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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 OSTI2024

Comprehensive Study of Carrier Recombination in High‐Efficiency CdTe Solar Cells Using Transient Photovoltage

Cadmium telluride (CdTe) solar cells represent a commercially successful photovoltaic technology, with an annual production capacity approaching 20 GW. However, improving the open-circuit voltage (V OC ) remains challenging. This study aims to deepen the understanding of charge carrier recombination in CdTe solar cells and to explore alternative dynamical characterization methods that address the limitations found in conventionally used time-resolved photoluminescence for CdTe solar cells. Transient photovoltage and transient photocurrent techniques are utilized to investigate charge carrier dynamics under conditions resembling real-world solar cell operation. The results reveal that an effective nonradiative recombination lifetime of 580 ns dominates the charge dynamics at VOC values below 850 mV. Above this threshold, radiative recombination becomes significant, with a radiative recombination coefficient of 1.1 × 10 −9 cm 3 s −1 . Additionally, the stationary charge carrier density at 1 sun is determined to be around 1 × 10 14 cm −3 . By accurately determining both radiative and nonradiative recombination, this work provides a comprehensive understanding of carrier dynamics in high-performing CdTe devices and paves the way for improving the V OC and performance of CdTe solar cells.

CdTe solar cells↗
DOE OSTI2020

A Comprehensive Scientific Survey of Excipients Used in Currently Marketed, Therapeutic Biological Drug Products

The steady development of biotechnology-derived therapeutic biologics over the last few decades has generated drugs that are now standard medical treatments for a range of indications. While the development of protein products has surged in recent years, the formulation and delivery of these complex molecules have relied on drug-specific studies and, in some instances, data from non-proteinaceous drug products. The commonalities, trends, and gaps in excipient technologies used to support the development of therapeutic proteins largely remain unexplored due to the drug-specific nature of many formulations. Using a comprehensive and relational database approach, we aimed to provide a scientific survey of all approved or licensed biotechnology-derived drug products with the goal of providing evidence-based information on common attributes and trending features in protein product excipients. We examined 665 formulations, and 395 unique formulations based on having unique excipients within them, that supported 211 therapeutic proteins as of June 2020. We report the prevalence of each excipient class and excipient chemical used in eight different drug types including monoclonal antibodies, antibody conjugates, cytokines and growth factors, enzymes, polypeptide hormones, pulmonary surfactants, recombinant fusion proteins, and toxins. We also report the prevalence by excipient type among all therapeutic proteins, in the context of each drug’s recommended pH range, concentration ranges for excipients, and route of administration. The results of our analyses indicate certain excipients common to monoclonal antibodies, cytokines, and polypeptide hormones. We also report on excipients unique to protein drug products, such as amino acids, solubilizers, and lyoprotectants. Overall, our report summarizes the current landscape of excipients used in marketed biotechnology-derived therapeutic biologic products.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Exploring urban typologies using comprehensive analysis of transportation dynamics

Abstract As urban areas continue to expand and develop, categorizing cities into typologies offers a valuable framework for understanding metropolitan dynamics and fostering inter-city collaboration. However, existing typologies related to urban mobility have limitations, failing to consider cities within a single large urban region and often overlooking crucial dimensions such as trip demand and traffic flow. In this paper, we introduce a transportation-focused characterization for cities within a large urban region, specifically the San Francisco Bay Area, California. We incorporate over 40 metrics across five transportation dimensions: trip demand, road network, multi-modal network, traffic flow, and land use. Specifically, for the trip demand dimension, we include metrics capturing residents’ trip characteristics, such as mode share, intra-city trips, and inter-city trips. Additionally, we analyze the purpose of trips entering the city to gain a deeper understanding of incoming trip patterns. In the traffic flow dimension, we examine metrics like vehicle miles traveled, delay, and congestion to assess the traffic conditions on the street network. These, combined with other dimensions, provide a comprehensive view of a city’s transportation dynamics. Using unsupervised machine learning clustering methods, we identified eight distinct typologies for the Bay Area: Live Work Cities; Job and Activity Magnet Cities; Anchor Cities; Multi-modal Cities; Hyper-connected Cities; Low-density Residential Cities; Medium-density Residential Cities; and Mixed-use Residential Cities. Our findings show that many clusters are strongly influenced by trip demand and traffic flow metrics. Finally, we examine the practicality of this typology and its potential to guide collaborative transportation management strategies. The typologies provide a foundation for dialogue among Bay Area cities, focusing on evaluating shared characteristics and leveraging successes or challenges to develop unified strategies for transportation management.

