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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

An accelerated framework for predicting creep rupture lifetimes in engineering alloys

Confidently predicting high-temperature deformation, including creep and creep rupture, is paramount for the design and commercialization of candidate materials for advanced nuclear energy systems. To accelerate creep quantification, we introduce a framework that enables rapid, cost-effective, and reliable prediction of creep rupture lifetimes, minimizing reliance on time-intensive bulk creep testing. Unlike conventional creep analysis, which requires extensive time and resources, our method leverages a maximum of four short-term bulk creep tests as training data for prediction. This framework combines high-throughput nanoindentation up to 700 °C with these targeted bulk tests to inform our creep rupture model in order to predict rupture lifetimes. The strong agreement between our predictions and conventional experimental data demonstrates the effectiveness of our approach for accelerated creep analysis and lifetime prediction of structural components in high-temperature applications. Our multi-pronged approach motivates further integration of computational tools and advanced instrumentation to establish a universal framework for understanding high-temperature material responses.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Recycling of silver from silicon solar cells by laser debonding

A laser-debonding approach to silver electrode recovery from solar cells is presented to address the critical need for efficient and eco-friendly recycling methods. The study explores the use of UV nanosecond and IR continuous-wave lasers to precisely debond silver electrodes from silicon wafers. By optimizing parameters like laser power, scan speed, and the number of passes, intact silver electrical contact lines were recovered from solar cells. Scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS) analysis confirmed the successful separation of silver from silicon wafers. Furthermore, an increase in the number of laser passes led to the production of silver microparticles, as validated by morphology and compositional studies using EDS. The estimated silver is approximately 90 mg for the Si solar cells of 15.4 * 15.4 cm 2 used in the study. To achieve a large area recovery of silver, a MATLAB code incorporating detection algorithms and specific criteria to automate laser scanning by accurately identifying the location of silver electrodes was developed. This presented laser debonding method is not confined to silicon solar cells but can be extended to other solar cell types featuring metal electrodes, offering a versatile solution. By minimizing the use of hazardous chemicals and reducing operational costs, the developed process aligns with the imperative of environmentally responsible photovoltaic waste management.

14 SOLAR ENERGY↗
DOE OSTI2022

Methods to Calculate Electronic Excited-State Dynamics for Molecules on Large Metal Clusters with Many States: Ensuring Fast Overlap Calculations and a Robust Choice of Phase

Here, we present an efficient set of methods for propagating excited-state dynamics involving a large number of configuration interaction singles (CIS) or Tamm-Dancoff approximation (TDA) single-reference excited states. Specifically, (i) following Head-Gordon et al., we implement an exact evaluation of the overlap of singly-excited CIS/TDA electronic states at different nuclear geometries using a biorthogonal basis and (ii) we employ a unified protocol for choosing the correct phase for each adiabat at each geometry. For many-electron systems, the combination of these techniques significantly reduces the computational cost of integrating the electronic Schrodinger equation and imposes minimal overhead on top of the underlying electronic structure calculation. As a demonstration, we calculate the electronic excited-state dynamics for a hydrogen molecule scattering off a silver metal cluster, focusing on high-lying excited states, where many electrons can be excited collectively and crossings are plentiful. Interestingly, we find that the high-lying, plasmon-like collective excitation spectrum changes with nuclear dynamics, highlighting the need to simulate non-adiabatic nuclear dynamics and plasmonic excitations simultaneously. In the future, the combination of methods presented here should help theorists build a mechanistic understanding of plasmon-assisted charge transfer and excitation energy relaxation processes near a nanoparticle or metal surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Variational Discrete Action Theory

