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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 469 records · Page 26
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DOE OSTI2025

A Comparative Analysis of Waste-as-a-Feedstock Accounting Methods in Life Cycle Assessments

Global waste generation is a ubiquitous challenge, driving a paradigm shift towards viewing waste as a valuable resource for a circular economy across diverse sectors. While innovative waste-to-resource pathways are crucial, rigorous Life Cycle Assessment (LCA) is essential to ensure the pathways are an important part of current practices. However, LCA application to waste valorization varies, leading to incomparable results due to differing methodological choices. This paper examines three key nuances in waste-as-resource LCAs: the zero-burden assumption, the biogenic carbon neutrality assumption, and the benchmark assumption for emissions avoidance. Using a waste gasification to hydrogen case study, we demonstrate how these methodological decisions impact LCA outcomes. Our findings reveal that waste composition significantly influences the results and highlight challenges associated with biogenic carbon accounting under various system boundary assumptions. Emissions avoidance accounting requires multi-functional unit perspectives and robust benchmark selection. This paper clarifies these accounting approaches, empirically illustrates their influence, and discusses broad implications for accurate sustainability assessment, emphasizing the critical role of transparent LCA choices for effective policy and investment in circular economy solutions.

LCA method↗
DOE OSTI2022

Evaluating Image Classification Deep Convolutional Neural Network Architectures for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition-based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available, and physical modeling is often not feasible. Additionally, using data-based models, which make decisions based on raw sensor data, allow features to be learned instead of manually determined. In this work, deep convolutional neural network (CNN) architectures are investigated for their ability to estimate the RUL of turbofan engines. To improve the accuracy of the models, CNN architectures, which have proven successful in image classification, are implemented and tested. Specifically, the blocks used in the Visual Geometry Group (VGG) architecture, inception modules used in the GoogLeNet architecture, and residual blocks used in the ResNet architecture are incorporated. To account for varying flight lengths, the input to the models is a window of time series data collected from the engine under test. Window locations at the climb, cruise, and descent stages are considered. To further improve the RUL estimations, multiple overlapping windows at each location are used. This increases the amount of training data available and is found to increase the accuracy of the resulting RUL estimations by averaging the estimates from all overlapping segments. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set, and high prognosis accuracy was achieved. Furthermore, this work expands on the model developed and used in the 2021 PHM Society Data Challenge, which received second place.

convolutional neural networks↗
DOE OSTI2021

Life Cycle Assessment of Pasture-based Agrivoltaic Systems: Emissions and Energy Use of Integrated Rabbit Production

Agrivoltaic systems, which deliberately maximize the utility of a single parcel of land for both solar photovoltaic (PV) electricity production and agriculture, have been demonstrated as a viable technology that can ameliorate competing land uses and meet growing energy and food demands efficiently. The goal of this study is to assess the environmental impacts of a novel pasture-based agrivoltaic concept: co-farming rabbits and solar PV. A life cycle assessment (LCA) quantified the impacts of 1) the integrated agrivoltaic concept in comparison to conventional practices including 2) separate rabbit farming and PV production and 3) separate rabbit farming and conventional electricity production. The impact assessment methods employed to determine the environmental impacts were IPCC 2013 global warming potential 100a V1.03 and fossil energy demand V1.11. The results indicate that the pasture-based agrivoltaic system produces the least amount of greenhouse gas emissions (3.8 million kg CO₂ equivalent) and demands the least amount of fossil energy (46 million MJ) per functional unit of cumulative MWh output of electricity and cumulative kg of meat over 30 years in comparison to the two other scenarios under study. The pasture-based agrivoltaic system features a dual synergy that consequently produces 69.3% less emissions and demands 82.9% less fossil energy compared to non-integrated production. The potential for agrivoltaic systems to significantly reduce environmental impacts revealed by this LCA, which was limited to emissions and fossil energy demand, demonstrates that integrated solar and pasture-based agricultural systems are superior to conventional practices in terms of their comparatively lower emission and energy intensity. These findings provide empirical support for increased agrivoltaic system development more broadly.

