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DOE OSTI2025

Uncertainty in inventories for life cycle assessment: State‐of‐the‐art, challenges, and new technologies

Uncertainty is a critical factor that can hinder the quality and potential applications of life cycle assessment (LCA) results. A prominent source of uncertainty stems from the life cycle inventory (LCI) data. Various methodologies exist to estimate the uncertainty associated with LCI data, primarily based on the widely used structured pedigree matrix approach or the computationally intensive Monte Carlo simulation. This perspective review explores how new technologies (e.g., computational algorithms and data collection methods) from data science and related fields can contribute to identifying, quantifying, and reducing uncertainty in LCI modeling. A brief overview of the sources of uncertainty in LCI modeling and how they are addressed in current LCA practice is provided. Additionally, several new technologies are identified, and the potential benefits of their implementation in reducing uncertainties in LCI modeling are discussed. This perspective review concludes by identifying potential areas that require further development for these technologies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Investigation of heat pump technologies for high-temperature applications above 250 °C

This paper examines heat pump (HP) technologies to achieve heat supply temperatures above 250 °C, referred to as ultra-high-temperature heat pump (UHTHP). UHTHP, as a low-carbon heat delivery and heat augmentation technology, can offer an alternative to traditional combustion heating for decarbonizing high-temperature industrial processes. However, the heat supply temperature of over 250 °C surpasses the temperature range typically covered in the high-temperature HP literature and existing capabilities of commercial HP systems. This paper reviews HP technologies with the potential to evolve into UHTHP. Additionally, UHTHP studies in the literature are analyzed to compare the pros, cons, and technical potentials of various HPs using different thermodynamic cycles and fluids. Furthermore, we then pinpoint the technical gaps and challenges that have hindered widespread adoption of UHTHP in industry, explore potential improvements and solutions, and discuss the feasibility. Current efforts aim to lay the ground for future research, development, and commercial deployment for UHTHP.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗

DOE OSTI2024

The importance of cycle-by-cycle data in performing rapid battery technology development and validation

Lithium-ion battery (LiB) technology is playing a crucial role in transforming the predominantly fossil fuel-based transportation and stationary storage sectors to achieve a low-carbon economy. Rapid innovation in the LiB materials to electrode to cell design is happening to satisfy the performance, life, and safety metrics required by those myriads of applications. Lately, advanced analytics, such as machine-learning or artificial intelligence (ML/AI) techniques, are being used more frequently to aid in expedited LiB technology development, performance validation, and life prediction. The success of these techniques often relies on a large volume of well-defined and high-quality battery test data. On the other hand, most battery developers and research and development (R&D) communities are still following a classical approach to develop batteries, which is running calendar- and/or cycle-aging tests, performing reference performance tests (RPTs), and conducting post-mortem analyses periodically without paying attention to the wealth of data often not collected during the calendar or cycle life aging tests. This sparse data collection approach is time- and resource-intensive, requiring data capture and evaluation of months to years of RPT data to diagnose accurate battery state of performance, health, and safety. Even so, the underlying aging modes and mechanisms can be missed. If collected properly, battery test data during cycling or calendaring can be efficiently combined with ML/AI techniques to create powerful tools in the rapid diagnosis of battery state of performance, health, and safety along with insights into underlying aging modes and mechanisms. In this report, we discuss the importance of effective cycle-by-cycle (CBC) data collection with example case studies. Within a reasonable timeframe, RPT data are often inadequate in capturing many of the crucial battery aging dynamics, which often predominantly show up in CBC test data. Finally, we also show examples of ML/AI techniques that use CBC data in rapid diagnosis and projection of LiB state of health (SOH) to motivate the scientific community in collecting and using CBC data to facilitate expeditious technology development and validation.

25 ENERGY STORAGE↗

DOE OSTI2025

Disruptive thermal-spectrum molten salt breeder reactor fuel cycle technologies

Multiple, disruptive fuel salt processing technologies for thermal-spectrum molten salt breeder reactors (TS-MSBRs) have been conceived of and brought to laboratory levels of maturity since the termination of the historic U.S. government program. This paper describes these technologies and the role that they could serve in decreasing the remaining technical risks for TS-MSBR deployment. In conclusion, the focus of the paper is on describing how the technologies could integrate into a dramatically simpler, proliferation resistant fuel salt processing system as well as identifying remaining development hurdles.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Barriers to adopting artificial intelligence and machine learning technologies in nuclear power

