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At least 1,657 records · Page 92

Understanding the Origin of Highly Selective CO 2 Electroreduction to CO on Ni,N‐doped Carbon Catalysts

Abstract Ni,N‐doped carbon catalysts have shown promising catalytic performance for CO 2 electroreduction (CO 2 R) to CO; this activity has often been attributed to the presence of nitrogen‐coordinated, single Ni atom active sites. However, experimentally confirming Ni−N bonding and correlating CO 2 reduction (CO 2 R) activity to these species has remained a fundamental challenge. We synthesized polyacrylonitrile‐derived Ni,N‐doped carbon electrocatalysts (Ni‐PACN) with a range of pyrolysis temperatures and Ni loadings and correlated their electrochemical activity with extensive physiochemical characterization to rigorously address the origin of activity in these materials. We found that the CO 2 R to CO partial current density increased with increased Ni content before plateauing at 2 wt % which suggests a dispersed Ni active site. These dispersed active sites were investigated by hard and soft X‐ray spectroscopy, which revealed that pyrrolic nitrogen ligands selectively bind Ni atoms in a distorted square‐planar geometry that strongly resembles the active sites of molecular metal–porphyrin catalysts.

Koshy, David M.↗

Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification

In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.

Bayesian inference↗

Supported and coordinated single metal site electrocatalysts

The ever-increasing global environmental and energy crisis issues necessitates technological innovation, especially in the development of renewable energy-related devices, such as electrochemical energy conversion and storage technologies, including fuel cells, water electrolyzers, and CO 2 electrolyzers. Reliable and sustainable energy conversion devices are highly dependent on engineering of electrocatalysts. Furthermore, state-of-the-art electrocatalysts for these electrochemical conversion systems are usually platinum group metal (PGM)-based nanoparticles with high cost, which has sparked intensive research on atomically dispersed single metal site electrocatalysts for decreasing metal loadings and boosting catalytic efficiencies by taking advantage of their inherent electronic effects, quantum size effects, and metal-support interactions. In this review, we first introduced the intrinsic active site identification and stabilization, followed by addressing the mutual metal-support interactions, and most important, their correlations with catalytic properties. Next, the advances in synthetic strategies and characterization techniques for single metal site electrocatalysts are highlighted. Additionally, recent advances in single metal site electrocatalysts designs for applications in electrochemical conversion reactions are also presented. Finally, remaining challenges and a forward-looking perspective on this field of research are provided.

25 ENERGY STORAGE↗

Current Status and Future Directions of All-Solid-State Batteries with Lithium Metal Anodes, Sulfide Electrolytes, and Layered Ternary Oxide Cathodes

All-solid-state batteries (ASSBs) offer great promise as a next-generation energy storage technology with higher energy density, wider operating temperature range, and improved safety for electric vehicles. ASSBs employing lithium metal anodes (Li), sulfide-based solid-state electrolytes (SSE), and Ni-rich layered transition metal oxide cathodes (LiMO 2 , M = Ni, Mn, Co, Al) are particularly promising due to its superior electrochemical performance compared to other solid-electrolyte systems. However, the battery cycle life at high cathode mass loading and high current is still limited because the failure mechanism is not fully understood. Lithium dendrite growth at the anode or inside a solid electrolyte still represents as a serious risk of cell failure. Interfacial resistance increases attributed to electrolyte decomposition and interfacial void formation at both cathode–electrolyte and anode–electrolyte interfaces lead to gradual capacity fading. In this Review, we present the fundamental challenges and recent scientific understandings of each component in ASSBs. The novel diagnostic tools for these components, especially the interfaces buried under the surface that are often hard for characterization are mainly examined. Lastly, we offer a perspective for future research directions. We hope this Review will provide a timely snapshot of state-of-the-art research progress in ASSBs to accelerate the development of ASSBs.

25 ENERGY STORAGE↗

Inverse methods for design of soft materials

Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide a systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. Furthermore, while multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models.