14 SOLAR ENERGY↗
DOE OSTI2022

4.2.1.31 Integrated Life Cycle Sustainability Analysis

This project provides the Department of Energy's Bioenergy Technologies Office (BETO) with strategic decision-support for the evaluation of its R&D portfolio by developing, validating, and applying a coherent methodology and consistent model framework to quantify the net effects of an expanding US bioeconomy. The framework fills an analysis gap previously identified by Peer Review and supports a related milestone in BETO's Multi-Year Program Plan. The framework was scoped with inputs from practitioners in academia, national laboratories, and federal agencies. The model is a top-down, economy-wide framework using a coherent methodology to compute environmental and socio-economic metrics. It is purposefully complementary to existing bottom-up, process-based techno-economic and life cycle assessment BETO tools and uses their data as inputs. Presently, the model covers several commercial and near-commercial biofuel routes and an emerging pathway for plastics upcycling. It covers temporal detail across four time-steps and is currently being expanded with a prospective modeling capability. The model has provided analyses for the Third Triennial Report to Congress (RtC3) on the environmental impacts of the Renewable Fuel Standard (RFS2), among others. As part of this project, NREL also provides scientific support to BETO in the International Energy Agency's Technology Collaboration Program on Bioenergy (IEA Bioenergy) Task 45 on Sustainability. Here, NREL evaluates and synthesizes activities that develop, compare, or apply metrics, methods, and tools to quantify sustainability effects of bioeconomy products. NREL also coordinates related national lab involvement and a BETO Working Group on Sustainable Land Management.

bioeconomy↗
DOE OSTI2022

Role of Life Cycle Assessment in the Circular Economy

Industry leaders are increasingly turning to the circular economy to address a global need for reducing waste and reusing materials. Stakeholder pressure is mounting to establish better practices for more sustainable systems. Join your industry colleagues for an informative workshop to connect and accelerate standards development related to the circular economy of discarded manufacturing materials. The objective of the workshop is to develop a roadmap for ASTM technical standards to support the formation and engagement of manufacturers in a circular economy. Particular focus will be given to standards to support manufacturers in bringing their discarded materials into a mature materials marketplace as well as standards to measure and improve processing efficiencies resulting in reductions in discarded materials. The resulting roadmap will identify priorities for near-term standardization efforts. The roadmap will identify the necessary definitions for information flows supporting a sophisticate ecosystem for traceability and reuse of the materials. What is the role that life cycle assessment should have in setting manufacturing standards for applying circular economy strategies?

circular economy↗
DOE OSTI2022

Pathways to a Sustainable Aviation Ecosystem: Life Cycle Analysis with Blockchain Carbon Accounting

When linked with the National Renewable Energy Laboratory's (NREL's) rigorous life cycle assessment methodologies, blockchain technology can enable aviation stakeholders to quantify and transact on the impacts and benefits of sustainable aviation fuels and technologies. NREL can develop high-fidelity carbon modeling tools that enable apples-to-apples comparisons across technologies, fuels, companies, and jurisdictions.

aviation↗
DOE OSTI2024

Task 3.1: Research and Development Guiding Technoeconomic Analysis and Life-Cycle Assessment

Technologies under development aim to increase yields of desired end products, reduce overall raw material costs, and/or develop more energy-efficient strategies for product recovery. Techno-economic analysis (TEA) and life-cycle assessments (LCA) help assure that economic and sustainability predictions of the technologies are unbiased and compelling and provide guidance to experimentalists on areas that need focus. Building on previous work, the performance-advantaged bioproducts and bioprocessing separations project (SepCon) continues to use the integrated TEA and LCA to evaluate and guide technologies under development and target challenges relevant to the industry and the Bioenergy Technologies Office (BETO) priority pathways. The analysis team aims to provide credible, unbiased assessments for each technology under development, with ongoing assessments to guide experimental teams. Additionally, the team also supports journal publications highlighting key findings.

biofuels↗
DOE OSTI2025

Data-Driven State of Health Estimation for Second-Life Batteries Using Interpolated Synthetic Data and Feature Selection

Accurate estimation of the State of Health (SOH) for second-life batteries (SLBs) is crucial given their increasing use in energy storage applications. Precise SOH prediction is essential for safe operation and robust battery management systems. A major challenge is the limited availability of datasets for building reliable degradation models. To address this, synthetic data generation through linear interpolation is performed to extend the available data, making it more representative of real-world battery operating conditions. By analyzing feature correlation with SOH, the most relevant features are selected for the model. The proposed approach employs a convolutional neural network (CNN) model trained on this interpolated, feature-selected dataset, using time series data of voltage, temperature, and current over a cycle. By focusing on highly correlated features, the model achieves over 95% accuracy, with mean absolute error and root mean squared error up to 2.27% and 2.64%, respectively, in SOH estimation for two battery datasets tested. These results highlight the potential of combining synthetic data generation and feature selection to enhance SOH predictions, showcasing the superior performance of the proposed CNN model for both new batteries and SLBs.

feature selection↗
DOE OSTI2025

Techno-Economic Assessments of Second-Life Batteries for Electric Vehicle Charging Stations