Artificial intelligence and machine learning (AI/ML) technologies offer unique opportunities to transform nuclear plant operations and power generation. Benefits will be felt not only within existing analog and digital instrumentation and control, but also within work processes, the integration of people with technology and most importantly, the business case. The application of this new technology can help simplify complex problems and produce more effective decision-making, making nuclear power safer, more efficient, and more economically viable in the current energy market. Nonetheless, there are potential barriers to its adoption that must be overcome. The purpose of this paper is to categorize, review, and discuss barriers to AI/ML adoption within the nuclear power industry, with a focus on existing commercial reactors. Unique considerations for advanced reactors are also offered. Here we provide a comprehensive overview of the historical, technical, and business barriers that the industry faces, as well as stakeholder readiness, and end-user acceptance. We underscore the importance of user experience and offer potential solutions in overcoming each barrier. These include provisions for easier plant data access, a friendly regulatory environment, and investment in user trust and explainable AI.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2026

A framework and calculator for evaluating the impacts of shelf life extension and other food loss and waste reduction technologies

Optimization of the food supply chain (FSC) depends on reducing food waste, especially at the consumer stage, where a substantial portion of food is not eaten, but instead disposed of via landfill, incineration, or in-sink disposals. One key strategy is to increase the time that consumers have before food goes bad or expires. This study developed a framework to assess the efficacy of shelf-life extension (SLE) technologies for mitigating food loss and waste (FLW), such as packaging improvements. The impact flows through the entire FSC, reducing FLW, energy use, and other inputs at each stage. The framework and resulting calculator can be used to evaluate the impact of FLW reduction at any stage for any food commodity. As shown by two SLE cases, the calculator is valuable for policy-makers, government entities, and professionals, specifically those in marketing, business development, and capital projects teams, to comprehensively evaluate the impacts of FLW reduction technologies and practices. The framework and calculator are sensitive to the shape of the consumption curve, the fraction of inedible waste, and the current shelf life. The calculator was used to assess the impacts of the United States goal of reducing food waste by consumers through various SLE lengths. It was found that uptake of several near-ready-to-deploy SLE technologies would reduce annual food production demand by about 10–19 MMT and supply chain energy consumption by 240–410 PJ in the United States.

Food loss and waste (FLW)↗

DOE OSTI2026

Cradle-to-Grave Lifecycle Analysis of U.S. Medium- and Heavy-Duty Vehicle-Fuel Pathways: A Greenhouse Gas Emissions Assessment of Current (2021) and Future (2035) Technologies

This study presents a cradle-to-grave lifecycle analysis of energy use and greenhouse gas (GHG) emissions for U.S. medium- and heavy-duty vehicles across current (2021) and future (2035) technologies using the Greenhouse gas, Regulated Emissions, and Energy use in Technologies (GREET) model with industry-vetted assumptions. Results vary across vehicle classes but point to common trends: today, battery electric vehicles (BEVs) offer significant (10–60%) GHG emissions reduction compared to diesel internal combustion engine vehicles and are the lowest emissions option per ton-mile of cargo movement, followed by hydrogen fuel cell electric vehicles (FCEVs) (5–50% emissions reduction). Emissions savings depend largely on the duty cycle and fuel economy of the vehicle type. Future vehicle technology advancements result in comparable emission reductions associated with BEVs and hydrogen FCEVs. Weight-limited BEV trucks see less per-ton-mile emissions reduction due to the impact of battery weight on increased vehicle weight and reduced payload capacity. By 2035, improvements in vehicle efficiency can reduce emissions across all powertrains. However, very low levels of emissions require switching vehicles’ use-phase fuel/energy to low-carbon fuels and electricity. Renewable diesel, e-fuels, hydrogen produced from natural gas with carbon capture and storage or renewables, and use of low-carbon electricity can all achieve over 70% reduction in GHG emissions from the current day diesel-based internal combustion engine vehicle.

alternative fuels↗

DOE OSTI2024

Technological evolution of large-scale blue hydrogen production toward the U.S. Hydrogen Energy Earthshot

Hydrogen potentially has a crucial role in the U.S. transition to a net-zero emissions economy. Learning from large-scale hydrogen projects will boost technological evolution and innovation toward the U.S. Hydrogen Energy Earthshot. We apply experience curves to estimate the evolving costs of blue hydrogen production and to further examine the economic effect on technological evolution of the Inflation Reduction Act’s tax credits for carbon sequestration and clean hydrogen. Learning-by-doing alone can decrease the production cost of blue hydrogen. Without tax incentives, however, it is hard for blue hydrogen production to reach the cost target of $\$1$/kg H 2 . Here we show that the breakeven cumulative production capacity required for gas-based blue hydrogen to reach the $\$1$/kg H 2 target highly depends on tax credit, natural gas price, inflation rate, and learning rates. We make recommendations for hydrogen hub development and for accelerating technological progress toward the Hydrogen Energy Earthshot.

