10 Search Results

Carbon capture and storage (CCS) plays a critical role in achieving climate change mitigation targets, offering a pathway to decarbonize power generation, industrial processes, and heat production while addressing atmospheric CO₂ removal. While CCS technologies are technically advanced, the widespread adoption of 100 % CO₂ capture capacities such as 1 mol of CO₂/mol of material and 1 g CO₂/g storage (targeted by the DARPA, Defense Sciences Office, USA Govt.) has raised questions about the feasibility of achieving higher capture capacities. In the context of limiting global warming to 1.5°C, reaching 100 % CO₂ capture capacity is increasingly necessary, with residualmore » emissions requiring complementary carbon dioxide removal (CDR) technologies. This review exclusively focuses on the CO₂ capture capacities of various sorbents under standard conditions, using different evaluation metrics. This study explores the performance of solid and liquid sorbents under standard conditions, analyzing factors including surface area, pore structure, solvent type, and functionalization to identify materials optimized for industrial-scale CCS applications. Emerging sorbents, including ILs, MOFs, COFs, POPs, DES, RCC, hybrid materials, and reactive sorbents, offer significant potential for enhanced selectivity and energy-efficient regeneration. Through a systematic assessment of gravimetric, volumetric, and molar capacities, the study provides insights into material efficiencies and trade-offs, offering guidance on optimizing sorbent selection for specific applications. The research advances understanding of scalable CCS technologies, contributing to global efforts to achieve net-zero emissions and address the pressing challenge of climate change.« less

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform position and energy reconstruction, and particle classification. This work highlights the effectiveness of convolutional neural networks and graph convolutional networks in enhancing data analysis. By leveraging these techniques, a 3.3% increase in effective statistics was achieved compared to traditional methods, showcasing their potential to improve analysis performance. Furthermore, these machine learning methodologiesmore » offer promising applications for other segmented particle detectors, underscoring their versatility and impact.« less

Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency‐based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep‐learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short‐time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks aremore » used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. In this report we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN‐based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground‐truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real‐world data shows that both the CNN and MWCNN models outperform standard band‐pass filtering (BPF; average improvement in signal‐to‐noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.« less

Recent computational studies have predicted many new ternary nitrides, revealing synthetic opportunities in this underexplored phase space. However, synthesizing new ternary nitrides is difficult, in part because intermediate and product phases often have high cohesive energies that inhibit diffusion. Here, we report the synthesis of two new phases, calcium zirconium nitride (CaZrN₂) and calcium hafnium nitride (CaHfN₂), by solid state metathesis reactions between Ca₃N2 and MCl₄ (M = Zr, Hf). Although the reaction nominally proceeds to the target phases in a 1:1 ratio of the precursors via Ca₃N₂ + MCl4 → CaMN₂ + 2 CaCl₂, reactions prepared this way resultmore » in Ca-poor materials (Ca_xM_2–xN2, x < 1). A small excess of Ca₃N₂ (ca. 20 mol %) is needed to yield stoichiometric CaMN₂, as confirmed by high-resolution synchrotron powder X-ray diffraction. In situ synchrotron X-ray diffraction studies reveal that nominally stoichiometric reactions produce Zr³⁺ intermediates early in the reaction pathway, and the excess Ca₃N₂ is needed to reoxidize Zr³⁺ intermediates back to the Zr⁴⁺ oxidation state of CaZrN₂. Analysis of computationally derived chemical potential diagrams rationalizes this synthetic approach and its contrast from the synthesis of MgZrN₂. These findings additionally highlight the utility of in situ diffraction studies and computational thermochemistry to provide mechanistic guidance for synthesis.« less

Studies suggest that feitknechtite (β-MnOOH) is a prevalent, and perhaps necessary, intermediate phase during the synthesis of birnessite-like phases, the abiotic oxidation of Mn2+, and the transformation of biogenic hexagonal phyllomanganates to more complex Mn oxides in laboratory and natural systems. Researchers have generally described feitknechtite as consisting of pyrochroite-like (or cadmium iodide-like) Mn-O octahedral layers, but a detailed crystal structure has not been reported. For this work, we used TEM/SAED and powder XRD and Rietveld refinements to derive the unit cell and, for the first time, report a complete structure description for feitknechtite (β-MnOOH). Rietveld refinements were also completedmore » for three natural feitknechtite/hausmannite samples, and time-resolved synchrotron XRD experiments were used to follow the thermal transformation of feitknechtite to hausmannite. Additionally, we identified and report the structure for a second, and perhaps novel, MnOOH polymorph (proposed designation ε-MnOOH), mixed with the synthetic feitknechtite, that is similar to β-MnOOH but with a different layer stacking.« less

This review of the current state of magnetocalorics is focused on materials exhibiting a giant magnetocaloric response near room temperature. To be economically viable for industrial applications and mass production, materials should have desired useful properties at a reasonable cost and should be safe for humans and the environment during manufacturing, handling, operational use, and after disposal. The discovery of novel materials is followed by a gradual improvement of properties by compositional adjustment and thermal or mechanical treatment. Consequently, with time, good materials become inferior to the best. There are several known classes of inexpensive materials with a giant magnetocaloricmore » effect, and the search continues. View Full-Text« less

Numerical simulation of Rayleigh–Taylor instabilities involving solids has been carried out in a spherically converging geometry. A solid shell is driven toward the center by high-pressure gas outside. Low-pressure gas inside the shell is then compressed, causing the solid shell to slow down and eventually bounce back. Here, simulation results show the absence of the onset condition unlike in the planar geometry equivalent. Results also show that disturbance growth seems to be strongly associated with the deformation direction instead of the acceleration direction. For example, when the interface is compressed in the cross-stream direction, the disturbance at the gas–solid interfacemore » grows even under the Rayleigh–Taylor stable situation.« less

Cooling rate and temperature control is implemented in rotary friction welding in order to obtain favorable microstructures and avoid martensite and other brittle microstructures. Limits of achievable cooling rates in friction welding are primarily determined by thermal and geometric properties such as thermal diffusivity and length. A controller limits interface temperatures during a weld preheat, changing the thermal profile, thus decreasing the cooling rate after the weld has finished. This method is demonstrated in 1045 steel. Cooling simulations, TTT diagrams, microhardness line scans, and scanning electron microscopy are used for analysis. Without temperature and cooling rate control, a martensite readilymore » forms after a weld. With temperature and cooling rate control, martensitic transformations are avoided and a pearlitic microstructure is developed. Temperature control is a viable tool in designing post-weld microstructures within achievable cooling rate limitations.« less

Due to the high computational cost associated with using detailed kinetic mechanisms, it is common practice to implement reaction models which rely on relatively small number of variables when simulating turbulent combustion systems. In this study, we perform an a priori assessment of the accuracy of three chemistry models: a Burke-Schumann chemistry model, an equilibrium chemistry model, and a steady laminar flamelet model. All chemistry modeling approaches considered include a mixture fraction for devolatilization products, and a normalized enthalpy deficit as parameters, with scalar dissipation rate as an additional input parameter for the flamelet model. Data used as a basismore » for the study is generated through One-Dimensional Turbulence (ODT) simulation of a coal combustion system in which high-fidelity models are used for gas phase chemistry and devolatilization. The flamelet model was found to be more accurate than both the equilibrium and Burke-Schumann models over a majority of parameter space. The performance of all models considered declined substantially for sufficiently fuel-rich conditions. Furthermore, the analysis in this work indicate that both differential diffusion and speciation of volatiles have a perceptible negative impact on flamelet reconstructions, though the effect of differential diffusion was more substantial.« less