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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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Comparing in planta accumulation with microbial routes to set targets for a cost-competitive bioeconomy

Plants and microbes share common metabolic pathways for producing a range of bioproducts that are potentially foundational to the future bioeconomy. However, in planta accumulation and microbial production of bioproducts have never been systematically compared on an economic basis to identify optimal routes of production. A detailed technoeconomic analysis of four exemplar compounds (4-hydroxybenzoic acid [4-HBA], catechol, muconic acid, and 2-pyrone-4,6-dicarboxylic acid [PDC]) is conducted with the highest reported yields and accumulation rates to identify economically advantaged platforms and breakeven targets for plants and microbes. The results indicate that in planta mass accumulation ranging from 0.1 to 0.3 dry weight % (dwt%) can achieve costs comparable to microbial routes operating at 40 to 55% of maximum theoretical yields. These yields and accumulation rates are sufficient to be cost competitive if the products are sold at market prices consistent with specialty chemicals ($20 to $50/kg). Prices consistent with commodity chemicals will require an order-of-magnitude-greater accumulation rate for plants and/or yields nearing theoretical maxima for microbial production platforms. This comparative analysis revealed that the demonstrated accumulation rates of 4-HBA (3.2 dwt%) and PDC (3.0 dwt%) in engineered plants vastly outperform microbial routes, even if microbial platforms were to reach theoretical maximum yields. Their recovery and sale as part of a lignocellulosic biorefinery could enable biofuel prices to be competitive with petroleum. Muconic acid and catechol, in contrast, are currently more attractive when produced microbially using a sugar feedstock. Ultimately, both platforms can play an important role in replacing fossil-derived products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks

In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider (LHC), it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in an environment with many simultaneous proton–proton interactions (pileup). Machine learning may offer a prospect for computationally efficient event reconstruction that is well-suited to heterogeneous computing platforms, while significantly improving the reconstruction quality over rule-based algorithms for granular detectors. We introduce MLPF, a novel, end-to-end trainable, machine-learned particle-flow algorithm based on parallelizable, computationally efficient, and scalable graph neural network optimized using a multi-task objective on simulated events. We report the physics and computational performance of the MLPF algorithm on a Monte Carlo dataset of top quark–antiquark pairs produced in proton–proton collisions in conditions similar to those expected for the high-luminosity LHC. The MLPF algorithm improves the physics response with respect to a rule-based benchmark algorithm and demonstrates computationally scalable particle-flow reconstruction in a high-pileup environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2013

Rapid Automated Dissolution and Analysis Techniques for Radionuclides in Recycle Process Streams

The analysis of process samples for radionuclide content is an important part of current procedures for material balance and accountancy in the different process streams of a recycling plant. The destructive sample analysis techniques currently available necessitate a significant amount of time. It is therefore desirable to develop new sample analysis procedures that allow for a quick turnaround time and increased sample throughput with a minimum of deviation between samples. In particular, new capabilities for rapid sample dissolution and radiochemical separation are required. Most of the radioanalytical techniques currently employed for sample analysis are based on manual laboratory procedures. Such procedures are time- and labor-intensive, and not well suited for situations in which a rapid sample analysis is required and/or large number of samples need to be analyzed. To address this issue we are currently investigating radiochemical separation methods based on extraction chromatography that have been specifically optimized for the analysis of process stream samples. The influence of potential interferences present in the process samples as well as mass loading, flow rate and resin performance is being studied. In addition, the potential to automate these procedures utilizing a robotic platform is evaluated. Initial studies have been carried out using the commercially available DGA resin. This resin shows an affinity for Am, Pu, U, and Th and is also exhibiting signs of a possible synergistic effects in the presence of iron.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2013

Analysis & Tools to Spur Increased Deployment of “Waste Heat” Rejection/Recycling Hybrid Ground-source Heat Pump Systems in Hot, Arid or Semiarid Climates Like Texas