In this work, we propose the variational discrete action theory (VDAT) to study the ground state properties of quantum many-body Hamiltonians. VDAT is a variational theory based on the sequential product density matrix (SPD) ansatz, characterized by an integer $\mathscr{N}$, which monotonically approaches the exact solution with increasing $\mathscr{N}$. To evaluate the SPD, we introduce a discrete action and a corresponding integer time Green’s function. We use VDAT to exactly evaluate the SPD in two canonical models of interacting electrons: the Anderson impurity model and the d = ∞ Hubbard model. For the latter, we evaluate $\mathscr{N}$ = 2 – 4, where $\mathscr{N}$ = 2 recovers the Gutzwiller approximation (GA), and we show that $\mathscr{N}$ = 3, which exactly evaluates the Gutzwiller-Baeriswyl wave function, provides a truly minimal yet precise description of Mott physics with a cost similar to that of the GA. VDAT is a flexible theory for studying quantum Hamiltonians, competing both with state-of-the-art methods and simple, efficient approaches all within a single framework.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Phased: Phase-Aware Submodularity-Based Energy Disaggregation

Energy disaggregation is the task of discerning the energy consumption of individual appliances from aggregated measurements, which holds promise for understanding and reducing energy usage. In this paper, we propose PHASED, an optimization approach for energy disaggregation that has two key features: PHASED (i) exploits the structure of power distribution systems to make use of readily available measurements that are neglected by existing methods, and (ii) poses the problem as a minimization of a difference of sub-modular functions. We leverage this form by applying a discrete optimization variant of the majorization-minimization algorithm to iteratively minimize a sequence of global upper bounds of the cost function to obtain high-quality approximate solutions. PHASED improves the disaggregation accuracy of state-of-the-art models by up to 61% and achieves better prediction on heavy load appliances.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION↗
DOE OSTI2022

A review on the application of machine learning for combustion in power generation applications

Abstract Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.

Engineering↗
DOE OSTI2021

Performance and flexibility improvements of Staged Pressurized Oxy-Combustion

Staged Pressurized Oxy-Combustion (SPOC) is a low-carbon coal combustion power technology being developed by Washington University in St. Louis (WUSTL). Oxy-combustion plants enable straightforward capture of carbon dioxide (CO 2 ) by removing most of the nitrogen in the combustion air prior to use, thereby burning fuel in near-pure oxygen instead of air, producing a flue gas containing primarily CO 2 and water. CO 2 capture at amounts > 90% is possible, often using cryogenic air separation. Oxy-combustion typically relies on flue gas recycle (FGR) to reduce the peak temperature and radiation that would otherwise occur in a fuel/oxygen only flame. SPOC reduces the peak temperatures of combustion by utilizing two or more pressurized boiler modules connected in series to produce fuel staging; hence, only a portion of the fuel is combusted in any given furnace module. This means that the thermal energy released at each stage can be captured and removed from the gases prior to subsequent stages, when more fuel is introduced. This allows the SPOC process to operate with minimal FGR, avoiding the associated efficiency losses and additional costs. Also, the process operates at an elevated gas-side pressure, reducing boiler size, enhancing heat transfer to achieve a compact boiler configuration as compared to an atmospheric-pressure boiler design, and allowing for recovery of the latent heat of the water from the flue gas at a temperature useful to the steam cycle. The resultant net efficiency of the system is over 3 percentage points greater than traditional atmospheric-pressure oxy-combustion, and 7 percentage points greater than the post combustion variant, representing a step-change improvement over first-generation capture technologies. To further develop the concept, WUSTL and the Electric Power Research Institute, Inc., organized a project with American Air Liquide, Inc., Doosan Babcock Limited, and the U.S. Department of Energy to investigate a practicable and workable boiler design. The team has identified the potential for enhanced process flexibility for controlling power generation over a wider load range than is normally available to conventional coal-fired power plants due to the staged nature of the heat release. With increasing intermittent renewable generator contribution, on-demand generators need to be highly flexible to participate in the future energy market, requiring extensive operation at reduced load. Conventional coal-fired steam generators typically face challenges in maintaining temperature control of the reheat steam and main steam at reduced loads. This results in inefficient operation, both in terms of the boiler efficiency and steam turbine heat rate. The results of this project show the SPOC process is capable of exceptional turndown, both on a stage basis and with the ability to bypass entire stages. Oxygen-supply flexibility was also investigated, as this is also a key consideration for the overall flexibility of the SPOC process given the operating constraints of conventional air separation units. A boiler design concept assessment was conducted and was focused on delivering compact and constructible design. The assessment checked appropriate tube operating metal temperatures at full load and at lower operating loads, balanced against the needs of efficient coal combustion, and the resultant slagging and ash environments. Combustion testing in the 100-kWth pressurized combustion test rig at WUSTL was carried out to validate the combustion, heat flux profiles and burnout at multiple loads. Combustion parameters investigated were flame stability, fuel burnout, ash composition, radiative heat flux, and temperature profiles. The results of these tests formed the basis of a full-scale boiler design that will encompass improvements in both efficiency and flexibility over conventional oxy-combustion processes. The air separation unit flexibility was investigated, and associated cost implications were addressed. Detailed economic assessment results for a 550 MWe net power block are also provided, allowing for a comparison against the baseline NETL oxy-combustion and post combustion capture cases.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2021