08 HYDROGEN↗

DOE OSTI2024

Direct-bonded diamond membranes for heterogeneous quantum and electronic technologies

Diamond has superlative material properties for a broad range of quantum and electronic technologies. However, heteroepitaxial growth of single crystal diamond remains limited, impeding integration and evolution of diamond-based technologies. Here, we directly bond single-crystal diamond membranes to a wide variety of materials including silicon, fused silica, sapphire, thermal oxide, and lithium niobate. Our bonding process combines customized membrane synthesis, transfer, and dry surface functionalization, allowing for minimal contamination while providing pathways for near unity yield and scalability. We generate bonded crystalline membranes with thickness as low as 10 nm, sub-nm interfacial regions, and nanometer-scale thickness variability over 200 by 200 μm 2 areas. We measure spin coherence times T 2 for nitrogen vacancy centers in 150 nm-thick bonded membranes of up to 623 ± 21 μs, suitable for advanced quantum applications. We demonstrate multiple methods for integrating high quality factor nanophotonic cavities with the diamond heterostructures, highlighting the platform versatility in quantum photonic applications. Furthermore, we show that our ultra-thin diamond membranes are compatible with total internal reflection fluorescence (TIRF) microscopy, which enables interfacing coherent diamond quantum sensors with living cells while rejecting unwanted background luminescence. The processes demonstrated herein provide a full toolkit to synthesize heterogeneous diamond-based hybrid systems for quantum and electronic technologies.

color center↗

DOE OSTI2026

Challenges, Technological Pathways and Trade-Offs of Perovskite Solar Modules for Long-Term Operation

Perovskite solar modules (PSMs) have emerged as a promising photovoltaic technology due to their high efficiency, low fabrication cost and compatibility with lightweight and flexible applications. However, ensuring long-term reliable performance under real-world conditions remains a critical barrier to commercialization. PSMs degrade through mechanisms that differ substantially from those affecting established technologies such as silicon, particularly under environmental stressors like ultraviolet light, oxygen, temperature cycling and reverse bias. Here we provide an analysis of the degradation pathways specific to perovskite modules and discuss why standard accelerated tests often fail to predict outdoor performance. We conceptualize challenges across material, device and module levels and evaluate strategies to mitigate ion migration, interfacial breakdown and encapsulation failure. By highlighting the need for realistic testing protocols and durable materials, we propose a framework highlighting key challenges, technological pathways and the trade-offs required to extend perovskite module lifetimes towards long-term operation, aiming to guide the development of PSMs capable of a 30-year operational lifetime.

14 SOLAR ENERGY↗

DOE OSTI2024

Polyhydroxyalkanoates in emerging recycling technologies for a circular materials economy

Circular polymer systems, specifically polyesters operating through chemical and biological technologies, are approaching a critical moment of industrial adoption and scale-up feasibility. At the same time, polyhydroxyalkanoate (PHA) production, scale-up, and resulting material development is converging toward commodity applications. The current PHA end-of-life philosophy, however, focalizes leveraging inherent biodegradability to circumvent plastic waste accumulation. If indeed a substantial replacement of incumbent single-use plastics with PHA alternatives is to be met in commercial manufacture, we emphasize the importance of linking PHA development with feasible polymer recycling technologies. In other words, a PHA materials economy is significantly more carbon- and cost-favorable when efficient mechanical (reprocessing), chemical (deconstruction, depolymerization), or biological (enzymatic) recycling is prioritized over biodegradation or composting. In this perspective, we discuss strategies for PHA recyclable-by-design principles, guidable by developing machine learning tools, as well as material compatibility with closed-loop recycling technologies. Additionally, we posit compelling life-cycle assessment incentives for adopting polymer reclamation over competing pathways. Ultimately, we hope this narrative further inspires the alignment between PHA design with growing calls for a circular material economy.

