42 Search Results

Electrocatalytic nitrate reduction reaction (NO₃RR) presents a sustainable paradigm for green NH₃ synthesis and NO₃⁻ wastewater valorization. However, overcoming sluggish NO₃RR kinetics under industrial-current operation persists as a critical challenge. Herein, robust oxygen vacancy-enriched heterostructures (O_v-Co(OH)₂/Cu) are engineered through in situ electrochemical reconstruction. By coupling Cu-mediated NO₃⁻-to-NO₂⁻ conversion with O_v-Co(OH)₂-accelerated NO₂⁻-to-NH₃ transformation, this heterostructured system delivers an unprecedented NH₃ yield rate of 167.8 mg h⁻¹ cm⁻² and 97.7% Faradaic efficiency at >2 A cm⁻², while maintaining exceptional current tolerance over 25 h. Operando spectroscopic characterizations and theoretical calculations reveal that the introduction of O_v in Co(OH)₂ synergistically accelerates water dissociationmore » to ensure continuous hydrogen supply and optimizes *NOOH adsorption, reducing the energy barrier for the rate-limiting step (*NO₂ to *NOOH). To demonstrate practical viability, a membrane-electrode-assembly electrolyzer integrating NO₃RR with glycerol oxidation reaction achieves highly effective co-production of NH₃ and formate alongside wastewater treatment. In conclusion, this work offers new insights into the rational design of electrocatalysts through in situ reconstruction-induced vacancy engineering for scalable and practical NO₃RR applications.« less

DIII-D research is being accelerated by leveraging high performance computing (HPC) and data resources available through the National Energy Research Scientific Computing Center (NERSC) Superfacility initiative. As part of this initiative, a high-resolution, fully automated, whole discharge kinetic equilibrium reconstruction workflow was developed that runs at the NERSC for most DIII-D shots in under 20 min. This has eliminated a long-standing research barrier and opened the door to more sophisticated analyses, including plasma transport and stability. These capabilities would benefit from being automated and executed within the larger Department of Energy Advanced Scientific Computing Research program’s Integrated Research Infrastructure (IRI)more » framework. The goal of IRI is to empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. For transport, we are looking at producing flux matched profiles and also using particle tracing to predict fast ion heat deposition from neutral beam injection before a shot takes place. Our starting point for evaluating plasma stability focuses on the pedestal limits that must be navigated to achieve better confinement. This information is meant to help operators run more effective experiments, so it needs to be available rapidly inside the DIII-D control room. So far this has been achieved by ensuring the data is available with existing tools, but as more novel results are produced new visualization tools must be developed. In addition, all of the high-quality data we have generated has been collected into databases that can unlock even deeper insights. This has already been leveraged for model and code validation studies as well as for developing AI/ML surrogates. The workflows developed for this project are intended to serve as prototypes that can be replicated on other experiments and can be run to provide timely and essential information for ITER, as well as next stage fusion power plants.« less

The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data ( i.e. organizing data/metadata, applying image filters etc. ) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI , a user interface for the well known tomography data processing package TomoPy . This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volumemore » reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI , along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI , and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.« less

Despite the increased brilliance of the new generation synchrotron sources, there is still a challenge with high-resolution scanning of very thick and absorbing samples, such as a whole mouse brain stained with heavy elements, and, extending further, brains of primates. Samples are typically cut into smaller parts, to ensure a sufficient X-ray transmission, and scanned separately. Compared with the standard tomography setup where the sample would be cut into many pillars, the laminographic geometry operates with slab-shaped sections significantly reducing the number of sample parts to be prepared, the cutting damage and data stitching problems. In this work, a laminographymore » pipeline for imaging large samples (>1 cm) at micrometre resolution is presented. The implementation includes a low-cost instrument setup installed at the 2-BM micro-CT beamline of the Advanced Photon Source. Additionally, sample mounting, scanning techniques, data stitching procedures, a fast reconstruction algorithm with low computational complexity, and accelerated reconstruction on multi-GPU systems for processing large-scale datasets are presented. The applicability of the whole laminography pipeline was demonstrated by imaging four sequential slabs throughout an entire mouse brain sample stained with osmium, in total generating approximately 12 TB of raw data for reconstruction.« less

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against inputmore » projections. This eliminates the need for pretraining, reducing reconstruction runtime by 3–20× compared to supervised methods. Our in silico results show improved shape completion and reduction of missing wedge artifacts, assessed through several voxel-based image quality metrics in real space and a novel directional Fourier Shell Correlation (FSC) metric. Our study illuminates benefits and considerations of both supervised and unsupervised approaches, guiding the development of improved reconstruction strategies.« less