U.S.-China Clean Energy Research Center Building Energy Efficiency (CERC-BEE) Open-Source Retrofit Targeting Tool (CRADA FP00007338 Final Report)

To increase the cost-saving energy and carbon dioxide (CO 2 ) emissions reductions in buildings and portfolios at the scale and speed necessary to limit climate change, researchers at LBNL and Johnson Controls (JCI) developed the Building Efficiency Targeting Tool for Energy Retrofits (BETTER). BETTER is a software tool that consists of three components: (1) the BETTER analytical engine source code (which was developed with intellectual property provided by JCI under CRADA FP00007338); (2) the BETTER web application, developed by LBNL and McQuillen Interactive Pty. Ltd; and (3) the BETTER application programming interface (API), also developed by LBNL and McQuillen Interactive Pty. Ltd. BETTER enables building and portfolio owners, managers, and service providers worldwide to quickly, easily identify cost-saving energy efficiency retrofits in existing buildings and portfolios without expensive site visits or complex modeling. With minimal data input, the tool benchmarks a building’s electric and fossil energy usage against peers; quantifies energy, cost and greenhouse gas (GHG) emission reduction potentials at the building and portfolio levels; and recommends energy efficiency measures to decarbonize and electrify buildings and portfolios, targeting specific energy savings levels. No other tool so comprehensively analyzes buildings and portfolios with such ease. If fully implemented, it is estimated that BETTER could help reduce emissions equivalent to planting 1.3 billion trees globally by 2030. Moreover, an additional 50-75% of embodied GHG emissions could be avoided in each case where BETTER results in a building being retrofitted instead of demolished and replaced, providing substantial additional decarbonization benefits for the buildings sector. BETTER has garnered multiple awards and avid interest from investors. In 2020, it earned a R&D 100 Award for innovation and a LBNL Director’s Award for Technology Transfer. In 2021, BETTER was named an EarthX E-Capital Summit Climate Tech Prize semi-finalist

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis

The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Hybrid AI-ML and FE-based Digital Twin Predictive Modeling Framework for a PWR Coolant System Components: Updates on Multi-Time-Series-3D-Location Dependent Usages Factor Prediction

The long-term operation (LTO) of nuclear power plants (NPP) beyond their original design life of 40 years can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition, the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Requiring frequent NDE inspections may lead to frequent NPP shutdowns which can lead to power outages and additional NDE inspection cost-related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing a more digital approach such as using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. The DT framework is based on limited experimental data, Artificial-intelligence (AI)-Machine-Learning (ML) and multiphysics-computationalmechanics such as finite element- (FE) based models. Given the real-time thermal-hydraulic process measurements from several existing plant sensors, the overall goal of the DT framework is to predict the cumulative usages factors or equivalent fatigue lives in real time and at any random 3D location of the components. This includes inaccessible locations such as inside the thickness or location of a component. This prediction can be at thousands to millions of 3D point clouds or locations like conventional FE-based models, but without running an FE model in real time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Processes in Salt Repositories for Radioactive Waste Disposal