This project team analyzed supplemental heat rejection/recovery (SHR) devices or systems that could be used in hybrid ground source heat pump (HGHP) systems located in arid or semi-arid regions in southwestern U.S. Identification of effective SHR solutions would enhance the deployment of ground source heat pumps (GHP) in these regions. In a parallel effort, the team developed integrated GHP models that coupled the building load, heat pump, and ground loop subsystems and which could be applied to residential and commercial office buildings. Then GHP and HGHP performances could be compared in terms of operational performance and life-cycle costs. Several potential SHR devices were analyzed by applying two strategies: 1) to remove heat directly from the water in the ground loop before it enters the ground and 2) to remove heat in the refrigerant loop of the vapor compression cycle (VCC) of the heat pump so less heat is transferred to the water loop at the condenser of the VCC. Cooling towers, adsorption coolers, and thermoelectric liquid coolers were included in strategy 1, and expanded desuperheaters, thermosyphons, and an optimized VCC were included in strategy 2. Of all SHR devices analyzed, only the cooling tower provided a cost-effective performance enhancement. For the integrated GHP model, the project team selected the building load model HAMBASE and its powerful computational Simulink/MatLab platform, empirical performance map models of the heat pumps based upon manufacturers’ performance data, and a ground loop model developed by Oklahoma State University and rewritten for this project in Simulink/MatLab. The design process used GLHEPRO, also from Oklahoma State University, to size the borehole fields. The building load and ground loop models were compared with simulations from eQuest, ASHRAE 140-2008 standards, EnergyPlus, and GLHEPRO and were found to predict those subsystems’ performance well. The integrated GHP model was applied to a 195m 2 (2100ft 2 ) residential building and a 4,982m 2 (53,628ft 2 ) three-story commercial office building, and it ran 10-15 year simulations. The integrated GHP model and its Simulink platform provided residential data, ranging from seconds to years, and commercial office building data, ranging from minutes to years. A cooling tower model was coupled to the base case integrated GHP model for the residential building and the resulting HGHP system provided a cost-effective solution for the Austin, TX location. Simulations for both the residential and commercial building models were run with varying degrees of SHR (device/system not identified) and the results were found to significantly decrease installation costs, increase heat pump efficiency (lower entering water temperature), and prolong the lifetime of the borehole field. Lifetime cycle costs were estimated from the simulation results. Sensitivity studies on system operating performance and lifetime costs were performed on design parameters, such as construction materials, borehole length, borehole configuration and spacing, grout conductivity, and effects of SHR. While some of the results are intuitive, these studies provided quantitative estimates of improved performance and cost. One of the most important results of this sensitivity study is that overall system performance is very sensitive to these design parameters and that modeling and simulation are essential tools to design cost-effective systems.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Overhead longwave infrared hyperspectral material identification using radiometric models

Material detection algorithms used in hyperspectral data processing are computationally efficient but can produce relatively high numbers of false positives. Material identification performed as a secondary processing step on detected pixels can help separate true and false positives. This paper presents a material identification processing chain for longwave infrared hyperspectral data of solid materials collected from airborne platforms. The algorithms utilize unwhitened radiance data and an iterative algorithm that determines the temperature, humidity, and ozone of the atmospheric profile. Pixel unmixing is done using constrained linear regression and Bayesian Information Criteria for model selection. The resulting product includes an optimal atmospheric profile and full radiance material model that includes material temperature, abundance values, and several fit statistics. A logistic regression method utilizing all model parameters to improve identification is also presented. This paper details the processing chain and provides justification for the algorithms used. Several examples are provided using modeled data at different noise levels.

