Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “high performance analytics”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Roadmap for Advanced Power System Measurements

Conditions in the electric power grid are changing rapidly. Changes are driven, in particular, by the increased penetration of renewable resources in the generation mix. Part of the solution to dealing with the changing conditions is the better use of measurement systems, taking advantage of the better understanding of measurements that was heralded by digital technology. Another part of the solution is the communication of samples of the directly observable voltages and currents by means of continuous point-on-wave values. The increased bandwidth capability of such a system allows more complicated measurement models to be built at the point of use. Additional measurements and analyses can then be performed. This Roadmap hinges on these two aspects of measuring: improved measurements supported by high-bandwidth (raw data) communications. Following an introduction to the overall subject, the report explores the impact of what can be thought of as the digital revolution in measurement. In essence, that revolution has served to clarify ideas about measurement and is continuing to lead to improved processes. An extensive survey of the relevant literature was performed . Webinars were held to learn from the wider community of practitioners. Based on this information, a number of gaps were identified whose continued existence would hamper the solution of the challenges now facing measurement and communication in the power grid. The underlying value propositions are interoperability (presently not very satisfactory in many measurements); greater (and less challenging) adoption of distributed energy resources; and improved international competitiveness. Underlying all that, of course, is progress toward a more reliable and resilient power system. The identified gaps were addressed in four major thrusts: technology deployment and demonstration, education, instrumentation testing and standards, and analytical methods. For each of the thrusts, multiple goals are defined to guide the implementation. The thrusts were designed to close gaps and unlock high-value use cases. After discussing the literature review, gap analysis, high-value use cases, and research thrusts in chapters 2-5, the report culminates in chapter 6 with a roadmap that links each aspect together to form a unified plan. Developing a research portfolio aligned with this plan will ensure that advanced measurement systems are in place to support reliable operation of the power system as it modernizes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Ionomers for electrochemical energy conversion & storage technologies

We report ionomers, which are used as polymer electrolyte membranes as well as catalyst binders in membrane electrode assemblies, are a key component of electrochemical energy conversion and storage technologies such as fuel cells, electrolyzers, and flow batteries. The use of ionomers in these clean energy technologies has a long history and there has been considerable progress in developing low-cost, high-performance ionomers in recent years. In this review article, we discuss some highlights from recent developments of ionomers with emphasis on chemically stable, mechanically durable, high-performance hydrocarbon ionomers and their state-of-the-art performance in electrochemical energy conversion devices. Future directions of ionomer research that are important to enhance our understanding and the remaining challenges related to device level applications are addressed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Control-inspired design and power optimization of an active mechanical motion rectifier based power takeoff for wave energy converters

Ocean waves have high energy density and are persistent and predictable. Yet, converting wave energy to a useable form remains challenging. A significant hurdle is the oscillatory nature of waves resulting in the alternating loads, which necessitate the use of rectification at some stage of the energy conversion. This research effort presents a novel design of active mechanical motion rectifier (AMMR) for the power takeoff (PTO), which provides enhanced controllability and better power performance when compared to passive mechanical motion rectifiers (MMR). Inspired by transistors used in synchronous electrical rectifiers, the proposed design uses controllable electromagnetic clutches in the mechanical transmission to allow active engagement-disengagement control; thus, rectifying the oscillatory motion into a unidirectional rotation for high energy conversion efficiency and allowing the generator in unidirectional rotation to control the bidirectional wave capture structure for maximizing the power output. A semi-analytical computational approach is developed to efficiently evaluate the optimal power achieved using the proposed AMMR-based PTO and active control. It is found that the AMMR-based PTO design yields a higher optimal power than the previous passive MMR design across the wave spectrum. The influences of generator inertia and reactive power are discussed. Furthermore, the effects of control parameters on the power output and the optimal trajectories are analyzed. Wave tank tests with the AMMR prototype demonstrated the effectiveness of AMMR based PTO design and validated the numerical analysis.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

