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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High–Performance NiCo 2 O 4 /Graphene Quantum Dots for Asymmetric and Symmetric Supercapacitors with Enhanced Energy Efficiency

For the sustainable growth of future generations, energy storage technologies like supercapacitors and batteries are becoming more and more common. However, reliable and high-performance materials’ design and development is the key for the widespread adoption of batteries and supercapacitors. Quantum dots with fascinating and unusual properties are expected to revolutionize future technologies. However, while the recent discovery of quantum dots honored with a Nobel prize in Chemistry, their benefits for the tenacious problem of energy are not realized yet. In this context, herein, chemical-composition tuning enabled exceptional performance of NiCo 2 O 4 (NCO)/graphene quantum dots (GQDs) is reported, which outperform the existing similar materials, in supercapacitors. A comprehensive study is performed on the synthesis, characterization, and electrochemical performance evaluation of highly functional NCO/GQDs in supercapacitors delivering enhanced energy efficiency. The high-performance, functional NCO/GQDs electrode materials are synthesized by the incorporation of GQDs into NCO. The effect of variable amount of GQDs on the energy performance characteristics of NCO/GQDs in supercapacitors is studied systematically. In-depth structural and chemical bonding analyses using X-ray diffraction (XRD) and Raman spectroscopic studies indicate that all the NCO/GQDs composites crystallize in the spinel cubic phase of NiCo 2 O 4 while graphene integration evident in all the NCO/GQDs. The scanning electron microscopy imaging analysis reveals homogeneously distributed spherical particles with a size distribution of 5–9 nm validating the formation of QDs. The high-resolution transmission electron microscopy analyses reveal that the NCOQDs are anchored on graphene sheets, which provide a high surface area of 42.27 m 2 g –1 and high mesoporosity for the composition of NCO/GQDs-10%. In addition to establishing reliable electrical connection to graphene sheets, the NCOQDs provide reliable 3D-conductive channels for rapid transport throughout the electrode as well as synergistic effects. Chemical-composition tuning, and optimization yields NCO/GQDs-10% to deliver the best specific capacitance of 3940 Fg –1 at 0.5 Ag –1 , where the electrodes retain ≈98% capacitance after 5000 cycles. The NCO/GQD-10%//AC asymmetric supercapacitor device demonstrates outstanding energy density and power density values of 118.04 Wh kg –1 and 798.76 W kg –1 , respectively. The NCO/GQDs-10%//NCO/GQDs-10% symmetric supercapacitor device delivers excellent energy and power density of 24.30 Wh kg –1 and 500 W kg –1 , respectively. These results demonstrate and conclude that NCO/GQDs are exceptional and prospective candidates for developing next-generation high-performance and sustainable energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2020

Pilot Plant Reliability Metrics for Grinding and Fast Pyrolysis of Woody Residues

The reliability of biomass size reduction, feeding, and conversion processes remains a critical technical hurdle and important cost driver for biomass conversion systems. While Biorefinery projects have typically focused on scaling up conversion technologies that maximize yield of desired products, e.g. fuels and chemicals, solids handling and reactor feeding systems are often considered secondarily. These are frequently scaled down or adapted from other industries, without a full appreciation of how biomass heterogeneity and variability impact process reliability. Here we present the effects of feedstock variability on relative throughput, mass loss, plant uptime, operator intervention, product yield, and product quality for grinding and fast pyrolysis operations at the pilot scale. Loblolly pine residues were sourced and collected using commercial harvesting techniques. Grinding and pyrolysis experiments were performed at INL's Biomass Feedstock National User Facility and NREL's Thermal and Catalytic Process Development Unit, respectively. Preprocessing throughput using a hammer mill varied between 31-48% of nameplate capacity (5 tons/hr), and fast pyrolysis throughput varied between 57-72% of nameplate capacity (20 kg/hr) for an overall system throughput of 18-35% of nameplate capacity across biomass preprocessing and conversion to pyrolysis oil. Pyrolysis oil yields varied between 46-53% (feedstock carbon to oil, dry basis). Grinder overloads in the size reduction step were more prevalent for lower ash and higher moisture materials. During fast pyrolysis operations, downtime was caused by bridging in the feed and char removal systems and plugging in the condensation system, and significant variation in the type and frequency of operator intervention was observed between feedstocks. Cohesion of solids (feedstock and char) leading to system plugging appeared less problematic for higher ash feedstocks, and differences in condenser plugging behavior between high and low ash feedstocks indicates the composition and physical nature of the deposited material is important for process reliability and is feedstock dependent.