Kuncheria, Anu↗
DOE OSTI2026

Thermokinetic mixing compounding for polymer composites – a comprehensive review

High-speed thermokinetic mixers (K-mixers) represent an advanced compounding technology that employs intense shear and friction to convert kinetic energy directly into thermal energy. This mechanism enables rapid mixing cycles, often under one minute, facilitating exceptional filler dispersion while minimizing the material’s thermal history. This is particularly effective for compounding challenging materials, including heat-sensitive biopolymers, wet filler feedstocks, and nanofillers prone to agglomeration. As the first comprehensive review of this technology, this article synthesizes the fundamental principles of thermokinetic mixing (K-mixing) and surveys recent advances in polymer composite fabrication. We contrast the working principles of K-mixers with conventional twin-screw extrusion, highlighting distinct advantages in dispersing nanoscale fillers, exfoliating layered materials, and processing wet cellulosic feedstocks and ultra-high filler loadings (e.g., 85 wt%). Furthermore, strategies to optimize filler–matrix interfacial bonding under rapid-processing constraints, such as the kinetic selection of compatibilizers and fiber surface treatments, are evaluated. Finally, we analyze key structure-processing-property relationships and outline future directions in scaling up, reactive processing, and hybrid material development.

Zhang, Xuefeng [University of Maine]↗
DOE OSTI2025

Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review

Artificial intelligence is emerging as a transformative force in addressing the multifaceted challenges of food safety, food quality, and food security. This review synthesizes advancements in AI-driven technologies, such as machine learning, deep learning, natural language processing, and computer vision, and their applications across the food supply chain, based on a comprehensive analysis of literature published from 1990 to 2024. AI enhances food safety through real-time contamination detection, predictive risk modeling, and compliance monitoring, reducing public health risks. It improves food quality by automating defect detection, optimizing shelf-life predictions, and ensuring consistency in taste, texture, and appearance. Furthermore, AI addresses food security by enabling resource-efficient agriculture, yield forecasting, and supply chain optimization to ensure the availability and accessibility of nutritious food resources. This review also highlights the integration of AI with advanced food processing techniques such as high-pressure processing, ultraviolet treatment, pulsed electric fields, cold plasma, and irradiation, which ensure microbial safety, extend shelf life, and enhance product quality. Additionally, the integration of AI with emerging technologies such as the Internet of Things, blockchain, and AI-powered sensors enables proactive risk management, predictive analytics, and automated quality control. By examining these innovations' potential to enhance transparency, efficiency, and decision-making within food systems, this review identifies current research gaps and proposes strategies to address barriers such as data limitations, model generalizability, and ethical concerns. These insights underscore the critical role of AI in advancing safer, higher-quality, and more secure food systems, guiding future research and fostering sustainable food systems that benefit public health and consumer trust.

AI↗
DOE OSTI2020

A comprehensive diffusion mobility database comprising 23 elements for magnesium alloys

We report that reliable experimental diffusion coefficients of 10 key alloying elements in Mg obtained by the present authors together with experimental data in the literature enabled us to perform a systematic test of the reliability of diffusion coefficients obtained from DFT calculations. The computed activation energy values were found to be quite accurate (mostly within 0.2 eV) but the computed pre-factors were less reliable. Such insights allowed us to develop a practical and yet robust strategy to perform diffusion mobility assessments by adopting the computed activation energy while fitting only the pre-factor when available experimental data are limited to a narrow temperature range. The overall good agreement between the DFT data and experimental data also gave us the confidence to employ the computed data for those that were still missing or inaccessible from experimental measurements. A systematic assessment of both the measured and computed diffusion data in hcp Mg was performed using the above holistic approach to yield the most comprehensive open Mg mobility database to date, comprising 23 elements (Mg, Ag, Al, Be, Ca, Cd, Ce, Cu, Fe, Ga, Gd, In, La, Li, Mn, Nd, Ni, Pu, Sb, Sn, U, Y, Zn). This more reliable mobility database will contribute to future development of advanced Mg alloys. The holistic approach developed in this study will be very beneficial to the future establishment of reliable mobility databases for other alloy systems as well.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hempseed cell wall polysaccharides are dominated by linear xylans and cellulose: Comprehensive structural profiling of ten cultivars of industrial hemp, Cannabis sativa L .

