Two-Dimensional Ultrathin Fe3Sn2 Kagome Metal with Defect-Dependent Magnetic Property.

Two-dimensional (2D) Fe3Sn2, which is a room-temperature ferromagnetic kagome metal, has potential applications in spintronic devices. However, the systematic synthesis and magnetic study of 2D Fe3Sn2 single crystals have rarely been reported. Here we have synthesized 2D hexagonal and triangular Fe3Sn2 nanosheets by controlling the amount of FeCl2 precursors in the chemical vapor deposition (CVD) method. It is found that the hexagonal Fe3Sn2 nanosheets exist with Fe vacancy defects and show no obvious coercivity. While the triangular Fe3Sn2 nanosheet has obvious hysteresis loops at room temperature, its coercivity first increases and then remains stable with an increase in temperature, which should result from the competition of the thermal activation mechanism and spin direction rotation mechanism. A first-principles calculation study shows that the Fe vacancy defects in Fe3Sn2 can increase the distances between Fe atoms and weaken the ferromagnetism of Fe3Sn2. The resulting 2D Fe3Sn2 nanosheets provide a new choice for spintronic devices.

Sustainable energy solutions: Well retrofit analysis and emission reduction for a net-zero future in the Intermountain West, United States of America.

To achieve net-zero emissions by 2050, we need economic means of sequestering carbon dioxide (CO2) and reducing greenhouse gas emissions (GHG). We analyze the sequestration potential of the Intermountain West (I-West) region, US, as a primary energy transition hub through analysis of wellbore retrofit potential and emission reduction in both fugitive gas abatement and flare gas. We selected the I-West region due to its abundant energy sources and oil and gas production legacy. Preliminary analysis hints that well retrofits can breathe new life into a well at a fraction of the cost of a new drill. With millions of potential candidates in the US, even a modest fraction (1% or less) suitable for retrofit could accelerate the shift to large-scale CO2 sequestration. Fugitive gas, the unintentional release of wellbore gases such as methane, is a significant emissions source. Through conservative analysis, it is estimated that wellhead leakage alone may account for 5 million tonnes of carbon dioxide equivalent (CO2e) emissions. We conclude by assessing the CO2 emissions from flaring, which is the burning of associated gas during well operations, conservative analysis indicates flaring contributes another 2 million tonnes of CO2 emissions to the region. We find that with targeted retrofit and better controls on emissions sources, the I-West region can make a significant impact in the nation's push to become net-zero. This study outlines economic feasibility and actionable items to achieve the critical reductions in emissions and increases in sequestration necessary to attain net zero.

Modeling Ecological Constraints on a CO2 Pipeline Network.

Carbon capture, utilization, and storage (CCUS) are a critical set of strategies to decarbonize the industrial and power sectors and to mitigate global climate change. Pipeline infrastructure connecting CO2 sources and sinks, if not planned strategically, can cause environmental and social impacts by disturbing local landscapes. We investigated the impacts of these considerations on optimal CO2 pipeline routing and sink locations by modifying and leveraging an open-source CCUS infrastructure model, SimCCS. We expanded SimCCS from a cost-minimizing to a multiobjective framework, explicitly incorporating environmental protection objectives. We estimated trade-offs between private costs and environmental and social impacts. Using a version of the model focused on the southeastern United States, we modeled seven scenarios with varying weights given to environmental impacts to evaluate how the pipeline network responds to the multiobjective optimization. We found that the optimal path is sensitive to environmental and social impact considerations in that a small increase in pipeline length (and cost) significantly avoids large environmental and social impacts. We hope such a tool can be used to improve the pipeline permitting and siting processes and contribute to the achievement of decarbonization goals with minimal environmental impacts.

Phase-based design of CO2 capture, transport, and storage infrastructure via SimCCS3.0.

The design of optimal infrastructure is essential for the deployment of commercial and large-scale carbon capture and storage (CCS) technology. During the design process, it is important to consider CO2 capture and storage locations and CO2 transportation pipelines to minimize the total project cost. SimCCS, first introduced in 2009, is an integrated open-source tool to optimize CCS infrastructure. The core CCS infrastructure design problem in SimCCS is structured as a mixed-integer linear programming problem by selecting the optimal pipeline routes, searching CO2 source capture and storage locations, and determining the corresponding CO2 amounts to meet desired capture targets. Multiple important and practical features have been developed to the latest version of SimCCS, SimCCS3.0. One of these features is phase-based modeling which enables users to dynamically design the CCS infrastructure. We demonstrate the phased-based modeling capability using two CCS infrastructure optimization case studies. The results from these case studies reveal that the phase-based modeling capability in SimCCS is particularly useful to optimize the dynamic deployment of CCS projects.

Improving deep learning performance for predicting large-scale geological [Formula: see text] sequestration modeling through feature coarsening.

Physics-based reservoir simulation for fluid flow in porous media is a numerical simulation method to predict the temporal-spatial patterns of state variables (e.g. pressure p) in porous media, and usually requires prohibitively high computational expense due to its non-linearity and the large number of degrees of freedom (DoF). This work describes a deep learning (DL) workflow to predict the pressure evolution as fluid flows in large-scale 3-dimensional(3D) heterogeneous porous media. In particular, we develop an efficient feature coarsening technique to extract the most representative information and perform the training and prediction of DL at the coarse scale, and further recover the resolution at the fine scale by spatial interpolation. We validate the DL approach to predict pressure field against physics-based simulation data for a field-scale 3D geologic [Formula: see text] sequestration reservoir model. We evaluate the impact of feature coarsening on DL performance, and observe that the feature coarsening not only decreases the training time by [Formula: see text] and reduces the memory consumption by [Formula: see text], but also maintains temporal error [Formula: see text] on average. Besides, the DL workflow provides predictive efficiency with 1406 times speedup compared to physics-based numerical simulation. The key findings from this research significantly improve the training and prediction efficiency of deep learning model to deal with large-scale heterogeneous reservoir models, and thus it can also be further applied to accelerate workflows of history matching and reservoir optimization for close-loop reservoir management.

Impact of adjustment measures on reducing outpatient waiting time in a community hospital: application of a computer simulation.

BACKGROUND: As an important determinant of patient satisfaction, waiting time, has gained increasing attention in the field of health care services. The present study aimed to illustrate the distribution characteristics of waiting time in a community hospital and explore the impact of potential measures to reduce outpatient waiting time based on a computer simulation approach. METHODS: During a one-month study period in 2006, a cross-sectional study was conducted in a community hospital located in Shanghai, China. Baseline data of outpatient waiting time were calculated according to the records of registration time and payment time. A simulation technique was adopted to investigate the impact of perspective reform methods on reducing waiting time. RESULTS: Data from a total of 10,092 patients and 26,816 medical consultations were collected in the study and 19,947 medical consultations were included. The average of the total visit time for outpatients in this hospital was 43.6 minutes in the morning, 19.1 minutes in the afternoon, and 34.3 minutes for the whole day studied period. The simulation results suggested that waiting time for outpatients could be greatly reduced through the introduction of appointment system and flexible demand-orientated doctor scheduling according to the numbers of patients waiting at different time of the workday. CONCLUSION: Adoption of an appointment system and flexible management of doctor scheduling may be effective way to achieve decreased waiting time.