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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Methacrylated gelatin-embedded fabrication of 3D graphene-supported Co 3 O 4 nanoparticles for water splitting

In this work, we developed a general platform for the fabrication of transition metal oxide nanoparticles supported by a graphene foam (GF) by first coating it with a methacrylated gelatin (GelMA) hydrogel, which served as a 3D matrix for nanoparticle dispersion. The engineered GelMA/GF matrix was hydrophilic with good mechanical strength and high conductivity, therefore providing a good platform for the dispersion of a variety of metal/oxide precursors. Due to this platform, well-dispersed Co 3 O 4 nanoparticles with the smallest size of 3 nm assembled on the nitrogen-doped graphene foam (Co 3 O 4 /NGF). The crystalline transformation from a CoCl 2 [H 2 O] 2 precursor to Co 3 O 4 was revealed by in operando X-ray diffraction and absorption techniques. After applying Co 3 O 4 /NGF as a free-standing electrocatalyst for water splitting, the nanoparticles of size 3 nm exhibited optimal catalytic activity in alkaline media; the corresponding cell could promote water splitting at a current density of 10 mA cm -2 with only 1.63 V and exhibited excellent stability in a 25 h long-term operation. Our results demonstrate that the GelMA hydrogel-coated 3D graphene foam can be a promising platform for the design and fabrication of graphene-based multifunctional materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within the reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical applications. Learning Objectives: Learn the general methodologies associated with model-based 3D image reconstruction. Learn the potential advantages in image quality and dose associated with model-based image reconstruction. Learn the challenges associated with computational load and image quality assessment for such reconstruction methods. Learn how imaging task can be incorporated as a means to drive optimal image acquisition and reconstruction techniques. Learn how model-based reconstruction methods can incorporate prior information to improve image quality, ease sampling requirements, and reduce dose.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Metal–Organic Framework Nodes as a Supporting Platform for Tailoring the Activity of Metal Catalysts

Catalysis is a ubiquitous element of the modern economy, representing a cornerstone of many sectors including energy, materials, and pharmaceuticals. Within this realm, 80% of reactions are carried out by heterogeneous catalysts due to their advantageous physical and chemical characteristics. In some heterogeneous systems, the reaction rate can be optimized through rational catalyst design, in which support materials can be tailored structurally and stereoelectronically for targeted purposes to better facilitate the catalytic reaction. To this end, metal-organic frameworks (MOFs) have garnered recent attention due to their high porosity, crystallinity, and chemical tunability, which can be used to derive structure-activity relationships in the design of catalysts. In this Perspective, we survey examples of catalytically active guest metals on the inorganic nodes of MOFs. This is done with the intention of providing design guidelines for tailoring chemical reactivity and insights into the opportunities for future growth in this field. Strategies for rational design in MOF catalysis include electronic tuning of the metal node in the MOF support, installing promoter ions onto the node to effect the reactivity of grafted metals, modulation of the spatial environment around the transition metal using the metal node structure, and achieving site uniformity in supported catalysts. By highlighting these efforts, we seek to underscore the role of MOF nodes as nonspectator supports for catalytic metal complexes and provide future directions for rational catalyst design by tuning reactivity on these anchoring platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Microstructural Underpinnings of Giant Intrinsic Exchange Bias in Epitaxial NiCo 2 O 4 Thin Films