Hempseed is a rich source of dietary fiber; however, there has been limited research on the variability of carbohydrate composition in hempseed cell walls. The primary aim of this study was to conduct a comprehensive chemical and structural analysis of the cell wall polysaccharides in ten hempseed cultivars. Water-soluble polysaccharides (WSP) and water-insoluble residues (WIR) were isolated and subsequently analyzed for their monosaccharide composition using HPAEC-PAD, glycosyl linkage analysis using GC–MS, and structural characterization via NMR spectroscopy. All hempseed cultivars contained a high proportion of insoluble fibers and smaller amounts of soluble polysaccharides. Glucose and xylose were the most abundant components of the WIR fractions, while the WSP fractions contained abundant amounts of galactose, galacturonic acid, arabinose, rhamnose, and mannose. The results of linkage and spectroscopic analysis were consistent with the compositional analysis, identifying cellulose and acetylated linear xylans as primary components of WIR, and arabinogalactans, rhamnogalacturonans, heteromannans, xyloglucans, and arabinan as predominant in WSP. Altogether, the study revealed a comparable cell wall structure among the analyzed hemp seed varieties. The high fiber content of whole hempseed-based ingredients presents significant potential for food manufacturers seeking to develop products with enhanced dietary fiber content, offering both functional and nutritional benefits for consumers.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Initial hydration process of calcium silicates in Portland cement: A comprehensive comparison from molecular dynamics simulations

As the main components of Portland cement, calcium silicates show substantial differences in their hydration reactivity which have not been fully explained. A comprehensive comparison of the initial hydration process of calcium silicates, namely dicalcium silicate (C{sub 2}S) and tricalcium silicate (C{sub 3}S), was conducted using molecular dynamics simulations. The initial hydration process was divided into three stages using cut-off times of 0.001 ns and 3 ns. The hydration of M{sub 3}-C{sub 3}S (010) was more evident than that of β-C{sub 2}S (100), supported by the hydroxylation degree, radial distribution function, atomic density profile, etc. The coordination number of the surface Ca atoms might be the underlying reason for such a difference. Interactions between cement surfaces and water molecules were mainly characterised by solid OH bonding and Ca-water O bonding. Dissolution of Ca atoms was observed, although quite scarce, while no dissolution of Si atoms was observed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Physics-informed neural networks (PINNs) have shown to be effective tools for solving both forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of the neural network using automatic differentiation, and this PDE loss is evaluated at a set of scattered spatio-temporal points (called residual points). The location and distribution of these residual points are highly important to the performance of PINNs. However, in the existing studies on PINNs, only a few simple residual point sampling methods have mainly been used. Here, we present a comprehensive study of two categories of sampling for PINNs: non-adaptive uniform sampling and adaptive nonuniform sampling. We consider six uniform sampling methods, including (1) equispaced uniform grid, (2) uniformly random sampling, (3) Latin hypercube sampling, (4) Halton sequence, (5) Hammersley sequence, and (6) Sobol sequence. We also consider a resampling strategy for uniform sampling. To improve the sampling efficiency and the accuracy of PINNs, we propose two new residual-based adaptive sampling methods: residual-based adaptive distribution (RAD) and residual-based adaptive refinement with distribution (RAR-D), which dynamically improve the distribution of residual points based on the PDE residuals during training. Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling. We extensively tested the performance of these sampling methods for four forward problems and two inverse problems in many setups. Our numerical results presented in this study are summarized from more than 6000 simulations of PINNs. Here, we show that the proposed adaptive sampling methods of RAD and RAR-D significantly improve the accuracy of PINNs with fewer residual points for both forward and inverse problems. Furthermore, the results obtained in this study can also be used as a practical guideline in choosing sampling methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A comprehensive experimental and kinetic modeling study of di-isobutylene isomers: Part 1