This document summarizes the key processes (thermal, hydrological, mechanical, and chemical; THMC) impacting the features of a deep geological repository for radioactive waste in salt. Some processes are natural and on-going whether the repository is there or not, and other processes are driven by the perturbation associated with the repository. The features considered here include both engineered and natural components of the repository system. The engineered barrier system (EBS) in a salt repository is quite different from those implemented for a repository in clay or crystalline rocks, because it is comprised mostly of granular salt and salt-compatible cements, rather than bentonite. When compared to other rocks (i.e., silicates), salt has unique properties that make it an excellent potential host rock. Openings and fractures in salt creep closed readily. Salt has high thermal conductivity, which can reduce peak temperatures. Additionally, far away from the excavations the porosity of salt is unconnected, which leads to essentially zero advective or diffusive transport. The small amount of hypersaline brine occurring in salt minimizes microbial activity, reduces colloid-assisted transport, and eliminates in-package criticality (i.e., chloride is a neutron poison). At the end of the report, we present a brief outline for a potential salt repository, including considerations avoided in previous repository disposal concepts. We propose considering higher-temperature processes in future disposal concepts, rather than trying to minimize the thermal perturbation of the repository. Since hot salt is drier, a dry repository would limit corrosion, gas generation, and solute transport. Openings and fractures creep shut faster in hot salt. Therefore, higher temperatures could be seen as beneficial, rather than something to minimize, through increased spacing between waste packages (increasing repository costs).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

A Synthetic Pathway for Producing Carbon Dots for Detecting Iron Ions Using a Fiber Optic Spectrometer

Iron detection is of growing importance in the critical minerals sector, where unwanted iron ions are typically removed during the processing of target critical metals. The ideal sensor should utilize inexpensive, scalable materials along with a low-cost, robust, and easy-to-use analysis platform. Here, we demonstrate a simple acid–base synthesis of luminescent iron-responsive carbon dots by reacting ethanolamine, phosphoric acid, and m-phenylenediamine. The carbon dots exhibit selective, iron-specific emission quenching, with the ability to detect part-per-billion levels of iron ions even in 0.1 M HCl. After benchmarking the purified materials using a commercial spectrometer, a “low-cost” process is demonstrated in which carbon dots with minimal purification are coupled with a portable fiber-optic spectrometer for analyzing iron content. Carbon dot-coated paper strips are also evaluated as another convenient platform for iron analysis. Taken together, the sensing material and platforms demonstrated here are well-suited for detecting trace quantities of iron in environmentally relevant conditions, with potential applications in tracking iron removal processes during critical mineral production as one exciting area of interest.

acid mine draining↗
DOE OSTI2021

A Computationally Efficient Algorithm for Computing Convex Hull Prices

Electricity markets worldwide allow participants to bid non-convex production offers. While non-convex offers can more accurately reflect a resource's capabilities, they create challenges for market clearing processes. For example, system operators may execute side payments when a participant’s cost is not covered through energy sale settlements from locational marginal pricing schemes, or when a participant incurs lost opportunity costs to follow the dispatch signal. Convex hull pricing minimizes these and other types of side payments while providing uniform (i.e., locationally and temporally consistent) prices. However, computing convex hull prices involves solving either a large-scale linear program - which in turn requires explicit descriptions of market participants’ convex hulls -or the Lagrangian dual of the corresponding non-convex scheduling problem. Here, we propose a computationally feasible and industrially scalable Benders decomposition approach to computing convex hull prices at least an order of magnitude faster than the current state-of-the-art while leveraging recent advances in convex hull formulations for thermal generating units.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Wind Beneath Your Wings

Advances in wind turbine technology and the need for renewable energy resources drive the rapid expansion of the wind industry in the Unites States and abroad. In 2019, wind energy overtook hydropower as the most-used source of renewable energy generation in the country, producing 300 million megawatt-hours of electricity or roughly 6% of the nation's total power output. Continuing this rapid expansion while also protecting wildlife requires more efficient and cost-effective wind-energy-wildlife impact monitoring and minimization technologies. The National Renewable Energy Laboratory's (NREL's) wind energy program offers unparalleled expertise in environmental science and wildlife impact mitigation, helping project partners develop and validate their ideas.