Understanding intrinsic exchange bias in nominally single-component ferromagnetic or ferrimagnetic materials is crucial for simplifying related device architectures. However, the mechanisms behind this phenomenon and its tunability remain elusive, which hinders the efforts to achieve unidirectional magnetization for widespread applications. Inspired by the high tunability of ferrimagnetic inverse spinel NiCo 2 O 4 , the origin of intrinsic exchange bias in NiCo 2 O 4 (111) films deposited on Al 2 O 3 (0001) substrates are investigated. The comprehensive characterizations, including electron diffraction, X-ray reflectometry and spectroscopy, and polarized neutron reflectometry, reveal that intrinsic exchange bias in NiCo 2 O 4 (111)/Al 2 O 3 (0001) arises from a reconstructed antiferromagnetic rock-salt Ni x Co 1-x O layer at the interface between the film and the substrate due to a significant structural mismatch. Remarkably, by engineering the interfacial structure under optimal growth conditions, it can achieve exchange bias larger than coercivity, leading to unidirectional magnetization. Such giant intrinsic exchange bias can be utilized for realistic device applications. This work establishes a new material platform based on NiCo 2 O 4 , an emergent spintronics material, to study tunable interfacial magnetic and spintronic properties.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Incorporating Heavy Alkanes in Metal–Organic Frameworks for Optimizing Adsorbed Natural Gas Capacity

Metal-Organic Frameworks (MOFs) as methane adsorbents are highly promising materials for applications such as methane-powered vehicles, flare gas capture, and field natural gas separation. Pre- and post-synthetic modification of MOFs have been known to help improve both the overall methane uptake as well as the working capacity. Herein, we introduce a post-synthetic modification strategy to non-covalently modify MOF adsorbents for the enhancement of the natural gas uptake for the MOF material. In our study, we doped PCN-250 adsorbents with C 10 alkane and C 14 fatty acid, investigating their impact on the methane uptake capabilities. We found that even trace amount of heavy hydrocarbons can considerably enhance the raw methane uptake of the MOF while still being regenerable. The doped hydrocarbons are presumably located at the mesoporous defects of PCN-250, thus optimizing the framework-methane interactions. Finally, these findings reveal a general approach which can be used to modify the MOF absorbents, improving their ability to be sustainable and renewable natural gas adsorption platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Error-mitigated data-driven circuit learning on noisy quantum hardware

Application-level benchmarks measure how well a quantum device performs meaningful calculations. In the case of parameterized circuit training, the computational task is the preparation of a target quantum state via optimization over a loss landscape. This is complicated by various sources of noise, fixed hardware connectivity, and generative modeling, the choice of target distribution. Gradient-based training has become a useful benchmarking task for noisy intermediate-scale quantum computers because of the additional requirement that the optimization step uses the quantum device to estimate the loss function gradient. In this work, we use gradient-based data-driven circuit learning to qualitatively evaluate the performance of several superconducting platform devices and present results that show how error mitigation can improve the training of quantum circuit Born machines with 28 tunable parameters.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

FAD-Toolset (Floating Array Design Toolset) [SWR-26-056]

The Floating Array Design (FAD) Toolset is a collection of tools for modeling and designing arrays of floating offshore structures. It was originally designed for floating wind systems but has applicability for many offshore applications. A core part of the FAD Toolset is the floating array model, which serves as a high-level library for efficiently modeling a floating array, such as a floating wind array. It combines site condition information and a description of the floating array design, and contains functions for evaluating the array's behavior considering the site conditions. For example, it combines information about site soil conditions, mooring line loads, and an array's anchor characteristics to estimate the holding capacity of each anchor. The library works in conjunction with the tools RAFT, MoorPy, and FLORIS to model floating platforms, wind turbines, mooring systems, power cables, and array wakes respectively. Layered on top of the floating array model is a set of design tools that can be used for algorithmically adjusting or optimizing parts of the a floating array. Specific tools existing for mooring lines, shared mooring systems, dynamic power cables, static power cable routing, and overall array layout. These capabilities work with the design representation and evaluation functions in the floating array model, and they can be applied by users in various combinations to suit different purposes. In addition to standalone uses of the FAD Toolset, a coupling has been made with Ard, (https://github.com/NLRWindSystems/Ard) a sophisticated and flexible wind farm optimization tool. This coupling allows Ard to use certain mooring system capabilities from FAD to perform layout optimization of floating wind farms with Ard's more advanced layout optimization capabilities. The FAD Toolset works with the IEA Wind Task 49 Ontology (https://github.com/IEAWindTask49/Ontology), which provides a standardized format for describing floating wind farm sites and designs. See example use cases in our examples folder (https://github.com/NLRWindSystems/FAD-Toolset/blob/main/examples/README.md) For working with the library, it is important to understand the floating array model structure, which is described more here: https://github.com/NLRWindSystems/FAD-Toolset/blob/main/fad/README.md.