We report di-isobutylene has received significant attention as a promising fuel blendstock, as it can be synthesized via biological routes and is a short-listed molecule from the Co-Optima initiative. Di-isobutylene is also popularly used as an alkene representative in multi-component surrogate models for engine studies of gasoline fuels. However, there is limited experimental data available in the literature for neat di-isobutylene under engine-like conditions. Hence, most existing di-isobutylene models have not been extensively validated, particularly at lower temperatures (< 1000 K). Most gasoline surrogate models include the di-isobutylene sub-mechanism published by Metcalfe et al. with little or no modification. The current study is undertaken to develop a detailed kinetic model for di-isobutylene and validate the model using a wide range of relevant experimental data. Part 1 of this study exclusively focuses on the low- to intermediate temperature kinetics of di-isobutylene. An upcoming part 2 discusses the high-temperature model development and validation of the relevant experimental targets. Ignition delay time measurements for the di-isobutylene isomers were performed at pressures ranging from 15 - 30 bar at equivalence ratios of 0.5, 1.0, and 2.0 diluted in air and in the temperature range 650 - 900 K using two independent rapid compression machine facilities. In addition, measurements of species identified during the oxidation of these isomers were performed in a jet-stirred reactor and in a rapid compression machine. A detailed kinetic model for the di-isobutylene isomers is developed to capture the wide range of new experimental targets. For the first time, a comprehensive low-temperature chemistry submodel is included. The differences in the important reaction pathways for the accurate prediction of the oxidation of the two DIB isomers are compared using reaction path analysis. In conclusion, the most sensitive reactions controlling the ignition delay times of the DIB isomers under the pressure and temperature conditions necessary for autoignition in engines are identified.

09 BIOMASS FUELS↗
DOE OSTI2022

Comprehensive insights into competitive oxidation/sulfidation reactions on binary ferritic alloys at high temperatures

Interpreting high-temperature corrosion induced by mixed-gas atmospheres is challenging due to the different contributions of oxidizing gases. In this work, a comprehensive study on the combined oxidation/sulfidation using label molecules is presented. Fe-Cr model alloys with 2 wt% and 9 wt% Cr were isothermally exposed using a volumetric mixture of 0.5%S 16 O 2 /27%H 2 18 O and 0.5%S 16 O 2 /7%H 2 18 O at 650 °C for 5 h and then characterized by secondary ion mass spectroscopy (SIMS). Additionally, the reactions were followed in-situ utilizing energy dispersive X-ray diffraction. The study showed that both S 16 O 2 and H 2 18 O contribute to the oxidation of the alloys but to different extents depending on the Cr-content.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Comprehensive powertrain modeling for heavy-duty applications: A study of plug-in hybrid electric bus

A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed to achieve real-time optimization of plug-in hybrid electric buses while considering the real vehicle drivability and the practical operation of all powertrain components under real driving conditions. The control strategy is based on a supervisory control algorithm that alternately employs charge-dominant control and discharge-dominant control to manage multiple powertrain sources, and enable an optimal overall efficiency-based powertrain operation state by maximizing the inherent optimal powertrain efficiency through best all transmission gear choice and the blend of motor and/or engine power. In the model, a component energy efficiency database was developed to rapidly enable an smart and optimal operating state determined from a set of efficiency maps characterizing the component and powertrain control states as a function of the instantaneous vehicle operational condition. The results revealed that the energy savings achieved with the innovative powertrain control improved by 10%–30% compared with the baseline hybrid powertrain control strategy. The benefits of eco-driving with respect to energy consumption were also evaluated using the powertrain model. The powertrain control model was implemented into a real hybrid bus. Measured energy savings with the optimized control strategy were similar to the simulated results.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

The short-term comprehensive impact of the phase-out of global coal combustion on air pollution and climate change