Altamont Pass↗
DOE OSTI2022

Gaseous hydrogen storage system with cryogenic supply

A gaseous hydrogen storage and distribution system with a cryogenic supply and a method for the cryogenic conversion of liquid hydrogen into high-pressure gaseous hydrogen are provided. The gaseous hydrogen storage and distribution system includes pressuring liquid hydrogen from a cryogenic tank using a low pressure liquid pump before vaporization within a relatively small vaporizer. The resulting high pressure gaseous hydrogen is transferred to a plurality of storage tanks at ambient temperature according to a desired fill sequence. The high pressure hydrogen gas is subsequently distributed from the storage tanks through a hydrogen fueling dispenser according to a desired dispensing sequence. The present system and method provide improvements in operational safety, eliminates the use of high pressure gas compressor, and minimizes boiling off and ventilation losses at a reduced cost when compared to existing thermal compression storage systems.

Feng, Zhili↗
DOE OSTI2020

Non-destructive Material Characterization of Radioactive Waste Packages with QUANTOM{sup R} - 20130

During the last decades, the nuclear and non-nuclear industry has produced a considerable amount of low and intermediate level radioactive wastes. In many countries, it is foreseen to dispose of these waste packages safely in a final repository under strict waste acceptance requirements (e.g. the radiological and material characterization) defined by national licensing and supervisory authorities. The material characterization of waste packages can be performed on the basis of existing documentation or, if the documentation is insufficient, on further destructive or non-destructive analysis. Non-destructive methods are to be preferred to minimize radiation exposures of operating personnel as well as costs. We offer here an innovative non-destructive technology called QUANTOM{sup R} (Quantitative Analysis of Toxic and nontoxic Materials) based on prompt and delayed gamma neutron activation analysis (P and DGNAA). This technology is able to identify, quantify and thus verify the amount of hazardous and non-hazardous substances in 200-l radioactive drums, which is required for a final disposal characterization. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Photovoltaics Based on Direct and Indirect Gap Semiconductors

Direct and thin, or indirect and thick absorbers can yield cells close to 30% but there are practical limits due to defects, cost, reliability. Tandems and new ideas are needed to minimize thermalization and entropy losses and to enable high absorptance in thin Si layers. Direct-gap absorbers have ~1000x higher recombination losses than Si, but being 100x thinner, the limiting 1-sun efficiencies are similar due to 100x higher G.

direct-gap↗
DOE OSTI2020

Multi-functional anodes boost the transient power and durability of proton exchange membrane fuel cells

Abstract Proton exchange membrane fuel cells have been regarded as the most promising candidate for fuel cell vehicles and tools. Their broader adaption, however, has been impeded by cost and lifetime. By integrating a thin layer of tungsten oxide within the anode, which serves as a rapid-response hydrogen reservoir, oxygen scavenger, sensor for power demand, and regulator for hydrogen-disassociation reaction, we herein report proton exchange membrane fuel cells with significantly enhanced power performance for transient operation and low humidified conditions, as well as improved durability against adverse operating conditions. Meanwhile, the enhanced power performance minimizes the use of auxiliary energy-storage systems and reduces costs. Scale fabrication of such devices can be readily achieved based on the current fabrication techniques with negligible extra expense. This work provides proton exchange membrane fuel cells with enhanced power performance, improved durability, prolonged lifetime, and reduced cost for automotive and other applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