When electric vehicle (EV) batteries degrade below a certain capacity, they may no longer be suitable for automotive use but can be repurposed as second-life batteries (SLBs) for other applications, such as EV charging stations. When integrated with photovoltaic (PV) systems, SLB can store surplus solar energy, reducing reliance on the grid and lowering operational costs. This paper presents a novel techno-economic assessment framework for deploying SLBs in combination with PV in grid-connected EV charging stations. The proposed framework integrates the value proposition, charging station operation, optimal dispatch strategies, battery degradation modeling, input data requirements, and detailed procedures for generating key economic performance metrics. Insightful analyses are performed to assess the performance of SLBs in comparison to new batteries across various cost scenarios. The results indicate that SLBs become financially attractive when their cost is 40% or lower than new batteries.

energy storage↗
DOE OSTI2023

ECAR-6076 Rev 0 MARVEL Reactor End of Life Enveloping Radiological Source Term

As part of the Microreactor Applications Research Validation and Evaluation Project (MARVEL) design effort, an enveloping end of life radiological source term for the reactor fuel and sodiumpotassium (NaK) eutectic coolant is calculated in support of the MARVEL preliminary hazard evaluation. The source term calculation assumes slightly more than two-years of uninterrupted operations at a power of about 120 kW for a total energy of 7,600,000 MJ which exceeds the planned two-year operation at 100 kW. The Reactor modeling was performed with MCNP6 Version 1.0 and the depletion was performed using the COUPLE and ORIGEN modules from the SCALE Version 6.2 code package.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2026

Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials

This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Paving Pathways Toward Long–Life Graphite/LiNi 0.5 Mn 1.5 O 4 Full Cells: Electrochemical and Interphasial Points of View

The high-voltage spinel cathode LiNi 0.5 Mn 1.5 O 4 (LNMO) with no cobalt and low nickel content is promising for lithium-ion batteries due to its high energy and power densities, good thermal stability, and low cost. However, its high operating voltage (≈4.7 V) results in a decomposition of the electrolyte, severe chemical crossover, and deterioration of electrode-electrolyte interphases (EEIs), hindering its practical viability. It is demonstrated here that by electrochemically pre-cycling the graphite in an electrolyte containing 30 wt.% fluoroethylene carbonate, a robust LiF-rich artificial solid electrolyte interphase can be constructed for surface protection; on the other hand, an electrochemical pre-lithiation of Fe-doped LNMO serves as a lithium source for graphite at a full-cell voltage of 2.6 V. As a result, the full cells with the as-modified electrodes deliver a high capacity of 129 mA h g –1 with an excellent capacity retention of 93% after 200 cycles, vastly outperforming the full cells with fresh electrodes (120 mA h g –1 initial capacity with 78% retention). Lastly, pathways toward long-life graphite||LNMO full cells are pictured based on the inspirations from the electrochemical modifications and in-depth analyses of the EEIs in this study.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Conductive Ink with Circular Life Cycle for Printed Electronics

Electronic waste carries energetic costs and an environmental burden rivaling that of plastic waste due to the rarity and toxicity of the heavy-metal components. Recyclable conductive composites are introduced for printed circuits formulated with polycaprolactone (PCL), conductive fillers, and enzyme/protectant nanoclusters. Circuits can be printed with flexibility (breaking strain ≈80%) and conductivity (≈2.1 × 10 4 S m -1 ). These composites are degraded at the end of life by immersion in warm water with programmable latency. Approximately 94% of the functional fillers can be recycled and reused with similar device performance. The printed circuits remain functional and degradable after shelf storage for at least 7 months at room temperature and one month of continuous operation under electrical voltage. Here, the present studies provide composite design toward recyclable and easily disposable printed electronics for applications such as wearable electronics, biosensors, and soft robotics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Carbon-Nanotube-Bridging Strategy for Integrating Single Fe Atoms and NiCo Nanoparticles in a Bifunctional Oxygen Electrocatalyst toward High-Efficiency and Long-Life Rechargeable Zinc–Air Batteries

The development of highly efficient and robust bifunctional electrocatalysts for oxygen reduction (ORR) and evolution reactions (OER) is the key issue for realizing high-performance and long-life rechargeable zinc–air batteries (ZABs). However, it is still a great challenge to integrate independent ORR and OER sites in a catalyst with high activity. Here, a carbon nanotube-bridging strategy is proposed to synthesize such a bifunctional oxygen electrocatalyst enriched with highly active single-atom Fe sites for the ORR and high-performance nanosized NiCo hydroxides for the OER. Consequently, the developed catalyst shows a small overpotential difference of 0.686 V. When used as an oxygen electrode catalyst, the corresponding ZAB exhibits a large power density of 219.5 mW cm ₋2 , a small charge–discharge voltage gap of 0.72 V at 10 mA cm ₋2 , and outstanding discharge–charge durability without attenuation after more than 700 cycles. This work proposes a new idea to realize multifunctional catalysts and drives the practical application of ZABs.

25 ENERGY STORAGE↗