With the continuous intensification of global warming, the reduction and ultimate phase-out of coal combustion is an inevitable trend in the future global energy transformation. This study comprehensively analyzed the impact of phasing out coal combustion on global emissions and concentrations of air pollutants, radiative fluxes, meteorology and climate using Community Earth System Model 2 (CESM2). The results indicate that after the global phase-out of coal combustion, there is a marked decrease in the concentrations of sulfur dioxide (SO 2 ), nitrogen oxides (NO x ) and fine particulate matter (PM 2.5 ), with some regions experiencing a reduction of exceeding 50%. There is no significant change in global ozone (O 3 ) concentration. There are decreasing AOD and positive radiative fluxes globally in the short term, though the cloud contributes minor negative radiative fluxes. The global air temperature may increase by approximately (0.02 ± 0.15) °C on average with regional and seasonal variations, and the precipitation may potentially increase by approximately (2.7 ± 40.6) mm yr −1 globally and over 20% in equatorial regions in the short term. But combined with the decreasing trend of cloud water content in the Northern Hemisphere, it indicates a potential increase in the extremity of precipitation events. In conclusion, this study provides references for global control of air pollution, mitigation strategies of climate change, and transformation of energy structures under the objective of “carbon neutrality”, such as focusing on the negative climate impacts of exacerbating regional warming and increasing extreme precipitation resulting from the rapid reduction of aerosols in the short term.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Comprehensive evaluation of commercially scalable atomic-layer-deposited alumina coating impact on full cell battery performance across varied test conditions

Atomic Layer Deposition (ALD) has emerged as a strategic enhancement method for lithium-ion battery (LIB) materials offering potential benefits and durability benefits for industrial battery production. However, the translation from laboratory achievements to commercial-scale applications has been limited. Here, this study aims to bridge this gap by comprehensively evaluating the effects of commercially scalable Al 2 O 3 ALD coatings using full pouch cell performance as a means to assess the ALD impact. We utilized large-scale slot-die coating techniques to ensure consistent electrode quality and tested four configurations of pouch cells to analyze the individual effects of ALD coating on anode and cathode electroactive materials. Our extensive testing matrix included long-term cycling, fast discharge, fast charge, leakage current, and high voltage tests. While at lower C-rates (<~1C), the influence of Al 2 O 3 coatings on cell performance is not significant. Fast charging conditions reveal that the anode ALD coating significantly enhances performance via a passivating effect, while on the cathode, it is detrimental, potentially due to increased resistance of the thin interfacial layer formed during the ALD processing. Leakage current and high-voltage tests show that the application of ALD coatings on either anode or cathode effectively minimizes side reactions at the electrode-electrolyte interface. Additionally, ALD coatings significantly mitigate concentrated and localized lithium plating on the anodes. These insights provide a valuable understanding of the potential of ALD technologies in LIB manufacturing to tailor cell performance, paving the way for safer, more efficient, and cost-effective battery solutions.

25 ENERGY STORAGE↗
DOE OSTI2024

A comprehensive numerical investigation on spray models for Direct-Injection Spark-Ignition engines

Gasoline direct-injection spark-ignition (DISI) engines generate a large portion of their unburned hydrocarbon (UHC) and soot emissions during the cold-start phase. A predictive computational fluid dynamics (CFD) modeling framework can be used to understand the physical processes that characterize fuel spray evolution and fuel-film formation at cold start conditions, which can help to reduce engine-out particulate emissions. This study systematically evaluated spray submodels and developed a set of simulation best practices for physical-numerical submodels with the goal of enabling accurate simulations of liquid spray behavior in a DISI engine. Three comprehensive experimental datasets containing free-spray projected liquid volume (PLV), liquid volume fraction (LVF), and near-field X-ray radiography data were used to validate the simulation results and evaluate the spray submodels. Systematic analysis delved into injected parcel distribution, droplet collision, spray breakup, and evaporation via a detailed assessment of the relevant spray submodels. Moreover, the effects of turbulence models and the initial turbulent flow properties on the liquid spray evolution were examined. Based on extensive calibration efforts, a set of simulation best practices for the free spray was developed and validated against the PLV/LVF data. Simulation results indicated that the uniform distribution for parcel initialization, coupled with appropriate droplet collision submodels, provides an improved spray morphology compared to the cluster distribution. The findings also underscored the importance of calibrating the Kelvin-Helmholtz Rayleigh-Taylor (KH-RT) breakup model constants and droplet heat transfer coefficient scaling factor to achieve favorable agreement regarding measured liquid penetration and spray widths. In conclusion, this study marks a substantial stride towards accurately predicting fuel film evolution and soot formation within DISI engine performance.

