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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Energy Systems Integration Facility (ESIF): World-Class Systems Integration Capabilities and Research

The Energy Systems Integration Facility (ESIF), located at the National Renewable Energy Laboratory (NREL) South Table Mountain campus, is a world-renowned user facility for research and development of modern, advanced, and clean energy technologies. ESIF is distinguished by its continuously evolving, highly integrated systems that span throughout the building, connecting research capabilities across multiple laboratories and test areas. The primary ESIF research systems include: [1] data, cyber, and control networks, [2] research electrical distribution buses (REDB), [3] thermal integration infrastructure, and [4] hydrogen systems. The data, cyber, and control networks provide monitoring, control, communication, automation, visualization, and time series data storage and tagging capabilities for research projects and ESIF systems, including facility safety functions. The REDB system consists of four dedicated AC and DC electrical power networks that can connect devices located across the facility through versatile, automatic circuit configuration to support complex power electronics experiments up to the megawatt-scale. The thermal integration infrastructure consists of three temperature-conditioned water loops that provide heating and cooling interfaces and capabilities for thermal energy research. The hydrogen systems provide megawatt-scale hydrogen production, drying, compression, high-pressure storage, and delivery to laboratory end uses, including hydrogen fuel cell vehicle fueling. The ESIF research systems interconnect and extend throughout the various lab areas of the facility to create elaborate networks composed of diverse technologies for cutting-edge research. The ESIF capabilities are operated and stewarded by the ESIF Research Operations group, who also actively upgrade and advance the systems to ensure they remain ahead of anticipated research - enabling the success of many pioneering energy integration projects. The poster, created by members of the ESIF Research Operations team, highlights and summarizes the four core integrated systems at ESIF. The poster was first presented at the internal NREL Energize Forum on May 13th, 2024, and received the "Best Poster" award.

capabilities↗
DOE OSTI2022

WORM (Write One, Read Many)

WORM (Write One, Run Many) is an easy to use, cross platform, embedded and extensible, functional programming language designed to facilitate the creation of input-decks for computer codes that use standard ASCII text files for input. WORM makes it easy to create generic (yet, complex and powerful) reusable models. Additionally its nature allows for complex calculations and routines to be coded once and easily reused, further simplifying the creation of input decks. WORM (Write One, Run Many) is a powerful and versatile tool designed to improve the efficiency of today’s criticality safety analyst by allowing: + input decks for parametric studies to be created quickly and easily, + calculations and variables to be imbedded into any input deck, thus allowing for meaningful parameter specifications, + problems to be specified using any combination of units, and + complex mathematically defined models to be created. A very simple syntax is employed, and therefore the WORM is easy to learn. A WORM model is essentially a standard input deck with some of its numerical values replaced by WORM code. WORM code may include and evaluate the following mathematical operators and functions: addition, subtraction, multiplication, division, exponentiation, modulus, sine, cosine, tangent, arcsine, arccosine, arctangent, the natural logarithm, logarithm base 10, integer truncation, absolute value, and random number. Several common constants, e.g., pi, e, and Avogadros’s Number (both as 6.022e23 and 0.6022), are predefined in WORM. Additionally, many unit conversion factors are also predefined: millimeters, meters, inches, feet, yards, and mils to centimeters; kilograms, pounds, and ounces to grams; liters, milliliters, gallons, and fluid ounces to cubic centimeters; and angular degrees to radians. For parametric studies, WORM supports various shorthand list specifications: the explicit step size, linear interpolation, and logarithmic interpolation. The list notation sequentially assigns multiple values to a name. WORM creates an input deck for each value of the name. If multiple lists are used, WORM steps through each list individually, i.e., WORM creates input decks corresponding to each and every permutation of the list values. Additionally, a library of standard material definitions and Perl subroutines are included. Any one of these files can be incorporated into the subject model with a simple WORM read command. WORM is completely written in Perl, the Practical Extraction and Reporting Language. Perl is one of the most portable programming languages available today. As such, the WORM works on practically any computer platform.