09 BIOMASS FUELS↗
DOE OSTI2021

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Engineering the fracture resistance of 2H-transition metal dichalcogenides using vacancies: An in-silico investigation based on HRTEM images

Vacancy engineering of 2H-transition metal dichalcogenides (2H-TMDs) has recently attracted great attention due to its potential to fine-tune the phonon and opto-electric properties of these materials. From a mechanical perspective, this symmetry-breaking process typically reduces the overall crack resistance of the material and adversely affects its reliability. However, vacancies can trigger the formation of heterogeneous phases that synergistically improve fracture properties. In this study, using MoSe2 as an example, we characterize the types and density of vacancies that can emerge under electron irradiation and quantify their effect on fracture. Molecular dynamic (MD) simulations, employing a re-parameterized Tersoff potential capable of accurately capturing bond dissociation and structural phase changes, reveal that isolated transition metal monovacancies or chalcogenide divacancies tend to arrest the crack tip and hence enhance the monolayer toughness. In contrast, isolated chalcogenide monovacancies do not significantly affect toughness. The investigation further reveals that selenium vacancy lines, formed by high electron dose rates, alter the crack propagating direction and lead to multiple crack kinking. Using atomic displacements and virial stresses together with a continuum mapping, displacement, strain, and stress fields are computed to extract mechanistic information, e.g., conditions for crack kinking and size effects in fracture events. The study also reveals the potential of specific defect patterns, “vacancy engineering,” to improve the toughness of 2H-TMDs materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

PV in the circular economy, a dynamic framework analyzing technology evolution and reliability impacts

Rapid, terawatt-scale deployment of photovoltaic (PV) modules is required to decarbonize the energy sector. Despite efficiency and manufacturing improvements, material demand will increase, eventually resulting in waste as deployed modules reach end of life. Circular choices for decommissioned modules could reduce waste and offset virgin materials. We present PV ICE, an open-source python framework using modern reliability data, which tracks module material flows throughout PV life cycles. We provide dynamic baselines capturing PV module and material evolution. PV ICE includes multimodal end of life, circular pathways, and manufacturing losses. We present a validation of the framework and a sensitivity analysis. Results show that manufacturing efficiencies strongly affect material demand, representing >20% of the 9 million tons of waste cumulatively expected by 2050. Reliability and circular pathways represent the best opportunities to reduce waste by 56% while maintaining installed capacity. Shorter-lived modules generate 81% more waste and reduce 2050 capacity by 6%.

14 SOLAR ENERGY↗
DOE OSTI2023

Survey Corrosion Testing Methodologies for Additively Manufactured Materials – PNNL

The Advanced Materials and Manufacturing Technology (AMMT) Program intends to develop cross-cutting technologies in support of a broad range of nuclear reactor technologies, and to maintain U.S. leadership in materials and manufacturing technologies for nuclear energy applications. The overarching vision of the AMMT Program is to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fully Inkjet‐Printed, 2D Materials‐Based Field‐Effect Transistor for Water Sensing

Abstract Despite significant progress in solution‐processing of 2D materials, it remains challenging to reliably print high‐performance semiconducting channels that can be efficiently modulated in a field‐effect transistor (FET). Herein, electrochemically exfoliated MoS 2 nanosheets are inkjet‐printed into ultrathin semiconducting channels, resulting in high on/off current ratios up to 10 3 . The reported printing strategy is reliable and general for thin film channel fabrication even in the presence of the ubiquitous coffee‐ring effect. Statistical modeling analysis on the printed pattern profiles suggests that a spaced parallel printing approach can overcome the coffee‐ring effect during inkjet printing, resulting in uniform 2D flake percolation networks. The uniformity of the printed features allows the MoS 2 channel to be hundreds of micrometers long, which easily accommodates the typical inkjet printing resolution of tens of micrometers, thereby enabling fully printed FETs. As a proof of concept, FET water sensors are demonstrated using printed MoS 2 as the FET channel, and printed graphene as the electrodes and the sensing area. After functionalization of the sensing area, the printed water sensor shows a selective response to Pb 2+ in water down to 2 ppb. This work paves the way for additive nanomanufacturing of FET‐based sensors and related devices using 2D nanomaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Sub-sized specimen testing and its relationship with Gen IV Materials Qualification