GAHLS: an optimized graph analytics based high level synthesis framework

The urgent need for low latency, high-compute and low power on-board intelligence in autonomous systems, cyber-physical systems, robotics, edge computing, evolvable computing, and complex data science calls for determining the optimal amount and type of specialized hardware together with reconfigurability capabilities. With these goals in mind, we propose a novel comprehensive graph analytics based high level synthesis (GAHLS) framework that efficiently analyzes complex high level programs through a combined compiler-based approach and graph theoretic optimization and synthesizes them into message passing domain-specific accelerators. This GAHLS framework first constructs a compiler-assisted dependency graph (CaDG) from low level virtual machine (LLVM) intermediate representation (IR) of high level programs and converts it into a hardware friendly description representation. Next, the GAHLS framework performs a memory design space exploration while account for the identified computational properties from the CaDG and optimizing the system performance for higher bandwidth. The GAHLS framework also performs a robust optimization to identify the CaDG subgraphs with similar computational structures and aggregate them into intelligent processing clusters in order to optimize the usage of underlying hardware resources. Finally, the GAHLS framework synthesizes this compressed specialized CaDG into processing elements while optimizing the system performance and area metrics. Evaluations of the GAHLS framework on several real-life applications (e.g., deep learning, brain machine interfaces) demonstrate that it provides 14.27× performance improvements compared to state-of-the-art approaches such as LegUp 6.2.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

AI for Science: Report on the Department of Energy (DOE) Town Halls on Artificial Intelligence (AI) for Science

The report documents the DOE Town Halls held during 2019 at Argonne National Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, and in Washington, DC. From July to October 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four town hall meetings attended by more than 1,000 U.S. scientists and engineers. The goal of the town hall series was to examine scientific opportunities in the areas of artificial intelligence (AI), Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these opportunities. In this report and in the Department of Energy (DOE) laboratory community, we use the term “AI for Science” to broadly represent the next generation of methods and scientific opportunities in computing, including the development and application of AI methods (e.g., machine learning, deep learning, statistical methods, data analytics, automated control, and related areas) to build models from data and to use these models alone or in conjunction with simulation and scalable computing to advance scientific research. The AI for Science town hall discussions focused on capturing the transformational uses of AI that employ HPC and/or data analysis, leveraging data sets from HPC simulations or instruments and user facilities, and addressing scientific challenges unique to DOE user facilities and the agency’s wide-ranging fundamental and applied science enterprise.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Perspectives on AI Architectures and Co-design for Earth System Predictability

Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

58 GEOSCIENCES↗
DOE OSTI2024

HPC-FAIR: A Framework Managing Data and AI Models for Analyzing and Optimizing Scientific Applications

The increasing reliance on machine learning (ML) to analyze and optimize large-scale scientific applications on supercomputers faces a significant bottleneck: the lack of readily available, high-quality training datasets and the difficulty in reusing existing AI models. This project was motivated by the urgent need to address the “FAIR” principles (Findability, Accessibility, Interoperability, Reusability) for both training datasets and AI models in the high-performance computing (HPC) domain. The project developed HPC-FAIR, a high-performance computing data management framework designed to centralize HPC-related datasets and AI models within a unified hub. To ensure interoperability, the framework established a standardized representation and vocabulary (ontology) for both data and models. HPC-FAIR also implemented automated workflows to streamline data processing, model access, and benchmarking. Additionally, the project focused on optimizing data harnessing efficiency through advanced techniques like deep reuse and compression-based analytics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

LaNi x Fe 1– x O 3–δ as a Robust Redox Catalyst for CO 2 Splitting and Methane Partial Oxidation

The current study reports LaNi 0.5 Fe 0.5 O 3–δ as a robust redox catalyst for CO 2 splitting and methane partial oxidation at relatively low temperatures (~700 °C) in the context of a hybrid redox process. Specifically, perovskite-structured LaNi x Fe 1–x O 3–δ (LNFs) with nine different compositions (x = 0.05–0.5) were prepared and investigated. Among the samples evaluated, LaNi 0.4 Fe 0.6 O 3–δ and LaNi 0.5 Fe 0.5 O 3–δ showed superior redox performance, with ~90% CO 2 and methane conversions and >90% syngas selectivity. The standalone LNFs also demonstrated performance comparable to that of LNF promoted by mixed conductive Ce 0.85 Gd 0.1 Cu 0.05 O 2–δ (CGCO). Long-term testing of LaNi 0.5 Fe 0.5 O 3–δ indicated that the redox catalyst gradually loses its activity over repeated redox cycles, amounting to approximately 0.02% activity loss each cycle, averaged over 500 cycles. This gradual deactivation was found to be reversible by deep oxidation with air. Further characterizations indicated that the loss of activity resulted from a slow accumulation of iron carbide (Fe 3 C and Fe 5 C 2 ) phases, which cannot be effectively removed during the CO 2 splitting step. Reoxidation with air removed the carbide phases, increased the availability of Fe for the redox reactions via solid-state reactions with La 2 O 3 , and decreased the average crystallite size of La 2 O 3 . As a result, reactivating the redox catalyst periodically, e.g., once every 40 cycles, was shown to be highly effective, as confirmed by operating the redox catalyst over 900 cumulative cycles while maintaining satisfactory redox performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Disordered, Sub-Nanometer Ru Structures on CeO 2 are Highly Efficient and Selective Catalysts in Polymer Upcycling by Hydrogenolysis