Sirkis, Leah [National Laboratory of the Rockies (↗
DOE OSTI2023

ESS-DIVE Unoccupied Aerial Systems (UAS) Reporting Format v1

Here we present documentation of the ESS-DIVE reporting format for Unoccupied Aerial System (UAS) data and metadata. This reporting format provides guidance to data contributors on how to store data to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format provides templates and guidance for the reporting of metadata for UAS experimental campaigns, individual flights, platform and sensor description. To improve data access and discoverability, the reporting format proposes a data description scheme of Levels based on the degree of processing, where Level 0 includes raw data, through to Level 3 being derived data end products. A range of examples of data types for each Level are given, with suggested file naming schemes. The reporting format presented here is intended to form a foundation for future development that will accommodate new UAS technologies and approaches to data access and use in the future. The reporting format documentation is maintained and updated on the ESS-DIVE Community Space GitHub at https://github.com/ess-dive-community/essdive-uas. This data package is the first published version of this reporting format, and comprises a zip file of the complete content of https://github.com/ess-dive-community/essdive-uas v1.0. The zip contains the reporting format description, instructions and variable definitions in GitHub markdown language (*.md) and metadata templates in csv format. The reporting format is designed to be compatible with other ESS-DIVE formats, and it is specifically recommended that this reporting format be used in conjunction with the File-level metadata (FLMD) and comma separated values (csv) reporting formats for submission to the ESS-DIVE repository.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Budget Period 1 Summary Report: Hywind Maine Project

In accordance with the Statement of Project Objectives (SOPO) agreed to between the Department of Energy (“DOE”) and Statoil for the Hywind Maine project, Statoil hereby submits a Budget Period 1 Summary Report which includes accomplishments for the project. The report includes summaries of the other submitted reports (see Section 1.2-2) and progress regarding innovations leading to potential reductions in the Cost of Energy (see Section 3). The Hywind Demo project, the world’s first full-scale 2.3 megawatts (MW) floating wind turbine, installed at a water depth of 200 meter (m), 10 kilometer (km) off the coast of Norway, has proven that the Hywind floating substructure is a suitable platform for conventional multi- MW turbines. A principal goal of the Hywind Maine Project was to leverage that experience, both to demonstrate the commercial feasibility of the technology and to further develop and optimize the technology all in order to bring the costs down in a larger scale development. With the Hywind Maine Project, Statoil planned to deploy four turbines of 3 MW in approximately 140 meters water depth. Although the project in Maine will not move forward, much value was gained through the BP1 work package. Advanced modeling related to the design basis, which will have applicability beyond the Maine project, was completed. In addition, initial supply chain analyses were conducted, which will help assist with development of updated cost of energy models. Geophysical and various environmental surveys were also conducted, the results of which Statoil has committed to share publicly, and which will help build a database of information that future developers may be able to access. Finally, Statoil gained a greater understanding of the US offshore wind industry and related markets, which will assist the company as it looks for full-scale, commercial opportunities.