Sartor, Raymond↗
DOE OSTI2026

Performance of the Pair Spectrometer in Hall D at Jefferson Lab

This article describes the performance of the pair spectrometer installed in experimental Hall D at Jefferson Lab and its successfully operation in multiple experiments with the GlueX detector. The primary purpose of the pair spectrometer is the precise determination of the flux of beam photons incident on the GlueX target, a critical input for physics analyses such as absolute cross-section measurements. The photon energy spectrum is determined by reconstructing electron–positron pairs produced in a thin converter inserted into the photon beam. The spectrometer is integrated into the GlueX trigger system, whose electronics enable continuous real-time monitoring of the photon flux and the recording of e+e- pair candidates for offline analysis. In addition, the pair spectrometer provides a versatile test facility for evaluating calorimeter prototypes using leptons with well-defined energies produced via electromagnetic pair production.

Somov, Alexander [Jefferson Lab]↗
DOE OSTI2025

Thermally Evaporated Naphthalene Diimides as Electron Transport Layers for Perovskite Solar Cells

Thermally evaporated organic electron transport layers (ETLs) have the potential to enable high-performance and scalable perovskite solar cells (PSCs). Among these, naphthalene diimide (NDI)-based ETLs are a promising family of materials that exhibit the optoelectronic properties, ambient stability and versatility required of high-performance ETLs. Here, we synthesized five NDI derivatives with varying functional groups and identified the two most promising candidates for evaluating the impact of molecular structure on processability via thermal evaporation. While phosphonic acid functionalization was shown to introduce thermal instability, leading to chemical changes during evaporation, NDI-bis N-phenyl-bromide (NDI-(PhBr) 2 ) emerged as a promising ETL candidate. NDI-(PhBr) 2 demonstrated excellent compatibility with the thermal evaporation process and enabled PSCs with power conversion efficiencies (PCEs) of 15.6%, surpassing all previously reported PSCs containing thermally evaporated NDI ETLs. Furthermore, NDI-(PhBr) 2 exhibited excellent operational stability, retaining 75% of the initial PCE after 150 h of operation under continuous illumination at 65 °C. These results highlight the potential of NDI-based ETLs for advancing the scalability and performance of PSCs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Functionalized carbon nanotubes for thermionic emission and cooling applications

Barium strontium oxide-coated carbon nanotubes (CNTs) were implemented as a work function lowering and field enhancing functional coating on a coiled tungsten filament to create a new thermionic cathode. This cathode resembles conventional oxide cathodes in structure. It has the same coiled tungsten filament as a conventional oxide cathode but uses barium strontium oxide-coated CNTs instead of the traditional barium strontium calcium oxide powder mixture as an emissive coating. The cathode produces a strong thermionic emission. At 1395 K and 2.5 V/μm, the thermionic emission current of 0.87 A or current density of 2.9 A/cm 2 was obtained from this oxide-coated CNT cathode. This level of emission is about three times as large as a conventional oxide cathode operating at similar temperature and field strength. Strong thermionic emissions from the cathode also lead to a large thermionic cooling effect. Temperature reduction as large as 90° was observed from the cathode surface when it was emitting electrons. Strong thermionic emission and a large cooling effect obtained are the result of the combination of the low work function of barium strontium oxide (1.6 eV) and the large field effect induced by the CNTs. Plasma enhanced chemical vapor deposition was used to grow CNTs, and magnetron sputtering deposition was used to deposit the barium strontium oxide functional coating; details of the cathode fabrication are presented to illustrate both the versatility of the processing techniques and the adaptability of barium strontium oxide-coated CNTs as a functional coating. Finally, measurements on thermionic emission and thermionic cooling of the cathode are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Machine-learning-based spectral methods for partial differential equations