Renewed interest in advanced reactor technologies which can produce inherently safe, reliable, low carbon, low cost energy requires the development of new materials which can operate in the extreme environments proposed by Gen IV reactor designs. The primary focus of the Nuclear Materials Discovery and Qualification Initiative (NMDQi) is to accelerate the process by which new materials can make their way into commercial use, enabling new technologies and innovations within the nuclear sector. Due to limitations in the production of neutron irradiated material for testing and qualification, the use of specimens far smaller than those traditionally called for in regulatory codes becomes a necessity. While the use of these sub-sized specimens is necessary to achieve materials qualification on a realistic timeline and budget, literature showing the efficacy of these samples as an appropriate surrogate for ASME/ASTM defined samples is limited. This poster will detail historical efforts to compare the mechanical behavior of small geometry samples with bulk material behavior and the potential of sub-sized specimen testing at INL for future materials qualification efforts.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Enabling Sustainable Lithium-Ion Battery Manufacturing via Recycling

With millions of electric vehicles set to hit the road, challenges arise with both the access to a reliable supply of critical raw materials and management of spent Li-ion batteries that once powered those vehicles. From a circular economy perspective, it is critical to develop cost-effective and environmentally sustainable recycling technologies to manage end-of-life batteries and reintroduce the recovered materials into the supply chain to maintain a secure and healthy battery ecosystem. In this chapter, an overview of the current state of battery recycling is discussed by analyzing recycling’s benefits, key issues, and recycling strategies including pyrometallurgy, hydrometallurgy, and direct recycling. We also compare those recycling processes and highlight the direct recycling strategy by analyzing its benefits, working principles, and challenges. Direct recycling is still in the development stage and some recent advancements are discussed.

Bai, Yaocai↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2020

High-throughput, combinatorial synthesis of multimetallic nanoclusters

Significance Multielement nanomaterials hold great promise for various applications due to their widely tunable surface chemistry, yet it remains challenging to efficiently study this multidimensional space. Conventional approaches are typically slow and depend on serendipity, while a robust and general synthesis is still lacking among increasingly complex compositions. We report a high-throughput technique for combinatorial compositional design (formulation in solution phases) and rapid synthesis (within seconds) of ultrafine multimetallic nanoclusters with a homogeneous alloy structure. We synthesized and screened the PtPdRhRuIrFeCoNi compositional space using scanning droplet cell electrochemistry, with two promising catalysts quickly identified and further verified in a rotating disk setup. The reported high-throughput approach establishes a facile and reliable pipeline to significantly accelerate material discovery in multimetallic nanomaterials.

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

Experimental and Computational Investigation of Integrated Internal and Film Cooling Designs Incorporating a Thermal Barrier Coating

Abstract Few studies in the open literature have studied the effect of thermal barrier coatings (TBC) when used in combination with shaped hole film cooling and enhanced internal cooling techniques. The current study presents Reynolds-averaged Navier–Stokes (RANS) conjugate heat transfer simulations that identify trends in cooling design performance as well as experimental measurements of overall effectiveness using a flat-plate matched-Biot number model with a simulated TBC layer of 0.42D thickness, where D is the film cooling hole diameter. Coolant is fed to the film cooling holes in a co-flow configuration, and the results of both the smooth and rib-turbulated channels are compared. At a constant coolant flow rate, enhanced internal cooling was found to provide a 44% increase in spatially-averaged overall effectiveness, $\bar{\bar{\phi}}$, without a TBC. The results show that the addition of a TBC can raise $\bar{\bar{\phi}}$ on a film-cooled component surface by 47%. The optimum velocity ratio was found to decrease with the addition of enhanced cooling techniques and a TBC as the film provided minimal benefit at the expense of reduced internal cooling. While the computational results closely identified trends in overall system performance without a TBC, the model over-predicted effectiveness on the metal–TBC interface. The results of this study will inform turbine component design as material science advances increase the reliability of the TBC.

Engineering↗
DOE OSTI2023

Durable Module Materials (DuraMAT) Consortium (Final Technical Report)

The DuraMAT Consortium brings together DOE national lab and university research capabilities with the photovoltaic (PV) and supply-chain industries to accelerate a sustainable, just, and equitable transition to zero carbon electricity generation by 2035 through our five core objectives: development of a central data resource for PV modules, multi-scale and multi-physics modeling, disruptive acceleration science, forensic tools for fielded modules, and materials solutions for more durable, reliable, and resilient modules.

14 SOLAR ENERGY↗
DOE OSTI2023

Combining Molecular Simulations and Machine Learning to Comprehensively Explore Adsorption Space (Final Report)

This is the final report for the project "Combining Molecular Simulations and Machine Learning to Comprehensively Explore Adsorption Space". The central aim of this project was to develop modeling tools that vastly increase the diversity of molecules for which reliable adsorption isotherms in nanoporous materials are available. Within the project, molecular simulation data for a diverse set of examples was combined with machine-learning (ML) methods to rapidly predict adsorption equilibria in crystalline nanoporous materials (primarily MOFs).

