In silico screening of carbon-capture materials
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Here, BN nanosheets were functionalized with polyethyleneimine (PEI) which is rich in amine density, through simple impregnation to improve their performance for CO 2 capture.
An improved method for measuring the cross sections for carrier trapping at defects in semiconductors is described. This method, a variation of deep level transient spectroscopy (DLTS) used with bipolar transistors, is applied to hot carrier trapping at vacancy-oxygen, carbon-oxygen, and three charge states of divacancy centers (V{sub 2}) in n- and p-type silicon. Unlike standard DLTS, we fill traps by injecting carriers into the depletion region of a bipolar transistor diode using a pulse of forward bias current applied to the adjacent diode. We show that this technique is capable of accurately measuring a wide range of capture cross sections at varying electric fields due to the control of the carrier density it provides. Because this technique can be applied to a variety of carrier energy distributions, it should be valuable in modeling the effect of radiation-induced generation-recombination currents in bipolar devices.
In this paper, we present a reinforcement-learning based distributed approach to wind farm energy capture maximization using yaw control, also known as wake steering. In order to maximize the power output of a wind farm, it is often necessary for individual turbines to decrease their own power output through yaw misalignment so as to deflect their wakes away from downstream turbines. Although using model-based methods to achieve yaw misalignment is one option, a model-free method might be better suited to incorporate factors that are difficult to model or changing conditions. We propose an algorithm that adapts concepts of temporal difference reinforcement learning distributed to a multi-agent environment that allows individual turbines to act so as to optimize overall wind farm output and react to unforeseen disturbances.
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The paper develops a real-time vehicle-specific eco-routing model for use in on-board navigation systems for both conventional fuel- and battery-powered electric vehicles. The model uses a novel link cost function that retains all microscopic transient behavior along a link by capturing within-link speed, acceleration, and road grade variations. Specifically, eight vehicle-agnostic link-specific variables are transmitted to the cloud and fused with information received from other connected vehicles. The fused data are then sent back to the connected vehicles and used to compute vehicle-specific eco-routes (edge computing). The solution produces an energy-efficient dynamic user-equilibrium traffic assignment that is solved incrementally. A numerical experiment is first designed to test the model, demonstrating the benefits associated with vehicle-specific eco-routes compared to those produced by traditional models. The model is then implemented in a micro-simulation framework to evaluate the network-wide impacts of the proposed energy-efficient user-optimum incremental traffic assignment for different traffic demand levels. Results demonstrate that the proposed model produces lower network-wide energy consumption levels compared to traditional energy-efficient and travel time user-optimum traffic assignment methods for both conventional fuel and electric vehicles. Conventional fuel savings relative to travel time routing decrease with increasing traffic demand levels, while electric power savings do not vary monotonically with congestion levels. Energy savings relative to traditional eco-routing are also demonstrated to not vary monotonically with demand levels. Results of the study also demonstrate that the transportation network layout significantly affects the eco-routing system performance. Finally, the feasibility of the proposed model in real-world applications is discussed and presented.
Iodine radioisotopes released during nuclear fuel reprocessing must be removed from the off-gas stream before discharge. One promising material for iodine capture is reduced silver mordenite (Ag 0 Z). Nevertheless, the adsorbent’s capacity will degrade, or age, over time when the material is exposed to other off-gas constituents. Though the overall impact of aging is known, the underlying physical and chemical processes are not. To examine these processes, Ag 0 Z samples were prepared and aged in 2% NO 2 in dry air and in 1% NO in N 2 gas streams at 150 °C for up to six months. Aged samples were then characterized using scanning electron microscopy, X-ray diffraction, Raman spectroscopy, and X-ray absorption spectroscopy. These techniques show that aging involves two overarching processes: (i) oxidation of the silver nanoparticles present in Ag 0 Z and (ii) migration of oxidized silver into the mordenite’s inner network. Silver on the nanoparticle’s surface is oxidized through adsorption of O 2 , NO, and NO 2 . Additionally, Raman spectroscopy and X-ray absorption spectroscopy indicate that nitrates are the primary products of this adsorption. Most of these nitrates migrate into the interior of the mordenite and exchange at framework binding sites, returning silver to its unreduced state (AgZ). The remaining nitrates exist at a persistent concentration without aggregating into bulk-phase AgNO 3 . X-ray absorption spectroscopy results further indicate that iodine adsorption occurs on not just Ag 0 Z but also on AgZ and a portion of the nitrates in the system. AgZ adsorbs a sizable quantity of iodine early in the aging process, but its capacity drops rapidly over time. For well-aged samples, nitrates are responsible for up to 95% of mordenite’s iodine capacity. Overall, these results have enhanced our understanding of the aging process in silver mordenite and are expected to guide the development of superior adsorbents for the capture of radioactive iodine from reprocessing off-gas.