Spectral methods are an important part of scientific computing’s arsenal for solving partial differential equations (PDEs). However, their applicability and effectiveness depend crucially on the choice of basis functions used to expand the solution of a PDE. The last decade has seen the emergence of deep learning as a strong contender in providing efficient representations of complex functions. In the current work, we present an approach for combining deep neural networks with spectral methods to solve PDEs. In particular, we use a deep learning technique known as the Deep Operator Network (DeepONet) to identify candidate functions on which to expand the solution of PDEs. We have devised an approach that uses the candidate functions provided by the DeepONet as a starting point to construct a set of functions that have the following properties: (1) they constitute a basis, (2) they are orthonormal, and (3) they are hierarchical, i.e., akin to Fourier series or orthogonal polynomials. We have exploited the favorable properties of our custom-made basis functions to both study their approximation capability and use them to expand the solution of linear and nonlinear time-dependent PDEs. The proposed approach advances the state of the art and versatility of spectral methods and, more generally, promotes the synergy between traditional scientific computing and machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hierarchically structured metal carbides as conductive fillers in thermo-responsive polymer nanocomposites for battery safety

Integrating thermo-responsive polymer switching materials (TRPS) into lithium-ion batteries (LIBs) has been recognized as one of the most effective strategies to prevent thermal runaway under various abuse scenarios. However, the current methods to obtain TRPS cannot satisfy different practical applications. Herein, we develop a versatile strategy for the preparation of various metal carbides (e.g., tungsten carbide, molybdenum carbide) with controllable hierarchical structure that is featured with surface protrusion structure, which is critical for high conductivity and rapid thermal response. Systematic studies of the phase and morphology evolutions by advanced characterizations illustrate that the reducing agent and reduction rate are critical for developing the specific morphology. In conclusion, by using the above-mentioned carbides as the conductive fillers of TRPS, the resulting TRPS with a specially controlled shape exhibits over 5 order of conductivity improvement compared with common carbides with particulate morphology, in addition to reversible shutdown performance and effective thermal abuse protections toward safe LIB operation.

25 ENERGY STORAGE↗
DOE OSTI2022

1.2.2.405 HydroWIRES Topic D1: Capacity Expansion Model (CEM) Enhancements

Long-term grid planning tools have difficulty representing detailed hydropower operating characteristics, which depend not only on technological specifications but also on water management practices and regulations. As a result, the value of hydropower is incompletely characterized, and the potential role of hydropower in the performance and resiliency of the future electric grid is not fully understood. This work will fill that gap by developing new ways to represent hydropower resource, technology, and operational characteristics in electric sector capacity expansion models and implementing them in the open-source version of the National Renewable Energy Laboratory's Regional Energy Deployment System (ReEDS) model. ReEDS is a well-established national scale grid planning tool used since 2003 by the U.S. Department of Energy and others to explore the evolution of the U.S. electric sector. Improvements will include a comprehensive national resource assessment for pumped storage hydropower and methods for modeling multiple hydropower technology categories characterized by technical, regulatory, and economic characteristics. The project will provide guiding principles and strategies for improving hydropower modeling in capacity expansion models and deliver a first-of-its kind versatile PSH dataset. All data, code, and methods will be publicly available, allowing the industry to better identify the value of hydropower in the future electricity system and make more informed planning decisions.

capacity expansion↗
DOE OSTI2023

High-Fidelity Energy Deposition Ignition Model Coupled with Flame Propagation Models at Engine-like Flow Conditions