36 MATERIALS SCIENCE↗
DOE OSTI2024

Preliminary Characterization and Evaluation on ShAPE Manufactured 316H and ODS Steels

This study provides the first- of- a- kind results of direct tube formation through shear assisted processing and extrusion (ShAPE) for oxide dispersion strengthened (ODS) steel material; previously only bar was successfully made. The Advanced Materials and Manufacturing Technology (AMMT) program develops cross-cutting technologies in support of a broad range of nuclear reactor technologies and maintains U.S. leadership in materials and manufacturing technologies for nuclear energy applications. The overarching vision of AMMT is to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. Solid-state advanced manufacturing techniques can overcome some of the challenges in liquid-based additive manufacturing processes and should therefore be considered in material design and manufacturing as well. The work presented in this report forms part of a study on solid-state additive manufacturing techniques of 316 stainless steels and ODS steel components and supports the vision and goals of the AMMT program relevant to accelerate the development and deployment of advanced manufacturing processes. Achieving this can provide a safety improvement through larger safety margins, economic benefit for higher efficiency during operation, and a cost reduction through more effective manufacturing processes and less waste.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Preliminary Characterization and Evaluation on FFF Manufactured 316H and ODS Steels

The Advanced Materials and Manufacturing Technology (AMMT) program develops cross-cutting technologies in support of a broad range of nuclear reactor technologies and maintains U.S. leadership in materials and manufacturing technologies for nuclear energy applications. The overarching vision of AMMT is to accelerate the development, qualification, demonstration, and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. Solid-state advanced manufacturing techniques can overcome some of the challenges in liquid-based additive manufacturing (AM) processes and should therefore be considered in material design and manufacturing as well. The work presented in this report forms part of a study on solid-state AM techniques of 316 stainless steels (SS) and oxide dispersion strengthened (ODS) steel components and supports the vision and goals of the AMMT program relevant to accelerate the development and deployment of advanced manufacturing processes. Achieving this can provide a safety improvement through larger safety margins, economic benefit for higher efficiency during operation, and a cost reduction through more effective manufacturing processes and less waste. This study provides preliminary information on development of the fused-filament fabrication (FFF) process using different powder types to demonstrate the sensitivity, and therefore the characterization of these sample components. The full solid-state manufacturing feasibility study will be completed and reported in a final feasibility evaluation during 2025. The study investigation used two 14YWT ODS powder batches, which provided information and the effect of different powder morphologies on manufacturability. The two 316SS powders demonstrated the effect of powder size on the manufacturability using FFF. Two product forms, namely a honeycomb structure and flat samples, were manufactured to demonstrate the flexibility of product form.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Li 7 P 2 S 8 Br 0.5 I 0.5 (LPSBI) solid state electrolyte by XPS

There have been increasing surface characterization studies of battery materials specifically in the context of before and after cell operation to determine any chemical changes. Therefore, providing reliable reference spectra of battery-related materials is important. In this paper, survey and high-energy resolution data are reported for the solid-state electrolyte Li 7 P 2 S 8 Br 0.5 I 0.5 (LPSBI), which has been synthesized and characterized at Pacific Northwest National Laboratory. The current data presents narrow scan regions of I 3d, I 4d, Br 3d, O 1s, P 2p, S 2p, and C 1s core level spectra as well as wide-scan survey data that was obtained using Al K a x-ray source with a Thermo Fisher Nexsa instrument.

25 ENERGY STORAGE↗
DOE OSTI2021

Towards validation of combined-accelerated stress testing through failure analysis of polyamide-based photovoltaic backsheets

Abstract Novel methods for advancing reliability testing of photovoltaic (PV) modules and materials have recently been developed. Combined-accelerated stress testing (C-AST) is one such method which has demonstrated reliable reproduction of some field-failures which were not reproducible by standard certification tests. To increase confidence and assist in the development of C-AST, and other new testing protocols, it is important to validate that the failure modes observed and mechanisms induced are representative of those observed in the field, and not the product of unrealistic stress conditions. Here we outline a method using appropriate materials characterization and modelling to validate the failure mechanisms induced in C-AST such that we can increase confidence in the test protocol. The method is demonstrated by applying it to a known cracking failure of a specific polyamide (PA)-based backsheet material. We found that the failure of the PA-based backsheet was a result of a combination of stress factors. Photo-oxidation from ultra-violet (UV) radiation exposure caused a reduction in fracture toughness, which ultimately lead to the cracking failure. We show that the chemical and structural changes observed in the backsheet following C-AST aging were also observed in field-aged samples. These results increase confidence that the conditions applied in C-AST are representative of the field and demonstrates our approach to validating the failure mechanisms induced.

14 SOLAR ENERGY↗