36 MATERIALS SCIENCE↗

Performing k eff Validation of As-Loaded Criticality Safety Calculations Using UNF-ST&DARDS: Sensitivity Calculations

The general method for performing validation of as loaded criticality safety calculations using UNF ST&DARDS is presented in a paper by Clarity, which includes a description of the UNF-ST&DARDS system. Proof-of-principle analyses were performed in the summer of 2019 for MPC-32 dual purpose canisters (DPCs) containing pressurized water reactor (PWR) fuel assemblies. Summaries of these results are presented in this and a companion paper for this conference. The current paper describes the TSUNAMI-3D calculations performed to generate sensitivity data, and the companion paper discusses the selection of critical experiments applicable for validation of the 11 MPC-32 DPCs considered. The generation of sensitivity data for as-loaded spent nuclear fuel (SNF) DPCs is a challenge given the detailed model of the fuel compositions generated by UNF ST&DARDS. Each fuel assembly is modeled with its own irradiation history in 18 axial nodes, unless the fuel assembly is damaged and thus considered as fresh by design basis. This results in a set of 576 fuel compositions, each of which must be processed separately in a multigroup (MG) calculation. Therefore, a continuous-energy (CE) TSUNAMI-3D method was chosen to alleviate this challenge. Two CE TSUNAMI-3D methods are available in SCALE 6.2.3: the iterated fission probability (IFP) and contribution-linked eigenvalue sensitivity/uncertainty estimation via track-length importance characterization (CLUTCH). Since the IFP method is not feasible because of memory requirements associated with its implementation in SCALE, the CLUTCH method was selected for these calculations. CLUTCH has been implemented in SCALE in parallel, allowing long calculations to be performed in reasonable timeframes. The two primary user inputs necessary for CLUTCH calculations are the F*(r) mesh and the number of latent generations used in determining the F*(r) function. This F*(r) function is used as the importance function for fission chains originating in a given volume element (voxel), and it is calculated using the IFP method in the skipped generations. A large number of skipped generations is thus required to ensure accurate calculation of this importance function. In these calculations, 500 generations were used to calculate the F*(r) function. For more information regarding the calculation of F*(r), see Jones [4]. The remainder of this paper is focused on the selection of the F*(r) mesh and the number of latent generations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Performance of Embedded Sensors in 3D Printed SiC

This report summarizes recent advances in embedding sensors in 3D printed silicon carbide (SiC) ceramic components under the Transformational Challenge Reactor (TCR) program. The additive manufacturing technologies developed under this program will enable fabrication of complex structures with embedded fuels and sensors. The sensors will be capable of characterizing fuel performance using spatially distributed measurements at the most strategic locations that would be otherwise inaccessible using conventional manufacturing techniques. While previous programmatic updates describe initial concepts for embedding sensors, materials selection, and initial characterization of the embedded sensors, the technology requires further demonstration, and quality-significant procedures must be established before the embedding technology is ready for adoption by industry. To this end, this report describes the most effective techniques that have been used to embed functional sensors in 3D printed components using a combination of binder-jet additive manufacturing and chemical vapor infiltration (CVI). A detailed procedure describes each step in the process and is available upon request. Molybdenum (Mo)-sheathed thermocouples have been successfully embedded in complex SiC components, and temperatures were monitored in situ during the embedding process. Post-embedding measurements showed no significant hysteresis, and characterization of the interface revealed qualitatively strong bonding around the entire perimeter of the sensor sheath. Distributed fiber-optic temperature sensors were able to briefly measure temperature profiles during CVI, but they ultimately failed prior to completion of the CVI run. The failure appears to be related to the fiber coating at temperatures close to 1,000°C. Future work will focus on irradiation testing of embedded thermocouples and other sheathed electrical sensors, as well as the identification of fiber-optic sensor coatings that can survive CVI.