17 WIND ENERGY↗
DOE OSTI2018

ATDM ASD report for ECP October 2018

In October the team merged in updated ATDM/Trilinos configuration to include SPARC requirements. Assisted and worked on issues identified by the EMPIRE switchover to the ATDM/Trilinos configuration. Progress was made on automating dashboard triaging. Discovered and began addressing issues with mixed language calling with gcc-7.2 on Power8. Worked on new Python tool to filter CDASH reports to only show new issues. Completed ECP ST review. In October the team put an algorithm in place to prevent inverted elements in SPARC during refinement and worked on tests to morph mesher and should be ready for SGM integration soon. The team worked on solver rebalance and corrected issues. Complete SIMD work. Worked out next steps for solver improvements for EMPIRE. Adjusted some solver settings and to improve performance; strong scaling curves are flat instead of ascending etc. Worked on optimizing kernels for SPARC. Completed ECP ST reviews. The team worked on cleaning up branch for merge into SPARC master dev branch. Progress was made building scripts for catalyst builds on all HPC platforms of interest. Worked on adding TuckerMPl reader as plugin for catalyst. Completed ECP project review. Work has continued on NimbleSM, which now runs in a container and can be used modularly with an MPI code. The team also made progress on a qthreads version of NibleSM. Worked on build system issues within NimbleSM and Sierra for GPUs. Progressed on getting kookfied material models in NimbleSM. The team incorporated panel feedback form ECP project reviews into FY19 plans.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ExaTN: Scalable GPU-Accelerated High-Performance Processing of General Tensor Networks at Exascale

We present ExaTN (Exascale Tensor Networks), a scalable GPU-accelerated C++ library which can express and process tensor networks on shared- as well as distributed-memory high-performance computing platforms, including those equipped with GPU accelerators. Specifically, ExaTN provides the ability to build, transform, and numerically evaluate tensor networks with arbitrary graph structures and complexity. It also provides algorithmic primitives for the optimization of tensor factors inside a given tensor network in order to find an extremum of a chosen tensor network functional, which is one of the key numerical procedures in quantum many-body theory and quantum-inspired machine learning. Numerical primitives exposed by ExaTN provide the foundation for composing rather complex tensor network algorithms. We enumerate multiple application domains which can benefit from the capabilities of our library, including condensed matter physics, quantum chemistry, quantum circuit simulations, as well as quantum and classical machine learning, for some of which we provide preliminary demonstrations and performance benchmarks just to emphasize a broad utility of our library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Development of a new ion mobility time-of-flight mass spectrometer

Complex samples require multidimensional measurements with high resolution for full characterization of biological and environmental systems. To address this challenge, we developed a drift tube-based ion mobility spectrometry-Orbitrap mass spectrometry (IMS-Orbitrap MS) platform. To circumvent the timing difference between the fast IMS separation and the slow Orbitrap MS acquisition, we utilized a dual gate and pseudorandom sequence to multiplex ions into the drift tube and Orbitrap. The instrument was designed to operate in signal averaging (SA), single multiplexing (SM) and double multiplexing (DM) IMS modes to fully optimize the signal-to-ratio of the measurements. For the SM measurements, a previously developed algorithm was used to reconstruct the IMS data, while a new algorithm was developed for the DM analyses. The new algorithm is a two-step process that first recovers the SM data from the encoded DM data and then decoded the SM data. The algorithm also performs multiple refining procedures in order to minimize the demultiplexing artifacts traditionally observed in such scheme. The new IMS-Orbitrap MS platform was demonstrated for the analysis of proteomic and petroleum samples, where the integration of IMS and high mass resolution proved essential for accurate assignment of molecular formulae.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Risk Analysis of Various Design Architectures for High Safety-significant Safety-related Digital Instrumentation and Control Systems of Nuclear Power Plants during Accident Scenarios