With the heightened pressure on car manufacturers to increase the efficiency and reduce the carbon emissions of their fleets, more challenging engine operation has become a viable option. Highly dilute, boosted, and stratified charge, among others, promise engine efficiency gains and emissions reductions. At such demanding engine conditions, the spark-ignition process is a key factor for the flame initiation propagation and the combustion event. From a computational standpoint, there exist multiple spark-ignition models that perform well under conventional conditions but are not truly predictive under strenuous engine operation modes, where the underlying physics needs to be expanded. In this paper, a hybrid Lagrangian-Eulerian spark-ignition (LESI) model is coupled with different turbulence models, grid sizes, and combustion models. The ignition model, previously developed, relies on coupling Eulerian energy deposition with a Lagrangian particle evolution of the spark channel, at every time-step. The spark channel is attached to the electrodes and allowed to elongate at a speed derived from the flow velocity. The LESI model is used to simulate spark ignition in a nonquiescent crossflow environment at engine-like conditions, using converge commercial computational fluid dynamics (CFD) solver. The results highlight the consistency, robustness, and versatility of the model in a range of engine-like setups, from typical with Reynolds-averaged Navier-Stokes (RANS) and a larger grid size to high fidelity with large-eddy simulation (LES) and a finer grid size. The flame kernel growth is then evaluated against Schlieren images from an optical constant volume ignition chamber with a focus on the performance of flame propagation models, such as G-equation and thickened flame model, versus the baseline well-stirred reactor model. Finally, future development details are discussed.

Advanced ignition modeling↗
DOE OSTI2020

Structures, Systems, and Components Classification Criteria for the Versatile Test Reactor

The Versatile Test Reactor (VTR) is a fast spectrum test reactor currently being developed in the United States under the direction of the US Department of Energy (DOE), Office of Nuclear Energy. The VTR mission is to enable accelerated testing of advanced reactor fuels and materials required for advanced reactor technologies. The conceptual design of the 300 MWth sodium-cooled metallic-fueled pool-type fast reactor has been led by the U.S. National Laboratories in collaboration with General Electric-Hitachi and Bechtel National Inc. As part of the VTR authorization process by the DOE, it is necessary to identify the safety classification of structures, systems, and components (SSCs) to determine the proper design, surveillance, operation, and incident-reporting requirements. Since VTR is utilizing a risk-informed, performance-based authorization process based on the Licensing Modernization Project (LMP) described in NEI 18-04 [1], the criteria for SSC classification, which are reviewed here, includes both risk information from the probabilistic risk assessment (PRA) and prescriptive requirements.

Grabaskas, David↗
DOE OSTI2023

SATS Enhanced Capabilities and Demonstration of Improved Ramp Rates for In-Cell Testing

This report presents the development of the second-generation Severe Accident Test Station, referred to as SATS 2.0. SATS 2.0 represents a major advancement in addressing critical research needs for the United States nuclear industry, particularly in relation to fuel fragmentation, relocation, and dispersal (FFRD) concerns associated with the nuclear industry’s goal to extend fuel burnup beyond a peak rod average of 62 GWd/tU. The central objective of SATS 2.0 was the deployment a new furnace capable of achieving heating rates up to 100°C/s. This achievement addresses critical capability gaps, enabling assessment of prioritized research objectives established by the Electric Power Research Institute’s (EPRI) Collaborative Research on Advanced Fuel Technologies (CRAFT) working group. These research objectives are directly related to high-burnup loss-of-coolant accident (LOCA) conditions and the effects of prolonged exposure to elevated temperatures, both of which are crucial to enhancing nuclear safety and efficiency. The SATS 2.0 system builds upon prior experience with the original SATS system, which focused on evaluating accident-tolerant fuel (ATF) cladding concepts during accident conditions. However, the new system not only expands upon prior core capabilities by achieving higher heating rates with a better furnace but also plans to incorporate novel auxiliary systems to create a versatile platform for addressing current research needs. For example, a system was developed for the quantification and characterization of fission gas released during high-temperature transients. Additionally, in situ measurement capabilities were developed, such as digital image correlation which enabled the real-time capture of strain related to balloon and burst events and fiber-optic sensors that allowed for high-fidelity characterization of temperature gradients. Demonstration tests of the new 12-lamp furnace achieved heating rates up to 120°C/s. Notably, SATS 2.0 demonstrated its capacity to simulate LOCA burst tests at different pressures, yielding burst data that align with historical empirical models. Moreover, the system exhibited its capability to simulate complex conditions observed in anticipated operational occurrences, while effectively mitigating temperature overshoots. These accomplishments mark significant progress toward overcoming FFRD challenges and advancing the United States nuclear industry's safety basis and technical capabilities for extended burnup.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Mesoporous Thin Film Architectures: Addressing Material Demands through Molecular Self-Assembly