36 MATERIALS SCIENCE↗

DOE OSTI2024

ChemPren: a new and economical technology for conversion of waste plastics to light olefins

With the ever-increasing demand for plastics, sustainable recycling methods are key necessities. Here, the current plastics industry can manage to recycle only 10% of the 400 million metric tons of plastic produced globally. Waste plastics, in the current infrastructure, land up mostly in landfills. Although a lot of research efforts have been spent on processing and recycling co-mingled mixed plastics, energy-efficient sustainable and scalable routes for plastic upcycling are still lacking. Catalytic valorization of waste plastic feedstock is one of the potential scalable routes for plastic upcycling. Silica-alumina based materials, and zeolites have shown a lot of promise. A major interest lies in restricting catalyst deactivation, and refining product selectivity and yield for such catalytic processes. This article highlights ChemPren technology as a clean energy solution to waste plastic recycling. Co-mingled, mixed plastic feedstock along with spray dried, attrition resistant, ZSM-5 containing catalysts is preprocessed with an extruder to form optimally sized particles and fed into a fluidized bed reactor for short contact times to produce selectively and in high yields ethylenes, propylenes and butylenes. This techno-economic perspective indicates that the ChemPren technology can produce propylene at $\$$0.16 per lb, whereas the current selling price of virgin propylene is $0.54 per lb. This technology can serve as a platform for mixed plastic upcycling, with more advancements necessary in the form of robust and resilient catalysts and reactor operation strategies for tuning product selectivity.

25 - ENERGY STORAGE↗

DOE OSTI2026

Material efficiency technologies in the food and beverage industry

The U.S. food and beverage (F&B) sector is a major contributor to manufacturing gross domestic product and supports substantial employment and economic activity, while exerting significant pressures on land and water resources. At the same time, the industry faces growing expectations to balance its resource-intensive operations without compromising cost competitiveness. Material inefficiencies across the F&B value chain, particularly in raw material use and product loss/waste, lead to substantial financial losses and resource depletion. Thus, the F&B sector requires adoption of solutions and measures to avoid food wastage, reduce raw material consumption and valorize waste to high-value added products. This work presents a comprehensive understanding of the various technology solutions available for the F&B sector. The following two research questions are addressed: “What are the mid-to-high Technology Readiness Level technologies or measures to reduce material use and enable waste valorization in the F&B sector? What are the barriers to their commercial deployment? Additionally, what targeted research and development efforts are needed to overcome these barriers and accelerate their scale-up?” The findings are intended to support evidence-based decision-making, guide strategic investment, and help stakeholders strengthen resilience and competitiveness across the F&B sector.

Nain, Preeti [ORNL] (ORCID:0000000258358959)↗

DOE OSTI2024

Toward Practical Federal Spectrum Sharing for Advanced Wireless Technologies

Wireless communications have become critical to modern society as the foundation of vital services, business interactions, and social connections. As technology develops and new scenarios emerge, wireless communications become increasingly entrenched as a foundation for modern life. The materialization of 5G has been especially impactful as an enabler for a wide range of new scenarios and increased dependence. With increasing demand for wireless communications comes increasing demand for the radio frequency (RF) spectrum necessary to support these communications. This spectrum demand, largely driven by desire to realize ubiquitous 5G networks, both public and private, is currently driving a reconsideration of spectrum assignments and forcing legacy users of spectrum to update their use of spectrum. As one of the largest legacy users of spectrum, the U.S. Government (USG) is particularly impacted by the increasing demand for spectrum for non USG use. Unfortunately, there is no clear pathway for U.S. federal agencies to modernize their spectrum use and instead these agencies are forced to simply react to spectrum repurposing driven by overwhelming demand. Here we present preliminary work toward a roadmap for federal agencies to modernize their spectrum use through practical analysis and leveraging emerging spectrum sharing (SS) technologies. This effort utilizes the example of DOE spectrum use in the context of National Telecommunications and Information Administration (NTIA) and Federal Communications Commission (FCC) repurposing to examine the factors that must be considered to facilitate spectrum sharing by federal agencies. Furthermore, this work provides an initial survey of the SS models and SS technologies of highest relevance to federal use. We particularly examine the emerging capabilities of 5G itself to enable SS and unlicensed access through transformative capabilities such as new radio unlicensed (NR-U). This work is a step toward translating the large body of academic discussion on SS into a practical roadmap for federal agencies.