We report non-degradable polyolefin plastics pose severe environmental threats, and thus demand efficient upcycling technologies. In this work, we discovered that low-loading (= 0.25 wt%) Ru/CeO 2 exhibits remarkable catalytic performance in the hydrogenolysis of polypropylene (PP), polyethylene (PE), and n-C 16 H 34 that is superior to high-loading (= 0.5 wt%) Ru/CeO 2 . They possess high PP conversion efficiency (7-fold increase over current literature reports), low selectivity towards undesired CH 4 , and good isomerization ability. In the low-loading range, the intrinsic activity of Ru in PP hydrogenolysis increases as the particle size decreases, opposite of the trend in the high-loading range. Detailed characterization revealed that the abrupt changes in catalytic behaviors coincide with Ru species transitioning from well-defined to highly disordered structures in the low-loading domain. The disordered Ru species were shown to be sub-nanometer in size and cationic. Mechanistically, the regioselectivity and the rate dependence on hydrogen pressure of C-C bond cleavage are different on low- and high-loading Ru/CeO 2 , both explained by the higher coverage of adsorbed hydrogen (*H) on low-loading Ru/CeO 2 . This work uncovers the remarkable catalytic performance of highly disordered, sub-nanometer, cationic Ru species in polyolefin hydrogenolysis, opening immense opportunities to develop effective, selective, and versatile catalysts for plastic upcycling.

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

First Annual Report on Development of Microwave Resonant Cavity Transducer for Fluid Flow Sensing: Development of Sensor Performance Model of Microwave Cavity Flow Meter for Advanced Reactor High Temperature Fluids

We are investigating a microwave cavity-based transducer for in-core high-temperature fluid flow sensing in molten salt cooled reactors (MSCR) and sodium fast reactors (SFR). This sensor is a hollow metallic cylindrical cavity, which can be fabricated from stainless steel, and as such is expected to be resilient to radiation, high temperature and corrosive environment of MSCR and SFR. The principle of sensing consists of making one wall of the cylindrical cavity flexible enough so that dynamic pressure, which is proportional to fluid velocity, will cause membrane deflection. Membrane deflection causes cavity volume change, which leads to a shift in the resonant frequency. Feasibility of the sensor was initially investigated with analytical derivations and with COMSOL RF Module computer simulations of resonant frequency spectral shift due to uniform load. We also investigated the mechanical integrity of the flowmeter’s membrane through analytical modelling and COMSOL Structural Mechanics Module computer simulations. Both the analytic model and COMSOL model showed that maximum stresses on the plate, which are at the radial boundary of the plate, are three orders of magnitude smaller than the material’s yield strength and ultimate tensile strength. This indicates that the sensor is at a low risk of mechanical failure. Using results from models, we have developed an initial design for a microwave K-band sensor. A cylindrical resonator prototype was fabricated from brass for the initial tests. The external dimensions of the cavity are matched to the flange of a standard WR-42 waveguide. Microwave field is coupled into the resonant cavity through a subwavelength-size aperture. A test article was developed consisting of a piping Tee with a bulkhead WR-42 microwave waveguide installed in a leak-proof assembly. A microwave waveguide circulator was installed in the setup to suppress the effect of reflections at the cavity entrance by increasing the isolation between the input and the output port. Preliminary spectral characterization of cavity spectral response was performed with a portable PXIe chassis microwave VNA with a custom GUI. Preliminary dry tests of the transducer response were conducted with a set of calibrated weights. Transducer frequency shift was shown to be monotonically increasing with increasing pressure. The next steps will involve investigation of the transducer performance for water flow sensing.