Mesoporous thin films spark interest across a wide range of disciplines due to their tunable nanostructures, large internal surface areas, and strong compatibility with planar optical, electronic, and microfluidic devices. While attention in the porous materials community has shifted toward macroporous or disordered nanoporous systems, a resurgence in mesoporous thin film research is underway, driven by new molecular self-assembly methods, advanced materials chemistry, and improved characterization techniques. The integration of high-χN block copolymer design, kinetically persistent micelle templating, and postdeposition processing protocols now allows control over structural parameters such as pore size, wall thickness, porosity, and connectivity. These advances have overcome many of the thermodynamic and processing constraints that previously limited widespread adoption. Rather than serving only as high-surface-area supports, mesoporous thin films are engineered as active interfaces where responsive chemistries and nanoscale confinement act in tandem. Embedding switchable ligands, thermoresponsive polymers, redox mediators, or ion-selective groups directly within the pore walls enables real-time control over transport, optical, and electrochemical properties. These capabilities open up new directions in adaptive coatings, gated membranes, and fast-response biosensors. To further expand their functional scope, mesoporous films are integrated into hierarchical and multicomponent architectures. Techniques such as triblock terpolymer templating, crack-directed assembly, and nanoimprint lithography allow for control over spatial organization on the micron and submicron scale and pore system orientation. This enables programmable anisotropy, enhanced molecular diffusion, and wavelength-selective photonic behavior, essential for next-generation sensing, catalysis, and energy applications. Such structural and functional complexity requires equally sophisticated characterization. Multimodal and in situ techniques can track material dynamics under operational conditions. Recent progress includes extended-range ellipsometric porosimetry (EP) for hierarchical architectures, vacuum EP for interface energetics, time-resolved EP for diffusion kinetics, and correlative AFM-SAXS mapping. The introduction of advanced neutron-based spectroscopies, particularly quasielastic neutron scattering (QENS), promises to provide real-time access to ion transport dynamics and segmental motion under nanoscale confinement, offering a path toward deeper mechanistic understanding of structure-performance correlations in mesoporous systems. This Account reflects the technical advances made and the interdisciplinary collaborations that have shaped our collective vision. The particular dimensions of mesopores enable us to subtly tune interactions at the molecular, interfacial, and mesoscopic levels that permit us to harness nanoconfinement. What emerges is a versatile, modular platform capable of chemical gating, energy transduction, and sensing with a level of tunability unmatched by other porous materials. We highlight critical challenges including the need for more robust large-area processing, a deeper understanding of dynamic behavior under cycling, and better integration with device-level architectures. Our strategies support the transition of mesoporous thin films into active high-performance components in next-generation energy, environmental, and biomedical systems.

oxides↗
DOE OSTI2021

Colloidal quantum dot lasers

We report semiconductor nanocrystals represent a promising class of solution-processable optical-gain media that can be manipulated via inexpensive, easily scalable colloidal techniques. Due to their extremely small sizes (typically <10 nm), their properties can be directly controlled via effects of quantum confinement; therefore, they are often termed colloidal quantum dots (CQDs). In addition to size-tunable emission wavelengths, CQDs offer other benefits for lasing applications, including low optical-gain thresholds and high temperature stability of lasing characteristics. Recent progress in understanding and practical control of processes impeding light amplification in CQDs has resulted in several breakthroughs, including the demonstration of optically pumped continuous-wave lasing, the realization of optical gain with direct current electrical injection and the development of dual-function electroluminescent devices that also operate as optically pumped lasers. The purpose of this Review is to assess the status of the field of CQD lasing and discuss the existing challenges and opportunities. A particular focus is on approaches for suppressing nonradiative Auger recombination, novel optical-gain concepts enabled by strong exciton-exciton interactions and controlled CQD charging, effects of nanocrystal form factors on light amplification and practical architectures for realizing electrically pumped CQD lasers. This overview suggests that the accumulated knowledge, along with the approaches developed for manipulating the optical-gain properties of colloidal nanostructures, perfectly position the CQD field for successfully addressing a long-standing challenge: the realization of CQD-based laser diodes. Colloidal quantum dots are promising materials for realizing versatile, wavelength-tunable, solution-processed lasers. This Review surveys recent advances in colloidal quantum dot lasing, provides an in-depth analysis of outstanding challenges and discusses a path forward to implementing technologically viable lasing devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Materials Engineering for High Performance and Durability Proton Exchange Membrane Water Electrolyzers