Copper (Cu) is the most attractive electrocatalyst for CO₂ reduction to multi-carbon (C₂₊) products with high economic value in considerable amounts. However, the rational design of a structurally stable Cu-based catalyst that can achieve high activity and stability towards C₂₊ products remain a grand challenge. Here, in this study, a highly stable nickel oxygenate/Cu electrocatalyst is developed with abundant NiOOH/Cu interfaces by in situ electrochemical reconstruction. The nickel oxygenate/Cu electrocatalyst achieves a superior Faradaic efficiency of 86.3 ± 3.0% and a record partial current density of 2085 A g^-1 for C₂₊ products with long-term stability. In situ experimental and theoreticalmore » studies demonstrates that the exceptional performance in generating C₂₊ products is attributed to the presence of the NiOOH/Cu interfaces which increase *CO coverage, lower energy barrier for *CO coupling and stabilize *OCCO simultaneously. This work provides new insights into the rational design of electrocatalysts to achieve stable and efficient electrocatalytic CO₂ reduction capabilities.« less

Developing a fast and highly active oxygen evolution reaction (OER) catalyst to change energy kinetics technology is essential for making clean energy. Herein, we prepare three-dimensional (3D) hollow Mo-doped amorphous FeOOH (Mo-FeOOH) based on the precatalyst MoS₂/FeC₂O₄ via in situ reconstruction strategy. Mo-FeOOH exhibits promising OER performance. Specifically, it has an overpotential of 285 mV and a durability of 15 h at 10 mA cm^–2. Characterizations indicate that Mo was included inside the FeOOH lattice, and it not only modifies the electronic energy levels of FeOOH but also effectively raises the inherent activity of FeOOH for OER. Additionally, in situmore » Raman analysis indicates that FeC₂O₄ gradually transforms into the FeOOH active site throughout the OER process. Finally, this study provides ideas for designing in situ reconstruction strategies to prepare heteroatom doping catalysts for high electrochemical activity.« less

Fast 3D data analysis and steering of a tomographic experiment by changing environmental conditions or acquisition parameters require fast, close to real-time, 3D reconstruction of large data volumes. Here a performance-optimized TomocuPy package is presented as a GPU alternative to the commonly used central processing unit (CPU) based TomoPy package for tomographic reconstruction. TomocuPy utilizes modern hardware capabilities to organize a 3D asynchronous reconstruction involving parallel read/write operations with storage drives, CPU–GPU data transfers, and GPU computations. In the asynchronous reconstruction, all the operations are timely overlapped to almost fully hide all data management time. Since most cameras work withmore » less than 16-bit digital output, the memory usage and processing speed are furthermore optimized by using 16-bit floating-point arithmetic. As a result, 3D reconstruction with TomocuPy became 20–30 times faster than its multi-threaded CPU equivalent. Full reconstruction (including read/write operations and methods initialization) of a 2048 ³ tomographic volume takes less than 7 s on a single Nvidia Tesla A100 and PCIe 4.0 NVMe SSD, and scales almost linearly increasing the data size. To simplify operation at synchrotron beamlines, TomocuPy provides an easy-to-use command-line interface. Efficacy of the package was demonstrated during a tomographic experiment on gas-hydrate formation in porous samples, where a steering option was implemented as a lens-changing mechanism for zooming to regions of interest.« less

Diverse phenomena such as positron annihilation in the Milky Way, merging binary neutron stars, and dark matter can be better understood by studying their gamma ray emission. Despite their importance, MeV gamma rays have been poorly explored at sensitivities that would allow for deeper insight into the nature of the gamma emitting objects. In response, a liquid argon time projection chamber (TPC) gamma ray instrument concept called GammaTPC has been proposed and promises exploration of the entire sky with a large field of view, large effective area, and high polarization sensitivity. Optimizing the pointing capability of this instrument is crucialmore » and can be accomplished by leveraging convolutional neural networks to reconstruct electron recoil paths from Compton scattering events within the detector. In this investigation, we develop a machine learning model architecture to accommodate a large data set of high fidelity simulated electron tracks and reconstruct paths. We create two model architectures: one to predict the electron recoil track origin and one for the initial scattering direction. We find that these models predict the true origin and direction with extremely high accuracy, thereby optimizing the observatory’s estimates of the sky location of gamma ray sources.« less