This report documents the plus-up activities performed by Idaho National Laboratory (INL) during Fiscal Year (FY) 2022 for the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program, Risk Informed Systems Analysis (RISA) Pathway, digital instrumentation and control (DI&C) risk assessment project. In FY 2019, the RISA Pathway initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades/designs. An integrated risk assessment technology for the DI&C systems was proposed for this strategy, which aims to (1) provide a best-estimate, risk-informed capability to quantitatively and accurately estimate the safety margin obtained from plant modernization, especially for the high safety-significant safety-related (HSSSR) DI&C systems, (2) support and supplement existing advanced risk-informed DI&C design guides by providing quantitative risk information and evidence, (3) offer a capability of design architecture evaluation of various DI&C systems to support system design decisions and diversity and redundancy applications, (4) assure the long-term safety and reliability of HSSSR DI&C systems, and (5) reduce uncertainty in costs and support integration of DI&C systems in the plant. To achieve these technical goals and deal with the expensive licensing justifications from regulatory insights, the LWRS-developed framework instructs nuclear vendors and utilities on how to effectively lower the costs associated with digital compliance and speed industry advances by: (1) defining an integrated risk-informed analysis process for DI&C upgrade, including hazard analysis, reliability analysis, and consequence analysis, (2) applying systematic and risk-informed tools to address common cause failures (CCFs) and quantify corresponding failure probabilities for DI&C technologies, particularly software CCFs, (3) evaluating the impact of digital failures at the component level, system level, and plant level, and (4) providing insights and suggestions on designs to manage the risks, thus to support the development, licensing, and deployment of advanced DI&C technologies on nuclear power plant (NPPs). Adding diversity within system or components is the main means to eliminate and mitigate CCFs, but diversity also increases plant complexity and errors and may not address all sources of systematic failures. How to optimize the diversity and redundancy applications for the safety-critical DI&C systems remains a challenge. To deal with the technical issues in addressing potential software CCFs in HSSSR DI&C systems of NPPs and supporting relevant design optimization, the framework provides: ? An integrated best-estimate, risk-informed capability to address new technical digital issues quantitatively, accurately, and efficiently in plan modernization progress, such as software CCFs in HSSSR DI&C systems of NPPs ? A common and a modularized platform for DI&C designers, software developers, cybersecurity analysts, and plant engineers to efficiently predict and prevent risk in the early design stage of DI&C systems ? Technical bases and risk-informed insights to assist U.S. Nuclear Regulatory Commission (NRC) and industry to address and fulfill the risk-informed alternatives for evaluation of CCFs in HSSSR DI&C systems of NPPs ? An integrated risk-informed tool that offers a capability of design architecture evaluation of various DI&C systems to support system design decisions in diversity and redundancy applications. The plus-up research and development efforts of this project in FY 2022 are focused on methodology improvement of software CCF modeling and estimation, prevention analysis, importance analysis and risk analysis of various design architectures of HSSSR DI&C systems. This work greatly enhances the capability of the LWRS-developed framework for the risk assessment and design optimization of safety-critical DI&C systems. It should be noted that all the analyses are performed for the demonstration of the LWRS-developed framework, not for the evaluation of relevant systems. Results are obtained based on very limited design information and testing data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Engineered enzymatic cascade converts diols to amino alcohols

Aliphatic amino alcohols such as 6-amino-1-hexanol are potential platform chemicals for a variety of advanced materials, but applications are currently limited by reagent costs. Aliphatic amino alcohols can currently be synthesized from biomass-derived diols at elevated temperatures and pressures using Ru-based catalysts that produce a mixture of amino-alcohol, diamine, and cyclic amine products. Replacing chemical amination with an enzymatic cascade would reduce resource needs and enable reactions under milder conditions. In this work, we characterized a two-enzyme cascade that selectively converts C4–C7 diols to the corresponding amino alcohols under aqueous conditions at room temperature and pressure. By engineering the rate-limiting enzyme and optimizing reaction conditions, we increased amino alcohol production nearly 30-fold, achieving a selectivity of 99%. The same enzyme cascade could also be used to convert amino alcohols into cyclic amines through reduction of the corresponding cyclic imine. This engineered cascade provides a green opportunity to sustainably synthesize asymmetric bifunctional platform chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Resolving SPARC–HSA binding kinetics with an ultrasensitive photonic sensor based on bound states in the continuum