Proton exchange membrane water electrolyzers (PEMWEs) are expected to play a crucial role in the global green energy transition during the 21st century. They provide a versatile and sustainable solution for generating hydrogen with very high purity in combination with renewable energies, such as solar and wind. Despite their promise, PEMWEs face several critical problems, including high costs, performance limitations, and durability challenges, particularly at low iridium (Ir) loading on the anode. Advancing next-generation PEMWEs requires extensive work on materials engineering of all cell components, including the catalyst layer (CL), membrane, porous transport layer (PTL), bipolar plate (BPP), and gasket. This task must be performed with the complementary contribution of different modeling and characterization techniques. This review presents a critical perspective from academia, research centers, and industry, mapping main developments, remaining gaps, and strategic pathways to advance PEMWE technology. A focus is devoted to key aspects, such as operation at low Ir loading, membrane durability, multiscale transport layers, porous and non-porous flow fields, multiphysics modeling, and multipurpose characterization techniques, which are thoroughly discussed. By unifying these topics, this review provides readers with the essential knowledge to grasp current developments and tackle tomorrow's challenges in PEMWE engineering.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Using GANs to predict milling stability from limited data

Milling is a key manufacturing process that requires the selection of operating parameters that provide efficient performance. However, the presence of chatter, a self-excited vibration causing poor surface finish and potential damage to the machine and cutting tool, makes it challenging to select the appropriate parameters. To predict chatter, stability maps are commonly used, but their generation requires expensive data, making it difficult to employ these maps in industry. Therefore, there is a pressing need for an approach that can accurately predict stability maps using limited experimental data. This study introduces the new Encoder GAN (EGAN) approach based on Generative Adversarial Networks (GANs) that predicts stability maps using limited experimental data. The approach consists of the encoder, generator, and discriminator subnetworks and uses the trained encoder and generator to predict the target stability map. This versatile method can be applied to various tool setups and can accurately predict stability maps with limited experimental data (five to 10 cutting tests) even when there is little information available for unknown parameters. In conclusion, the study evaluates the proposed approach using both numerical data and experiments and demonstrates its superior performance compared to state-of-the-art benchmarks.

42 ENGINEERING↗
DOE OSTI2024

Integrating HPC, AI, and Workflows for Scientific Data Analysis: Report from Dagstuhl Seminar 23352

The Dagstuhl Seminar 23352, titled “Integrating HPC, AI, and Workflows for Scientific Data Analysis,” held from August 27 to September 1, 2023, was a significant event focusing on the synergy between High-Performance Computing (HPC), Artificial Intelligence (AI), and scientific workflow technologies. The seminar recognized that modern Big Data analysis in science rests on three pillars: workflow technologies for reproducibility and steering, AI and Machine Learning (ML) for versatile analysis, and HPC for handling large data sets. These elements, while crucial, have traditionally been researched separately, leading to gaps in their integration. The seminar aimed to bridge these gaps, acknowledging the challenges and opportunities at the intersection of these technologies. The event highlighted the complex interplay between HPC, workflows, and ML, noting how ML has increasingly been integrated into scientific workflows, thereby enhancing resource demands and bringing new requirements to HPC architectures, like support for GPUs and iterative computations. The seminar also addressed the challenges in adapting HPC for large-scale ML tasks, including in areas like deep learning, and the need for workflow systems to evolve to leverage ML in data analysis fully. Moreover, the seminar explored how ML could optimize scientific workflow systems and HPC operations, such as through improved scheduling and fault tolerance. A key focus was on identifying prestigious use cases of ML in HPC and understanding their unique, unmet requirements. The stochastic nature of ML and its impact on the reproducibility of data analysis on HPC systems was also a topic of discussion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Systematic Construction of Time-Dependent Hamiltonians for Microwave-Driven Josephson Circuits