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

Optimizing TinyTPC Test Stand for Low Energy Event Reconstruction

Introducing dopants into liquid argon offers a promising avenue to enhance the sensitivity and performance of LAr detectors such as DUNE. To explore this opportunity, we are optimizing and deploying the TinyTPC test stand, a compact time projection chamber with LArPix readout to study the effects of photosensitive dopants on the charge detected. TinyTPC enables comparative measurements of low-energy responses under various operational configurations and provides a platform to study the behavior of pixel readouts at low energy. We ensured system reliability through continuous current and voltage monitoring, modeling the detector's electrical behavior, and indirectly estimating total resistance under testing conditions. We ran the Blanche cryostat under various thresholds and gain configurations to evaluate their potential impact to low-energy event reconstruction and advancing R&D efforts for dopant integration in neutrino detectors

Van Loenen, Drew [Franciscan U. Steubenville]↗
DOE OSTI2018

Advancing the Authenticatable Container Tracking System (ACTS)

The Authenticatable Container Tracking System (ACTS) is an extensible secure container tag that can be used to optimize chain-of-custody monitoring for packaged nuclear materials as they are being stored, processed, and transported. Based on the ultra–low power TI MSP430 mixed-signal microcontroller, ACTS is an active device that uses an almost-universal core platform that can be appropriately configured with expansion modules to provide the application-specific data acquisition, data logging, container sealing, and communications functions needed for 21st century material accountancy, monitoring, and tracking applications. This core architecture enables appropriately designed modules to be easily interfaced to the basic system, providing an integration path for current and new technologies. ACTS contains a built-in set of sensors and supports an expansion bus for up to six additional communications, tracking, authentication, encryption, sealing, location, or sensing modules that may enhance the monitoring and tracking of designated containers for specific applications. To date, testing has used an IEEE 802.15.4-2011 ultra-wideband–compliant wireless transceiver module that has additional capabilities for indoor positioning and proximity location. Proximity-based location (i.e., where an ACTS tag knows where it is relative to other ACTS tags) will be explored as a continuity-of-knowledge mechanism to determine unauthorized movement of containers in storage arrays of many items. Partners for this system are Oak Ridge National Laboratory, Argonne National Laboratory, Savannah River National Laboratory, Pacific Northwest National Laboratory and commercialization partner the Aquila Group.

Younkin, Jim↗
DOE OSTI2020

Density Matrix Quantum Circuit Simulation via the BSP Machine on Modern GPU Clusters

As quantum computers evolve, simulations of quantum programs on classical computers will be essential in validating quantum algorithms, understanding the effect of system noise, and design applications for future quantum computers. In this paper, we propose a novel multi-GPU programming model called MG-BSP that constructs a virtual BSP machine on top of modern multi-GPU platforms, and tweak the programming model to build a multi-GPU density matrix quantum simulator. We propose and evaluated a new formulation minimizing communication and prove that the transformation conserves original semantics when noise is introduced. We build the tool-chain to support quantum assembly open standard, synthesize testing quantum circuit, and enable ultra-deep quantum simulation. We evaluated our design on four state-of-the-art multi-GPU platforms including the latest DGX-1 and DGX-2 systems. We demonstrate simulation of 1 million gates in 94 minutes, far deeper circuits than has been demonstrated in prior work. A roofline-model analysis show that we have reached near-optimal performance under memory bound.

Li, Ang↗
DOE OSTI2018

Advancing the Authenticatable Container Tracking System (ACTS)

The Authenticatable Container Tracking System (ACTS) is an extensible secure container tag that can be used to optimize chain-of-custody monitoring for packaged nuclear materials as they are being stored, processed, and transported. Based on the ultra–low power TI MSP430 mixed-signal microcontroller, ACTS is an active device that uses an almost-universal core platform that can be appropriately configured with expansion modules to provide the application-specific data acquisition, data logging, container sealing, and communications functions needed for 21st century material accountancy, monitoring, and tracking applications. This core architecture enables appropriately designed modules to be easily interfaced to the basic system, providing an integration path for current and new technologies. ACTS contains a built-in set of sensors and supports an expansion bus for up to six additional communications, tracking, authentication, encryption, sealing, location, or sensing modules that may enhance the monitoring and tracking of designated containers for specific applications. To date, testing has used an IEEE 802.15.4-2011 ultra-wideband–compliant wireless transceiver module that has additional capabilities for indoor positioning and proximity location. Proximity-based location (i.e., where an ACTS tag knows where it is relative to other ACTS tags) will be explored as a continuity-of-knowledge mechanism to determine unauthorized movement of containers in storage arrays of many items. Partners for this system are Oak Ridge National Laboratory, Argonne National Laboratory, Savannah River National Laboratory, Pacific Northwest National Laboratory, and commercialization partner the Aquila Group.