42 ENGINEERING↗
DOE OSTI2021

Design and Analysis of High Heat Flux Plasma-Facing Components for NSTX Upgrade

The National Spherical Tokamak Experiment (NSTX) upgrade (NSTX-U) requirements lead to enhanced heat loads on plasma-facing components (PFCs) especially in the divertor regions, where normal heat flux density can reach up to 8 MW/m 2 . For these high heat flux (HHF) regions, the PFCs were redesigned, to use castellations which relieved the thermal stresses associated with high incident heat flux. Another design feature of HHF PFCs is the absence of front face mounting holes which create localized areas of high temperature and thermal stress concentrations. Optimized shaping of the front face of the HHF PFCs eliminates regions where the front face is perpendicular to the heat flux direction during normal helicity operation and spreads the heat load. A unique mechanism was designed to connect PFC tiles to the NSTX-U center stack casing using locking features accessible from the low heat flux regions. Isotropic graphite was selected as the HHF PFC tile material. Initial tile design parameters were accessed using analytical expressions for pulsed heat flux loading. A working prototype of the locking mechanism was created during the initial stages of the design to prove the concept performance. This article presents an overview of the divertor HHF PFC design and the results of the thermal and structural analyses performed using ANSYS software. The results of the analyses cover normal operating conditions and disruptions which impose electromagnetic (EM) loads from eddy and halo currents. The 3-D, transient, nonlinear analyses took into account the temperature-dependent properties of the materials, friction interfaces between the parts, and variable electric properties of the parts and interfaces. The results confirm that the HHF PFC tiles remain within the allowable limits for the loads defined by the NSTX-U Recovery Project. In addition, tolerance stack up analyses were performed to ensure tile performance in the worst possible assembly configuration. Finally, the design process was completed successfully, and the NSTX HHF PFC tiles are currently in the production phase.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Toward hydrogen economy: Selective guaiacol hydrogenolysis under ambient hydrogen pressure

The conversion of guaiacol into the nylon precursors, cyclohexanol and cyclohexanone, is catalyzed by palladium supported on high surface area ceria (Pd/HS CeO 2 ) under mild conditions (100 °C, ≤1 bar H 2 ) via sequential hydrodemethoxylation and hydrogenation. In contrast, the 2-methoxycyclohexanol side product is generated by direct guaiacol hydrogenation. Reaction selectivity is determined by competing CO bond hydrogenolysis versus arene hydrogenation. Hydrogenolysis selectivity increases as H 2 pressure decreases, and over 80 % H 2 is incorporated into the products at 1 bar H 2 . Higher reactivity of guaiacol than anisole implies that the hydroxyl group is essential in Pd/HS CeO 2 catalysis. Participation of water in the conversion is evidenced by deuterium incorporation at non-exchangeable positions when the reaction is performed in D 2 O. Here, the combination of near quantitative mass balance of H 2 , high recyclability, and use of water as a solvent offers a simple, green and efficient conversion of lignin-derived aromatics into commercial products.

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

Ka-band linearizer for the Ultra-Compact X-ray free-electron laser at UCLA

There is a strong demand for accelerating structures able to achieve higher gradients and more compact dimensions for the next generation of linear accelerators for research, industrial and medical applications. Notably innovative technologies will permit compact and affordable advanced accelerators as the linear collider and X-ray free-electron lasers (XFELs) with accelerating gradients over twice the value achieved with current technologies. In particular XFELs are able to produce coherent X-ray pulses with peak brightness 10 orders of magnitude greater than preceding approaches, which has revolutionized numerous research fields through imaging of the nanoscopic world at the time and length scale of atom-based systems, that is of femtosecond and Angstrom. There is a strong interest for combining these two fields, to form a proper tool with the goal of producing a very compact XFEL in order to investigate multi-disciplinary topics in chemistry, biology, materials science, medicine and physics. In the framework of the Ultra-Compact XFEL project under study at the University of California, Los Angeles, a high gradient radio-frequency accelerating structure for the longitudinal phase-space linearization with an integrated voltage of at least 15 MV working on 6th harmonic of the main Linac frequency is required. We here present the electromagnetic design of a cryogenic normal-conducting 8 cm long Ka-band standing-wave linearizer working on π mode with a target accelerating gradient beyond 100 MV/m. The studies have been performed analytically and numerically to investigate the beam dynamics and electromagnetic issues.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Correlating Oxidation State and Surface Area to Activity from Operando Studies of Copper CO Electroreduction Catalysts in a Gas-Fed Device