42 ENGINEERING↗

Simple Scattering: Lipid nanoparticle structural data repository

Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.

59 BASIC BIOLOGICAL SCIENCES↗

Effect of Composition on the Phase Structure and Magnetic Properties of Ball-Milled LaFe11.71-xMnxSi1.29H1.6 Magnetocaloric Powders

Magnetocaloric alloys are an important class of materials that enable non-vapor compression cycles. One promising candidate for magnetocaloric systems is LaFeMnSi, thanks to a combination of factors including low-cost constituents and a useful curie temperature, although control of the constituents’ phase distribution can be challenging. In this paper, the effects of composition and high energy ball milling on the particle morphology and phase stability of LaFe11.71-xMnxSi1.29H1.6 magnetocaloric powders were investigated. The powders were characterized with optical microscopy, dynamic light scattering, X-ray diffraction (XRD), and differential scanning calorimetry (DSC). It was found that the powders retained most of their original magnetocaloric phase during milling, although milling reduced the degree of crystallinity in the powder. Furthermore, some oxide phases (<1 weight percent) were present in the as-received and milled powders, which indicates that no significant contamination of the powders occurred during milling. Finally, the results indicated that the Curie temperature drops as Fe content decreases (Mn content increases). In all of the powders, milling led to an increase in the Curie temperature of ~3–6 °C.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast

We report turbulence intensity (TI) is often used to quantify the strength of turbulence in wind energy applications and serves as the basis of standards in wind turbine design. Thus, accurately characterizing the spatiotemporal variability in TI should lead to improved predictions of power production. Nevertheless, turbulence measurements over the ocean are far less prevalent than over land due to challenges in instrumental deployment, maintenance, and operation. Atmospheric models such as mesoscale (weather prediction) and large-eddy simulation (LES) models are commonly used in the wind energy industry to assess the spatial variability of a given site. However, the TI derivation from atmospheric models has not been well examined. An algorithm is proposed in this study to realize online calculation of TI in the Weather Research and Forecasting (WRF) model. Simulated TI is divided into two components depending on scale, including sub-grid (parameterized based on turbulence kinetic energy (TKE)) and grid resolved. The sensitivity of sea surface temperature (SST) on simulated TI is also tested. An assessment is performed by using observations collected during a field campaign conducted from February to June 2020 near the Woods Hole Oceanographic Institution Martha's Vineyard Coastal Observatory. Results show that while simulated TKE is generally smaller than the lidar-observed value, wind speed bias is usually small. Overall, this leads to a slight underestimation in sub-grid-scale estimated TI. Improved SST representation subsequently reduces model biases in atmospheric stability as well as wind speed and sub-grid TI near the hub height. Large TI events in conjunction with mesoscale weather systems observed during the studied period pose a challenge to accurately estimating TI from models. Due to notable uncertainty in accurately simulating those events, this suggests summing up sub-grid and resolved TI may not be an ideal solution. Efforts in further improving skills in simulating mesoscale flow and cloud systems are necessary as the next steps.

17 WIND ENERGY↗

Detecting flash drought events to inform adaptive water management

Abstract: Flash droughts are characterized by a rapid onset of drought conditions, a common feature among various indicators of these events. The lack of a standardized definition and indicator makes flash droughts particularly challenging to predict and manage. We test six state-of-the-art flash drought indicators using 41 years of daily data across all Hydrological Unit Code 4 (HUC4) regions in the contiguous United States. Our results show substantial disagreements among indicators, suggesting that the detection of a flash drought event strongly depends on the choice of indicator. From the perspective of informing management and operations, an open challenge remains: which flash drought indicator should be used to trigger operational adaptations? To this end, we propose a methodological framework that informs adaptive actions by regionally assessing the level of agreement between different indicators. The regional differences in hydroclimatic conditions are expected to be reflected in the level of agreement between indicators. To better inform adaptive actions, the framework will also consider the impacted sectoral water uses and their varying response times. The insights gained from this study improve the understanding of how these different flash drought indicators capture the different aspects of regional differences and impacted sectors. Furthermore, this information can support more targeted adaptive actions to mitigate adverse impacts through improved preparedness and response strategies.

