolyte membrane fuel cells as well as other electrocatalysis systems such as electrolyzers. Here we took the approach of utilizing the covalently grafted polyaniline on carbon to stabilize Pt nanoparticles by simultaneously alleviating the particle migration and mitigating the Ostwald ripening, which are two major mechanisms for loss of the catalyst stability. A comprehensive investigation of the stability and performance of Pt catalyst nanoparticles on these supports reveals that the polyaniline can shift the Pt L edge binding energy and the Pt 4f peak energy, leading to the increased redox potential of the Pt nanoparticles and making them more resistant to oxidation. Interestingly, the highest polyaniline density (50 wt%) does not show the best catalyst stability, rather, the second (33 wt%) demonstrates the best catalyst stability among these catalysts. Combining the XPS, XAS and fuel cell stability studies, we concluded that the locations of the Pt nanoparticles over the polyaniline layer on the carbon surface depend on the surface density of the polyaniline over the carbon surface. The Pt nanoparticles in the 50 wt% polyaniline catalyst are wrapped by the polyaniline polymer but do not sit directly on the carbon surface, while Pt nanoparticles in the 33 wt% polyaniline catalyst sit on the carbon surface and are densely surrounded by the polyaniline polymer. The results show that the mass activity loss is 12.5% while the ECSA (electrochemically active surface area) loss is 37.1% after 30k cycles of accelerating stress test for the 33 wt% polyaniline catalyst, far exceeding the DOE 2020 target for MEA (i.e., mass activity loss < 50%, and ECSA loss < 40%). Overall, the polyaniline has a synergistic effect on mitigating the surface migration and slowing down the Ostwald ripening to stabilize the Pt nanoparticles in fuel cells. The novel strategy to stabilize Pt nanoparticles using polyaniline opens a new pathway in the development of highly stable catalysts: relying on the functionalization group on the carbon support, instead of just focusing on the catalyst itself.

Li, Chenzhao↗
DOE OSTI2023

The nature and suppression strategies of interfacial reactions in all-solid-state batteries

Solid-state Li batteries are promising energy storage devices owing to their high safety and high theoretical energy density. However, the serious interfacial reaction between solid state electrolytes and cathodes deteriorates the battery performance, impeding the realization of long-term cyclability. The buried nature of the interphase layer presents a significant challenge in achieving a comprehensive understanding of the underlying interfacial reaction mechanisms. Herein, we systematically explore the interfacial reaction evolutions and interphase compositions and electronic properties between the popular oxide cathodes and sulfide solid electrolytes (SSEs). This includes analysis of the chemical and electrochemical reactions between cathodes/coatings and SSEs, as well as the electrochemical self-decomposition of SSEs by thermodynamic phase equilibrium analysis. Herein, we disclose that the driving force of the electrochemical reaction at the chemical potential of μ Li is much stronger than that of the chemical reaction, which dominates the interfacial reaction. Preventing the formation of an electronically conductive interphase is crucial in inhibiting the continuous interfacial degradation during long-term cycling, which can be achieved through the optimized combination of cathodes and SSEs, as well as the introduction of functional coatings between them. Based on these findings, the percentage of molar fraction ( f ) of electronically conductive species in the formed interphase is proposed as a key factor for indicating the interfacial stability for the first time. Furthermore, we propose a specific high-throughput screening scheme to filter the functional coating materials by comprehensively evaluating their functionality. The tiered screening identifies 48 coating materials with optimal properties. The work highlights the significant roles of rational coupling of the cathodes and SSEs, and optimizing interfacial coating materials for solid-state batteries. It opens new avenues for engineering an interphase with improved interfacial compatibility to realize long-term cyclability.

25 ENERGY STORAGE↗
DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Mechanistic insight into the effects of A-site doping in perovskite nanorods on interfacial Li-ion and Na-ion transport in polymer–perovskite composite electrolytes

Composite polymer electrolytes (CPEs) offer a promising pathway to safer and higher energy density electrochemical energy storage yet optimizing interfacial ion transport remains a critical challenge due to high resistances and a lack of understanding of the complex interfacial electrostatic interactions. This work investigates the mechanistic origins of the interfacial ion transport enhancement in single-ion conducting (SIC) polymer electrolytes filled with A-site disordered perovskite LaxMyTiOz (LMTO) (M = Li, Na, K) nanorods. By integrating Density Functional Theory (DFT) with broadband dielectric spectroscopy (BDS) and pulsed-field-gradient NMR (PFG-NMR), we analyze the ion transport behavior in Li-based and Na-based SIC systems containing compositionally distinct LMTO fillers (LMTO800 and LMTO900). Specifically, we introduce the site-to-site energy difference (ΔE) descriptor, which extends beyond single-site descriptors such as adsorption energy and ion binding energies. We demonstrate that ΔE together with ion binding energies provide a more comprehensive picture of the energy landscape at the polymer–ceramic interface. The combined DFT and experimental results show that A-site doping in the ceramic filler can effectively modulate ΔE and ion binding energies, creating a favorable potential energy landscape that promotes enhanced ion transport along the interface. This comprehensive study illustrates the impact of ceramic compositional tuning on interfacial ion transport and provides materials design rules towards real-world applications.

B shepard, Lauren [Pennsylvania State University]↗
DOE OSTI2024

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

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

Sun, Shining↗
DOE OSTI2023

Condition monitoring of permanent magnet AC machines for all-electric transportation systems: State of the art

The current state of the art on emerging and efficient techniques for condition monitoring of permanent magnet (PM) alternating-current (AC) machines deployed in electric vehicle (EV) applications is presented. The discussion includes the most common and specific types of faults in PM motors, such as rotor demagnetisation and stator inter-turn faults, respectively. Fault indicators, such as voltage (v s ) and current (i s ) signals and machine signatures based on motor back electromotive force (EMF) (E B ) and magnetic flux (φ), are taken into account as a measuring quantity in diagnosing motor faults. Other signatures, including thermal analysis, acoustic noise, and vibrations, are also illustrated as some of the emerging techniques in estimating the performance of EV motors while under operations. In addition, various fault modelling methods, condition monitoring techniques, and comprehensive approaches applied in diagnosing the effect of machine faults during its incipient stages are illustrated. Since most of the fault diagnostic techniques discussed here include only machine-based quantities as fault indices/indicators, the provided solutions are therefore found to be more reliable and accurate for diagnosing the motor faults. This comprehensive review study is inclusive of the existing fault diagnostic techniques, which are currently employed in industrial and commercial practices, in addition to the new methodologies proposed by the authors. All the given condition monitoring schemes therefore seem significantly vital in estimating the state of health of PM AC machines while under operation in all-electric transportation systems.

33 ADVANCED PROPULSION SYSTEMS↗