5G↗

DOE OSTI2024

Emerging Technologies for Privacy Preservation in Energy Systems

This study explores the intersection of digitalization and privacy within the energy sector, focusing on the emerging challenges and opportunities presented by integrating Distributed Energy Resources (DERs) and advanced metering infrastructure. The need for robust digital privacy measures has become crucial as the energy industry evolves towards a more decentralized, digitalized, and decarbonized future. This study delves into four cutting-edge privacy-preserving technologies—Homomorphic Encryption (HE), Secure Multiparty Computation (SMPC), Differential Privacy (DP), and Federated Learning (FL)—each offering unique solutions to safeguard consumer data by increasing digital connectivity and data exchange. Through a detailed examination of these methods, the study explains how each technology operates, its applications within the energy sector, and the specific privacy challenges it addresses. Homomorphic Encryption allows for secure computations on encrypted data, enabling data analysis without compromising privacy. Secure Multiparty Computation enables collaborative data analysis across different entities while protecting the confidentiality of the inputs. Differential Privacy introduces randomness into the assembled data set, preventing the identification of individual records in statistical databases. Lastly, Federated Learning offers a paradigm shift in data analysis, where machine learning models are trained at the edge, minimizing the centralization of sensitive data. The research underscores the significance of implementing these privacy-enhancing technologies to comply with strict data protection regulations, foster consumer trust, and enhance the security of the energy infrastructure. By providing a comprehensive overview of these methodologies and their practical implications for the energy sector, this study aims to contribute to the ongoing discourse on digital privacy, offering insights into how the energy industry can navigate the complexities of data privacy in the digital age.

Cali, Umit↗

DOE OSTI2024

STREAM: Strategic Technology Roadmapping and Energy, Environmental, and Economic Analysis Model

This is the implementation of the framework for the planning of the technological makeup of the industrial sector. Motivated by the efforts to achieve carbon neutrality, the model of this framework modifies the portfolio of technologies over time for the sector of interest such that, - Net Present Value (NPV) is minimized - Constraint on Greenhouse Emissions (GHG), e.g. carbon dioxide, is satisfied - Demand of the underlying commodity is satisfied - The current implementation reflects a case study for the electric power sector. The for a given initial set of capacities of different vintages, the space of decisions include, - Retirement of the existing capacities. - Retrofitting the existing capacities to alternative characteristics. - Creation of new capacities from a technology portfolio. All the associated quantities with the deployments, e.g. CO2, heat requirement, etc.

Thiery, David↗

DOE OSTI2026

Autonomie Simulation Datasets in Support of U.S. DOT-NHTSA Advanced Vehicle Technology Research

Understanding how new vehicle technologies affect fuel economy and energy use is critical to the regulatory work performed by the U.S. Department of Transportation’s National Highway Traffic Safety Administration (NHTSA), which sets Corporate Average Fuel Economy (CAFE) standards under the Energy Policy and Conservation Act of 1975. In order to support this work, Argonne National Laboratory uses Autonomie, a full-vehicle simulation tool, to evaluate advanced powertrain architectures and their effects on vehicle energy consumption and performance. A wide range of vehicle classes has been assessed (i.e., internal combustion engine vehicles, hybrid electric vehicles, plug-in hybrid electric vehicles, battery-electric vehicles, and fuel cell electric vehicles), as well as the effects of various technology improvements such as lightweighting, aerodynamic refinements, and low-rolling-resistance tires. Simulations have been run across multiple drive cycles to capture fuel and electricity use under realistic operating conditions. The resulting datasets include detailed vehicle-level results, model assumptions, and validation reports, all of which have been made publicly available through NHTSA in support of the 2023 notice of proposed rulemaking covering light-duty vehicles for model years 2027 to 2035. These data are critical to stakeholders working in fuel economy regulation, vehicle technology assessment, and energy policy analysis.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Abstract for CRADA between National Energy Technology Laboratory and Elysia LLC

The National Energy Technology Laboratory (NETL) and Elysia Labs LLC are embarking on a collaborative project to develop and evaluate a low-cost, high-performance carbon dioxide capture process for the burgeoning Direct Air Capture (DAC) industry. The partnership will focus on developing an electrospun fiber based air contactor that promises higher efficiency and lower costs to set a new standard in the DAC field. The expected outcomes of this collaboration include the creation of a DAC contactor that significantly outperforms existing technologies in cost, capacity, cycle time, regeneration energy, and durability. This project is a result of an awarded project from the Department of Energy’s Direct Air Capture Pre-Commercialization Technology Prize.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