climate resilience↗

Silicon carbide, an emerging high temperature semiconductor

In recent years, the aerospace propulsion and space power communities have expressed a growing need for electronic devices that are capable of sustained high temperature operation. Applications for high temperature electronic devices include development instrumentation within engines, engine control, and condition monitoring systems, and power conditioning and control systems for space platforms and satellites. Other earth-based applications include deep-well drilling instrumentation, nuclear reactor instrumentation and control, and automotive sensors. To meet the needs of these applications, the High Temperature Electronics Program at the Lewis Research Center is developing silicon carbide (SiC) as a high temperature semiconductor material. Research is focussed on developing the crystal growth, characterization, and device fabrication technologies necessary to produce a family of silicon carbide electronic devices and integrated sensors. The progress made in developing silicon carbide is presented, and the challenges that lie ahead are discussed.

Matus, Lawrence G.↗

Initial Multidisciplinary Design and Analysis Framework

Within the Supersonics (SUP) Project of the Fundamental Aeronautics Program (FAP), an initial multidisciplinary design & analysis framework has been developed. A set of low- and intermediate-fidelity discipline design and analysis codes were integrated within a multidisciplinary design and analysis framework and demonstrated on two challenging test cases. The first test case demonstrates an initial capability to design for low boom and performance. The second test case demonstrates rapid assessment of a well-characterized design. The current system has been shown to greatly increase the design and analysis speed and capability, and many future areas for development were identified. This work has established a state-of-the-art capability for immediate use by supersonic concept designers and systems analysts at NASA, while also providing a strong base to build upon for future releases as more multifidelity capabilities are developed and integrated.

Ozoroski, L. P.↗

Ceramic Matrix Composites for Rotorcraft Engines

Ceramic matrix composite (CMC) components are being developed for turbine engine applications. Compared to metallic components, the CMC components offer benefits of higher temperature capability and less cooling requirements which correlates to improved efficiency and reduced emissions. This presentation discusses a technology develop effort for overcoming challenges in fabricating a CMC vane for the high pressure turbine. The areas of technology development include small component fabrication, ceramic joining and integration, material and component testing and characterization, and design and analysis of concept components.

Halbig, Michael C.↗

Small Radioisotope Power System at NASA Glenn Research Center

In April 2009, NASA Glenn Research Center (GRC) formed an integrated product team (IPT) to develop a Small Radioisotope Power System (SRPS) utilizing a single Advanced Stirling Convertor (ASC) with passive balancer for possible use by the International Lunar Network (ILN) program. The ILN program is studying the feasibility of implementing a multiple node seismometer network to investigate the internal lunar structure. A single ASC produces approximately 80 W(sub e) and could potentially supply sufficient power for that application. The IPT consists of Sunpower, Inc., to provide the single ASC with balancer, The Johns Hopkins University Applied Physics Laboratory (JHU/APL) to design an engineering model Single Convertor Controller (SCC) for an ASC with balancer, and NASA GRC to provide technical support to these tasks and to develop a simulated lunar lander test stand. A controller maintains stable operation of an ASC. It regulates the alternating current produced by the linear alternator of the convertor, provides a specified output voltage, and maintains operation at a steady piston amplitude and hot end temperature. JHU/APL also designed an ASC dynamic engine/alternator simulator to aid in the testing and troubleshooting of the SCC. This paper describes the requirements, design, and development of the SCC, including some of the key challenges and the solutions chosen to overcome those issues. In addition, it describes the plans to analyze the effectiveness of a passive balancer to minimize vibration from the ASC, characterize the effect of ASC vibration on a lunar lander, characterize the performance of the SCC, and integrate the single ASC, SCC, and lunar lander test stand to characterize performance of the overall system.