ECN Spray G↗
DOE OSTI2021

A control oriented comprehensive degradation model for battery energy storage system life prediction

In order to ensure that stationary battery energy storage systems (BESSs) provide reliable energy buffering, both for power quality and economic purposes, the degradation must be considered. Cell degradation involves various side reactions and is highly dependent on its operating conditions. To accurately track cell degradation and predict its impact on battery behavior, a comprehensive physics-based degradation model based on an electrolyte phase-enhanced single particle (SP) model is developed. Key degradation physics, namely solid electrolyte interphase (SEI) layer formation and growth, Li plating on the graphite anode, and Mn dissolution on cathode of nickel-cobalt-manganese oxide (NMC622) are considered. The model is validated against experimental capacity data. The results reveal that the deposition rate of both SEI layer and Li metal increase as the charge voltage increases. At the cathode side, the solvent oxidation rate determines the Mn dissolution rate. As a result, the volume fraction of NMC622 in the cathode continually decreases at a gradually increasing rate.

25 ENERGY STORAGE↗
DOE OSTI2021

A comprehensive modeling method for proton exchange membrane electrolyzer development

Hydrogen attracts significant interests as an effective energy carrier that can be derived from renewable sources. Hydrogen production using a proton-exchange membrane (PEM) electrolyzer can efficiently convert renewable power via water splitting in wide scales—from large, centralized generation to on-site production. Mathematical models with multiple scales and fidelities facilitate the continuing improvements of PEM electrolyzer development to improve performance, cost, and reliability. The model scopes and methods are presented in this paper, which also introduces a comprehensive PEM electrolysis modeling tool based on computational fluid dynamics (CFD) software, ANSYS/Fluent. The modeling tool incorporates electrochemical model of a PEM electrolysis cell to simulate the performance of coupled thermal-fluid, species transport, and electrochemical processes in a product-scale cell or stack by leveraging the powerful meshing generation and CFD solver of ANSYS/Fluent. The thermal-fluid modeling includes liquid water/gas two-phase flow and simulates a PEM electrolysis cell by using Fluent user-defined functions as add-on modules accounting for PEM-specific species transport and electrochemical processes. The modeling outcomes expediate PEM electrolyzer scaling up from basic material development and laboratory testing.

08 HYDROGEN↗
DOE OSTI2023

Operational and scaling-up barriers of SOEC and mitigation strategies to boost H 2 production- a comprehensive review

Solid oxide electrolysis cells (SOECs) proffer a flexible and environmentally friendly solution to reduce CO 2 emissions and/or produce H 2 fuel for various applications. Here, the integration of this technology with renewable energy systems makes them even more promising in realizing a near-perpetual green energy infrastructure in the near future. SOEC technology has achieved stupendous improvement in recent years, and some of the limiting factors to the commercialization of this technology have been explored. It is, therefore, necessary to have a succinct outlook of the different factors that contribute to the recent developments in SOEC technology. The current work presents a comprehensive review of the state-of-the-art SOEC component materials and their degradation mechanisms, SOEC modeling essentials and efficiencies, and the scalability of SOECs. Although SOEC is considered a highly promising technology and sufficient multifaceted research has been done, yet not been widely commercialized. Despite the improvement to mitigate degradation of the SOEC materials, a large share of the degradation concerns sprang from charge transfer reaction at the triple-phase boundaries of the H 2 -electrode. Part of which can be improved by microkinetic modeling of these materials supported by their characterization to give insight into their development. Some other directions for the future in advancing this technology toward the commercialization stage are thoroughly addressed.

08 HYDROGEN↗