Time-dependent electromagnetic drives are fundamental for controlling complex quantum systems, including superconducting Josephson circuits. In these devices, accurate time-dependent Hamiltonian models are imperative for predicting their dynamics and designing high-fidelity quantum operations. Existing numerical methods, such as black-box quantization (BBQ) and energy-participation ratio (EPR), excel at modeling the static Hamiltonians of Josephson circuits. However, these techniques do not fully capture the behavior of driven circuits stimulated by external microwave drives, nor do they include a generalized approach to account for the inevitable noise and dissipation that enter through microwave ports. Here, we introduce numerical techniques that leverage classical microwave simulations, efficiently executable in finite-element solvers, to obtain the time-dependent Hamiltonian of microwave-driven superconducting circuits with arbitrary geometries under charge, flux, or mixed electromagnetic modulation. Importantly, our techniques do not rely on a lumped-element description of the superconducting circuit, in contrast to previous approaches to tackling this problem. We demonstrate the versatility of our approach by characterizing the driven properties of realistic circuit devices in complex electromagnetic environments, including coherent dynamics due to charge and flux modulation, as well as drive-induced relaxation and dephasing. Our techniques offer a powerful toolbox for optimizing circuit designs and advancing practical applications in superconducting quantum computing.

Lu, Yao [Yale U.; Yale U. (main); Fermilab] (ORCID↗
DOE OSTI2025

Develop a Simulation Framework for Understanding Physico-Chemical Processes and Optimization of CHZ' Plastic Thermolyze Technology (CRADA Final Report)

Thermolyzer(TM) technology is a third-generation, multi-reactor, oxygen-free, low pressure, slow pyrolysis process. It has proven to be remarkably versatile in successfully processing all types of hydrocarbon waste. These include all seven types of plastics, tires, auto shredder residue, carpet, electronic waste, and composites. Working with the IACMI, Thermolyzer(TM) technology has recovered the glass and carbon fibers from wind turbine blades for reuse into new applications. Its primary output is a synthesis gas that is rich in H 2 , CH 4 , and small amounts of C 2-4 aliphatic hydrocarbons. Other components are CO and CO 2 . Three unique features set Thermolyzer(TM) apart from other pyrolysis systems: the syngas is clean enough to run in Siemens or Solar gas turbines or IC engines without fouling, clean, salable, byproducts are produced, and CO 2 emissions are lower than natural gas power plants. Research results have been obtained from an operating 7 ton/day facility. A 44 ton/day plant has successfully operated as noted above. The process works because it makes use of the recoverable embodied energy in the feedstock. Surprisingly, a pound of some plastics has the same recoverable energy content as a pound of gasoline. Thus, it is imperative to develop a process that economically converts scrap plastics (including ocean plastics) into energy and thereby conserve the non-renewable fossil fuels for future generations. The Thermolyzer(TM) technology can also be used to create liquid fuels like gasoline and diesel from hydrocarbon wastes. Because of the high hydrogen content of the synthesis gas, hydrogen can be recovered more inexpensively than the current solar or wind energy being used to electrolyze water. That hydrogen can be used for fuel cell powered vehicles or converted into ammonia for agriculture or as a hydrogen storage medium. Extension of this technology to other wastes such as tires, auto shredder residue or wood wastes would expand the circular economy.

36 MATERIALS SCIENCE↗