Younkin, James↗
DOE OSTI2025

Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications

This dissertation explores factors influencing pooled rideshare (PR) adoption to provide actionable insights for transportation network companies (TNCs) and policymakers. PR allows travelers to share rides with unknown passengers, offering benefits such as cost reduction and congestion relief. However, adoption remains limited due to safety concerns, privacy issues, and trust in rideshare platforms. A national U.S. survey with 5,385 respondents examined transportation preferences and barriers to PR adoption. Exploratory and confirmatory factor analyses identified five key factors influencing PR consideration—safety, service experience, privacy, traffic/environment, and time/cost. Second factor analyses examined ways to optimize PR experiences, revealing four factors—comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Privacy concerns, for instance, using regression analysis, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. The Pooled Rideshare Acceptance Model (PRAM), based on the Technology Acceptance Model, assessed the impact of these factors using the Structural Equation Model (SEM). Privacy, safety, trust, and convenience had a large effect (Cohen's f2 > 0.35) on PR acceptance, while multigroup analyses (PRAMMA) explored 16 demographic variables such as gender, generation, and income, emphasizing the need for tailored strategies. Based on all the statistical analysis and workshops using descriptive statistics, 95 actionable recommendations were made from the riders' perspective. Findings highlight the importance of customized services, user experience improvements, and policy interventions to enhance PR adoption. This dissertation provides a roadmap for future research and policy development, ensuring evidence-based, practical strategies to improve PR services in the U.S. and beyond.

Gangadharaiah, Rakesh↗
DOE OSTI2020

Centimeter-Scale Pillared-Layer Metal–Organic Framework Thin Films Mediated by Hydroxy Double Salt Intermediates for CO 2 Sensor Applications

Fabrication of metal–organic framework (MOF) thin films over macroscopic surface areas is a subject of great interest for gas sensor application platforms such as optics and microelectronics. However, a direct synthesis of MOF films at ambient conditions, in particular pillared-layer MOF films due to their anisotropic structures, remains a significant challenge. Herein, we demonstrate for the first time a facile construction of dense and continuous pillared-layer MOF thin films on a centimeter scale via an aluminum-doped zinc oxide template and hydroxy double salt (HDS) intermediates at room temperature. A series of Cu(II)-based pillared MOFs with different 1,4-benzenedicarboxylic acid (bdc) ligands were explored for optimizing MOF film formation for CO 2 sensor applications. Nonpolar ligands with lower water solubility preferentially formed crystalline pillared MOF structures from HDS intermediates. A Cu 2 (ndc) 2 (dabco) (ndc = 1,4-naphthalene-bdc; dabco = 1,4-diazabicyclo[2.2.2]octane) MOF demonstrated the most dense and uniform film growth with micrometer thickness over one square centimeter area. This synthetic approach for growing Cu 2 (ndc) 2 (dabco) MOF thin films was successfully translated toward two sensing platforms: a quartz crystal microbalance and an optical fiber sensor. These Cu 2 (ndc) 2 (dabco) MOF-coated sensors displayed sensitivity toward CO 2 and response/recovery time on the scale of seconds, even at moderate humidity levels. Finally, this work provides a road map for producing continuous and anisotropic crystalline MOF thin films over a centimeter scale area on various substrates, which will greatly facilitate their utilization in MOF-based sensor devices, among other applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Planning Optimization for Inductively Charged On-demand Automated Electric Shuttles Project at Greenville, South Carolina