Copyright © 2020 American Chemical Society. The rational design of high-performance electrocatalysts requires a detailed understanding of dynamic changes in catalyst properties, including oxidation states, surface area, and morphology under realistic working conditions. Oxide-derived Cu catalysts exhibit a remarkable selectivity toward multicarbon products for the electrochemical CO reduction reaction (CORR), but the exact role of the oxide remains elusive for explaining the performance enhancements. Here, we used operando X-ray absorption spectroscopy (XAS) coupled with simultaneous measurements of the catalyst activity and selectivity by gas chromatography (GC) to study the relationship between oxidation states of Cu-based catalysts and the activity for ethylene (C2H4) production in a CO gas-fed cell. By utilizing a custom-built XAS cell, oxidation states of Cu catalysts can be probed in device-relevant settings and under high current densities (>80 mA cm-2) for the CORR. By employing an electrochemical oxidation process, we found that the Cu oxidation states and specific ion species do not correlate with C2H4 production. The difference in the CORR activity is also investigated in relation to electrochemical surface area (ECSA) changes. While the hydrogen evolution reaction (HER) activity is positively correlated to the ECSA changes, the increased C2H4 activity is not proportional to the ECSA. Ex situ characterization from microscopic techniques suggests that the changes in the C2H4 activity and selectivity may arise from a morphological transformation that evolves into a more active structure. These comprehensive results give rise to the development of a cell regeneration method that can restore the performance of the Cu catalyst without cell disassembly. Our study establishes a basis for the rational design of highly active electrocatalysts for broad-range reactions in a gas-fed device.

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

Long-Range Exciton Diffusion in Two-Dimensional Assemblies of Cesium Lead Bromide Perovskite Nanocrystals

Förster resonant energy transfer (FRET)-mediated exciton diffusion through artificial nanoscale building block assemblies could be used as an optoelectronic design element to transport energy. However, so far, nanocrystal (NC) systems supported only diffusion lengths of 30 nm, which are too small to be useful in devices. In this work, we demonstrate a FRET-mediated exciton diffusion length of 200 nm with 0.5 cm 2 /s diffusivity through an ordered, two-dimensional assembly of cesium lead bromide perovskite nanocrystals (CsPbBr 3 PNCs). Exciton diffusion was directly measured via steady-state and time-resolved photoluminescence (PL) microscopy, with physical modeling providing deeper insight into the transport process. This exceptionally efficient exciton transport is facilitated by PNCs' high PL quantum yield, large absorption cross section, and high polarizability, together with minimal energetic and geometric disorder of the assembly. This FRET-mediated exciton diffusion length matches perovskites' optical absorption depth, thus enabling the design of device architectures with improved performances and providing insight into the high conversion efficiencies of PNC-based optoelectronic devices.

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

A tailored multi-functional catalyst for ultra-efficient styrene production under a cyclic redox scheme

Abstract Styrene is an important commodity chemical that is highly energy and CO 2 intensive to produce. We report a redox oxidative dehydrogenation (redox-ODH) strategy to efficiently produce styrene. Facilitated by a multifunctional (Ca/Mn) 1− x O@KFeO 2 core-shell redox catalyst which acts as (i) a heterogeneous catalyst, (ii) an oxygen separation agent, and (iii) a selective hydrogen combustion material, redox-ODH auto-thermally converts ethylbenzene to styrene with up to 97% single-pass conversion and >94% selectivity. This represents a 72% yield increase compared to commercial dehydrogenation on a relative basis, leading to 82% energy savings and 79% CO 2 emission reduction. The redox catalyst is composed of a catalytically active KFeO 2 shell and a (Ca/Mn) 1− x O core for reversible lattice oxygen storage and donation. The lattice oxygen donation from (Ca/Mn) 1− x O sacrificially stabilizes Fe 3+ in the shell to maintain high catalytic activity and coke resistance. From a practical standpoint, the redox catalyst exhibits excellent long-term performance under industrially compatible conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Hydrogen separation with a graphenylene monolayer: Diffusion Monte Carlo study

Here, we performed fixed-node diffusion Monte Carlo (DMC) calculations to investigate structural and energetic properties of graphenylene (GPNL), a two-dimensional network of sp 2 -bonded carbon atoms with large near-circular pores, and its H 2 separation performance for gas mixtures. We have found that the energetic stability of a GPNL monolayer is comparable to that of γ-graphyne, as evidenced by its large cohesive energy of 6.755(3) eV/atom. Diffusion barriers of several gas molecules, including hydrogen, through a GPNL membrane were determined from the analysis of their adsorption energies depending on the adsorption distance, which led to our estimation for hydrogen selectivity with respect to other target molecules. DMC hydrogen selectivity of a GPNL monolayer was found to be exceptionally high at 300 K, as high as 10 10 –10 11 against CO and N 2 gases. This, along with high hydrogen permeance due to its generic pore structure, leads us to conclude that GPNL is a promising membrane to be used as a high-performance hydrogen separator from gas mixtures. We find that when compared to our DMC results, DFT calculations tend to overestimate H 2 selectivity, which is mostly due to their inaccurate description of short-range repulsive interactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