Dugala, Gina M.↗

Full-Coverage High-Resolution Daily PM(sub 2.5) Estimation using MAIAC AOD in the Yangtze River Delta of China

Satellite aerosol optical depth (AOD) has been used to assess population exposure to fine particulate matter (PM (sub 2.5)). The emerging high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction(MAIAC), provides a valuable opportunity to characterize local-scale PM(sub 2.5) at 1-km resolution. However, non-random missing AOD due to cloud snow cover or high surface reflectance makes this task challenging. Previous studies filled the data gap by spatially interpolating neighboring PM(sub 2.5) measurements or predictions. This strategy ignored the effect of cloud cover on aerosol loadings and has been shown to exhibit poor performance when monitoring stations are sparse or when there is seasonal large-scale missngness. Using the Yangtze River Delta of China as an example, we present a Multiple Imputation (MI) method that combines the MAIAC high-resolution satellite retrievals with chemical transport model (CTM) simulations to fill missing AOD. A two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM(sub 2.5) concentrations in 2013 and 2014 at 1 km resolution with complete coverage in space and time. The daily MI models have an average R(exp 2) of 0.77, with an inter-quartile range of 0.71 to 0.82 across days. The overall Ml model 10-fold cross-validation R(exp 2) (root mean square error) were 0.81 (25 gm(exp 3)) and 0.73 (18 gm(exp 3)) for year 2013 and 2014, respectively. Predictions with only observational AOD or only imputed AOD showed similar accuracy.Comparing with previous gap-filling methods, our MI method presented in this study performed bette rwith higher coverage, higher accuracy, and the ability to fill missing PM(sub 2.5) predictions without ground PM(sub 2.5) measurements. This method can provide reliable PM(sub 2.5)predictions with complete coverage that can reduce biasin exposure assessment in air pollution and health studies.

PM2.5↗

Maneuver Strategy for OSIRIS-REx Proximity Operations

The Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRIS-REx) asteroid sample return mission will study and observe asteroid (101955) Bennu (previously known as 1999 RQ36) and subsequently collect and return a sample from the asteroid to Earth for further detailed analysis. After a successful launch in September 2016, the spacecraft will be in cruise phase for two years until arrival at asteroid Bennu in late 2018. At that time, aseries of critical maneuvers will provide an initial characterization of Bennu and the dynamical environment surrounding it, ultimately concluding with a successful capture into orbit about the small asteroid. This paper discusses some of the unique navigation challenges presented by these early operational phases in close proximity to Bennu and shares key observations and results from operational tests that have prepared the operations team and help mitigate the risks posed by these challenges.

Wibben, Daniel R.↗

Effect of Boundary Conditions on Process-Induced Stresses in a Plain Weave Unit Cell

Woven polymer matrix composites (PMCs) are leveraged in aerospace applications for their desirable specific properties, yet they are vulnerable to high residual stresses during manufacturing and their complex geometry makes experimental results difficult to observe. Process modeling is needed to characterize the effects of the curing and predict end stress states. Finite element software can be used to model woven architectures, however accurate representation of processing conditions remains a challenge when it comes to selecting boundary conditions. The effect of BCs on process-induced stress within woven PMCs is studied. The commercial Finite Element Analysis (FEA) software Abaqus is coupled with user-written subroutines in a process modeling framework. A two-dimensionally (2D) woven PMC repeating unit cell (RUC) is modeled with TexGen and Abaqus. Virtual curing is imposed on the bulk matrix. The BC study is conducted with Free, Periodic, Flat, and Flat-Free configurations. Results show that the end stress state is sensitive to the boundary condition assumptions. Flat BC results show great agreement with Periodic BCs. Residual stress results from process modeling are then compared with a linear-elastic thermal cooldown analysis in Abaqus. Cooldown results indicate an overestimation in matrix stresses compared with process modeling.

micromechanics↗