Wireless charging technology presents an ideal fit for autonomous electric vehicles for realizing a fully automated system (vehicle and charger). This paper presents a planning optimization analysis for a pilot project of in-route wireless charging infrastructure serving fixed-route on-demand shared automated electric shuttles (SAESs) at Greenville, South Carolina, USA. A single-objective non-linear mixed integer system planning optimization problem is formulated. A comprehensive cost model representing the overall inductively charged SAESs system is developed, considering road construction, power electronics and materials, traction battery, and installation costs. The optimization problem is solved to determine the best combination of the system key design parameters (number and allocations of wireless chargers, charging power level, track length and on-board battery capacity) that show the most cost-effective solution and allow the SAESs to achieve charge-sustaining operation. The planning platform incorporates representative simulated traffic data (driving speed and routes) for four SAESs in the Greenville using the Simulation of Urban Mobility tool. These data are fed to a vehicle powertrain model and a wireless charger power model to predict the battery power, energy and state-of-charge profiles, which are provided to the search algorithm to assess the design objectives under specific constraints. Finally, the results indicate that implementing high-power (100 kW) wireless charger at a few designated stops for fixed-route SAESs with the proper track length allows the vehicles to realize charge-sustaining operation, infinite range, and zero recharge downtime, with a significant reduction in the on-board battery (36%) and road coverage (69%), at minimum cost.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

High-throughput solubility determination for data-driven materials design and discovery in redox flow battery research

Solubility is crucial for redox flow batteries because it affects their energy density. A data-driven approach based on artificial intelligence/machine learning models can accelerate the development of highly soluble redox-active materials, but the lack of relevant, large-quantity data makes accurate solubility prediction difficult. To overcome this deficiency, we developed a high-throughput experimentation process that combines a robotically controlled platform with high-throughput methodology to collect large-scale and high-quality solubility data. We demonstrate the potential utility and applicability of this high-throughput process by measuring the aqueous and non-aqueous solubilities of redox-active materials and studying the effect of additives on their solubilities for both aqueous and non-aqueous redox flow battery applications. A redox flow battery based on our optimized negative electrolyte formulation and a ferrocyanide-positive electrolyte offers highly stable performance over 18 days (>100 cycles) with consistent capacity and a 24% boost in energy density.

25 ENERGY STORAGE↗
DOE OSTI2022

High-Temperature Gas-Cooled Reactor Research Survey and Overview: Preliminary Data Platform Construction for the Nuclear Energy University Program

Since the U.S. Department of Energy Office of Nuclear Energy initiated the Nuclear Energy University Program (NEUP) in 2009, there are 29 NEUP projects focusing on high-temperature gas-cooled reactor (HTGR) research up to July 2022. The resultant research product, either experimental or computational, were published as final NEUP reports, journal articles and conference proceedings. However, these federally funded products have been scattered and sometimes cannot be easily accessed. To improve access to this valuable HTGR validation data and optimize the return on the significant investment made by the Department of Energy, the Advanced Reactor Technologies (ART) Gas-Cooled Reactor (GCR) program started a survey of completed and ongoing HTGR NEUP projects to develop a public-access database specific for HTGRs applications that can be used to retrieve computational fluid dynamics and system code validation data. This effort will help guide future NEUP-funded research, define new state of the ART Phenomena Identification and Ranking Table (PIRT), and promote the usage of this data in the codes validation matrices. This report provides an overview of the NEUP-funded HTGR-related research projects from Fiscal Year (FY) 2009–2021 and identifies validation knowledge gaps still existing in HTGR thermal-fluid research. A preliminary data platform has been developed for the 29 NEUP projects investigating HTGR thermal hydraulics, including their final reports as well as the available scientific publications. As an ultimate goal for this work, the ART-GCR program will create a central database at Idaho National Laboratory to identify, organize, and store these datasets generated by experimental investigations or computational models, experimental facility descriptions, and publicly-available academic products from the HTGR-related NEUP projects and provide future guidance for the storage and transmission of important project documentations for later NEUP projects as well.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Rapid Aqueous Spray Fabrication of Robust NiO x : A Simple and Scalable Platform for Efficient Perovskite Solar Cells