Secreted protein acidic and rich in cysteine (SPARC) is critical in cell-matrix interactions and tissue remodeling. It influences tumor progression through its affinity for human serum albumin (HSA) - the most abundant plasma protein, which also plays a crucial role in drug delivery. Strong molecular binding leads to a dissociation constant KD in the nanomolar range. Thus, determining KD requires detecting sub-nanomolar concentrations with ultrasensitive methods. This may be crucial for elucidating the nature of SPARC-HSA binding, as their interaction remains a subject of debate. Capturing these interactions accurately requires a platform capable of resolving rapid binding kinetics at extremely low analyte concentrations. In this work, we report on a microfluidics-integrated photonic nanostructure that supports bound states in the continuum (BICs) and is optimized for studying the fast kinetics of high-affinity protein-protein interactions. The unprecedented capability of detecting sub-nanomolar concentrations allows quantifying KD between SPARC and HSA beyond the state of the art. We leverage an all-dielectric photonic crystal slab (PhCS) sustaining two BIC branches arising from gapped Dirac cone dispersion. HSA is covalently immobilized on the PhCS bonded to a PDMS microfluidic chamber. SPARC dissociation is carried out using PBS buffer (pH 7.4), ensuring complete protein release through precise control of the flow rate and continuous spectral monitoring of the BICs. The measured KD=8.2±0.8 nM confirms the strong affinity of SPARC for HSA. This study highlights the potential of BIC-based sensing as a versatile tool for investigating protein interactions. These results also have implications for the optimization of drug delivery systems and cancer treatment strategies.

Albumin↗
DOE OSTI2021

Directing Energy Transfer in Halide Perovskite–Chromophore Hybrid Assemblies

Directing the flow of energy and the nature of the excited states that are produced in nanocrystal–chromophore hybrid assemblies is crucial for realizing their photocatalytic and optoelectronic applications. Using a combination of steady-state and time-resolved absorption and photoluminescence (PL) experiments, we have probed the excited-state interactions in the CsPbBr 3 –Rhodamine B (RhB) hybrid assembly. PL studies reveal quenching of the CsPbBr 3 emission with a concomitant enhancement of the fluorescence of RhB, indicating a singlet-energy-transfer mechanism. Transient absorption spectroscopy shows that this energy transfer occurs on the ~200 ps time scale. To understand whether the energy transfer occurs through a Förster or Dexter mechanism, we leveraged facile halide-exchange reactions to tune the optical properties of the donor CsPbBr 3 by alloying with chloride. This allowed us to tune the spectral overlap between the donor CsPb(Br 1–x Cl x ) 3 emission and acceptor RhB absorption. For CsPbBr 3 -RhB, the rate constant for energy transfer (k ET ) agrees well with Förster theory, whereas alloying with chloride to produce chloride-rich CsPb(Br 1–x Cl x ) 3 favors a Dexter mechanism. These results highlight the importance of optimizing both the donor and acceptor properties to design light-harvesting assemblies that employ energy transfer. Furthermore, the ease of tuning optical properties through halide exchange of the nanocrystal donor provides a unique platform for studying and tailoring excited-state interactions in perovskite–chromophore assemblies.

14 SOLAR ENERGY↗
DOE OSTI2022

High-efficiency microwave-optical quantum transduction based on a cavity electro-optic superconducting system with long coherence time

Abstract Frequency conversion between microwave and optical photons is a key enabling technology to create links between superconducting quantum processors and to realize distributed quantum networks. We propose a microwave-optical transduction platform based on long-coherence time superconducting radio-frequency (SRF) cavities coupled to electro-optic optical cavities to mitigate the loss mechanisms that limit the attainment of high conversion efficiency. We optimize the microwave-optical field overlap and optical coupling losses in the design while achieving long microwave and optical photon lifetime at milli-Kelvin temperatures. This represents a significant enhancement of the transduction efficiency up to 50% under incoming pump power of 140 μW, which allows the conversion of few-photon quantum signals. Furthermore, this scheme exhibits high resolution for optically reading out the dispersive shift induced by a superconducting transmon qubit coupled to the SRF cavity. We also show that low microwave losses enhance the fidelity of heralded entanglement generation between two remote quantum systems. Finally, high precision in quantum sensing can be reached below the standard quantum limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