A novel method for the postcombustion capture of CO2 from coal-fired power plants has been described utilizing an aminosilicone absorbent. 1,3-Bis(3-aminopropy1)-1,1,3,3-tetramethyldsiloxane (GAP-0) rapidly transforms from a low viscosity liquid to a friable solid upon exposure to CO2 in simulated flue gas. This material has excellent thermal stability, low vapor pressure, high CO2 loading capability, and a large dynamic CO2 capacity between rich and lean solvent loadings. Preliminary plant and process models assembled from experimental data show a decrease in parasitic energy loss from 30% to 18% when compared to the benchmark monoethanolamine (MEA) process and a concomitant lowering of the cost of electricity (COE) from 74% to 44% increase versus a plant without carbon capture.
Abstract X-ray bursts are among the brightest stellar objects frequently observed in the sky by space-based telescopes. A type-I X-ray burst is understood as a violent thermonuclear explosion on the surface of a neutron star, accreting matter from a companion star in a binary system. The bursts are powered by a nuclear reaction sequence known as the rapid proton capture process (rp process), which involves hundreds of exotic neutron-deficient nuclides. At so-called waiting-point nuclides, the process stalls until a slower β + decay enables a bypass. One of the handful of rp process waiting-point nuclides is 64 Ge, which plays a decisive role in matter flow and therefore the produced X-ray flux. Here we report precision measurements of the masses of 63 Ge, 64,65 As and 66,67 Se—the relevant nuclear masses around the waiting-point 64 Ge—and use them as inputs for X-ray burst model calculations. We obtain the X-ray burst light curve to constrain the neutron-star compactness, and suggest that the distance to the X-ray burster GS 1826–24 needs to be increased by about 6.5% to match astronomical observations. The nucleosynthesis results affect the thermal structure of accreting neutron stars, which will subsequently modify the calculations of associated observables.
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Chemists, engineers, scientists, lend us your ears… Carbon capture, utilisation, and storage (CCUS) is among the largest challenges on the horizon and we need your help.
Integration of renewables-assisted carbon capture with existing fossil power plants is an economical grid decarbonization technique and renewable intermittency solution.
The Detector for Advanced Neutron Capture Experiments (DANCE) has been used for extensive studies of neutron capture, gamma decay, photon strength functions, and prompt and delayed fission-gamma emission. Despite these successes, the potential measurements have been limited by the data acquisition hardware. We report on a major upgrade of the DANCE data acquisition that simultaneously enables strait-forward coupling to auxiliary detectors, including high-resolution high-purity germanium detectors and neutron tagging array. The upgrade will enhance the time domain accessible for time-of-flight neutron measurements as well as improve the resolution in the DANCE barium fluoride crystals for photons.
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Gravitational lensing of distant galaxies can be exploited to infer the convergence field as a function of angular position on the sky. The statistics of this field, much like that of the cosmic microwave background (CMB), can be studied to extract information about fundamental parameters in cosmology, most notably the dark energy in the universe. Unlike the CMB, the distribution of matter in the universe which determines the convergence field is highly non-Gaussian, reflecting the nonlinear processes that accompanied structure formation. Much of the cosmic information contained in the initial field is therefore unavailable to the standard power spectrum measurements. Here we propose a method for re-capturing cosmic information by using the power spectrum of a simple function of the observed (nonlinear) convergence field. We adapt the approach of Neyrinck et al. to lensing by using a modified logarithmic transform of the convergence field. The Fourier transform of the log-transformed field has modes that are nearly uncorrelated, which allows for additional cosmological information to be extracted from small-scale modes.