Organometal halide perovskites have powerful intrinsic potential to drive next-generation solar technology, but their insufficient thermomechanical reliability and unproven large-area manufacturability limit competition with incumbent silicon photovoltaics. This work addresses these limitations by leveraging large-area processing and robust inorganic hole transport layers (HTLs). Inverted perovskite solar cells utilizing NiO x HTLs deposited by rapid aqueous spray-coating that outperform spin-coated NiO x and lead to a 5× improvement in the fracture energy (Gc), a primary metric of thermomechanical stability, are presented. Additionally, the morphology, chemical composition, and optoelectronic properties of the NiO x films are characterized to understand and optimize compatibility with an archetypal double cation perovskite, Cs .17 FA .83 Pb(Br .17 I .83 ) 3 . Perovskite solar cells with sprayed NiO x show higher photovoltaic performance, exhibiting up to 82% fill factor and 17.7% power conversion efficiency (PCE)—the highest PCE reported for inverted cell with scalable charge transport layers—as well as excellent stability under full illumination and after 4000 h aging in inert conditions at room temperature. By utilizing open-air techniques and aqueous precursors, this combination of robust materials and low-cost processing provides a platform for scaling perovskite modules with long-term reliability.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chapter 12: Gene Editing Technologies for Biofuel Production in Thermophilic Microbes

Thermophilic microbes are an attractive bioproduction platform due to their inherently lower contamination risk and their ability to perform thermostable enzymatic processes which may be required for biomass processing and other industrial applications. The engineering of microbes for industrial scale processes requires a suite of genetic engineering tools to optimize existing biological systems as well as to design and incorporate new metabolic pathways within strains. Yet, such tools are often lacking and/or inadequate for novel microbes, especially thermophiles. This chapter focuses on genetic tool development and engineering strategies, in addition to challenges, for thermophilic microbes. We provide detailed instructions and techniques for tool development for an anaerobic thermophile, Caldanaerobacter subterraneus subsp. tengcongensis, including culturing, plasmid construction, transformation, and selection. This establishes a foundation for advanced genetic tool development necessary for the metabolic engineering of this microbe and potentially other thermophilic organisms.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS↗
DOE OSTI2019

Evaluation of an untargeted nano-liquid chromatography-mass spectrometry approach to expand coverage of low molecular weight dissolved organic matter in Arctic soil

Abstract Characterizing low molecular weight (LMW) dissolved organic matter (DOM) in soils and evaluating the availability of this labile pool is critical to understanding the underlying mechanisms that control carbon storage or release across terrestrial systems. However, due to wide-ranging physicochemical diversity, characterizing this complex mixture of small molecules and how it varies across space remains an analytical challenge. Here, we evaluate an untargeted approach to detect qualitative and relative-quantitative variations in LMW DOM with depth using water extracts from a soil core from the Alaskan Arctic, a unique system that contains nearly half the Earth’s terrestrial carbon and is rapidly warming due to climate change. We combined reversed-phase and hydrophilic interaction liquid chromatography, and nano-electrospray ionization coupled with high-resolution tandem mass spectrometry in positive- and negative-ionization mode. The optimized conditions were sensitive, robust, highly complementary, and enabled detection and putative annotations of a wide range of compounds ( e . g . amino acids, plant/microbial metabolites, sugars, lipids, peptides). Furthermore, multivariate statistical analyses revealed subtle but consistent and significant variations with depth. Thus, this platform is useful not only for characterizing LMW DOM, but also for quantifying relative variations in LMW DOM availability across space, revealing hotspots of biogeochemical activity for further evaluation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Phonon-based scalable platform for chip-scale quantum computing

Here, we present a scalable phonon-based quantum computer on a phononic crystal platform. Practical schemes involve selective placement of a single acceptor atom in the peak of the strain field in a high-Q phononic crystal cavity that enables coupling of the phonon modes to the energy levels of the atom. We show theoretical optimization of the cavity design and coupling waveguide, along with estimated performance figures of the coupled system. A qubit can be created by entangling a phonon at the resonance frequency of the cavity with the atom states. Qubits based on this half-sound, half-matter quasi-particle, called a phoniton, may outcompete other quantum architectures in terms of combined emission rate, coherence lifetime, and fabrication demands.

97 MATHEMATICS AND COMPUTING↗