Site suitability assessment for the development of wind power plant in Wolaita area, Southern Ethiopia: an AHP-GIS model.

The primary driver of economic growth is energy, predominantly derived from fossil fuels, the demand for which has experienced a significant increase since the advent of the Industrial Revolution. The emissions of hazardous gases resulting from the utilization of these fuels have been well acknowledged, therefore exerting a notable impact on the environment. In the context of Ethiopia, it is observed that despite the presence of ample renewable resources, the accessibility to power continues to be constrained. In order to effectively tackle this issue, it is imperative to redirect attention towards the utilization of renewable sources, such as wind energy, as a means of enhancing the existing power grid infrastructure. The present study used geospatial tools to evaluate the appropriateness of the Wolayita region for the establishment of a wind power facility. The process of site selection is guided by multiple factors, and a multi-criteria approach is facilitated through the utilization of Geographic Information System (GIS). The evaluation of seven characteristics was conducted utilizing the Analytical Hierarchy Process (AHP) methodology, which involved pairwise comparisons and weighted scoring. The process of suitability mapping involves the classification of locations into four distinct categories, which range from the most suitable to the least suitable. The findings demonstrate that the area of 0.628% (28.00 km2) is deemed the most suitable, while 54.61% (2433.96 km2) is considered somewhat acceptable. Additionally, 0.85% (37.85 km2) is identified as the least suitable, leaving a remaining 43.91% (1060.00 km2) that is deemed unsuitable. The central, northwestern, and southern regions are identified as optimal geographic areas. The results of this study facilitate the process of investing in renewable energy, thereby assisting Ethiopian authorities and organizations in promoting sustainable development. This report serves as a crucial reference point for the wind energy industry.

Advancing energy storage through solubility prediction: leveraging the potential of deep learning.

Solubility prediction plays a crucial role in energy storage applications, such as redox flow batteries, because it directly affects the efficiency and reliability. Researchers have developed various methods that utilize quantum calculations and descriptors to predict the aqueous solubilities of organic molecules. Notably, machine learning models based on descriptors have shown promise for solubility prediction. As deep learning tools, graph neural networks (GNNs) have emerged to capture complex structure-property relationships for material property prediction. Specifically, MolGAT, a type of GNN model, was designed to incorporate n-dimensional edge attributes, enabling the modeling of intricacies in molecular graphs and enhancing the prediction capabilities. In a previous study, MolGAT successfully screened 23 467 promising redox-active molecules from a database of over 500 000 compounds, based on redox potential predictions. This study focused on applying the MolGAT model to predict the aqueous solubility (log S) of a broad range of organic compounds, including those previously screened for redox activity. The model was trained on a diverse sample of 8494 organic molecules from AqSolDB and benchmarked against literature data, demonstrating superior accuracy compared with other state of the art graph-based and descriptor-based models. Subsequently, the trained MolGAT model was employed to screen redox-active organic compounds identified in the first phase of high-throughput virtual screening, targeting favorable solubility in energy storage applications. The second round of screening, which considered solubility, yielded 12 332 promising redox-active and soluble organic molecules suitable for use in aqueous redox flow batteries. Thus, the two-phase high-throughput virtual screening approach utilizing MolGAT, specifically trained for redox potential and solubility, is an effective strategy for selecting suitable intrinsically soluble redox-active molecules from extensive databases, potentially advancing energy storage through reliable material development. This indicates that the model is reliable for predicting the solubility of various molecules and provides valuable insights for energy storage, pharmaceutical, environmental, and chemical applications.

High-Throughput Screening of Promising Redox-Active Molecules with MolGAT.

Redox flow batteries (RFBs) have emerged as a promising option for large-scale energy storage, owing to their high energy density, low cost, and environmental benefits. However, the identification of organic compounds with high redox activity, aqueous solubility, stability, and fast redox kinetics is a crucial and challenging step in developing an RFB technology. Density functional theory-based computational materials prediction and screening is a time-consuming and computationally expensive technique, yet it has a high success rate. To speed up the discovery of new materials with desired properties, machine-learning-based models can be trained on large data sets. Graph neural networks (GNNs) are particularly well-suited for non-Euclidean data and can model complex relationships, making them ideal for accelerating the discovery of novel materials. In this study, a GNN-based model called MolGAT was developed to predict the redox potential of organic molecules using molecular structures, atomic properties, and bond attributes. The model was trained on a data set of over 15,000 compounds with redox potentials ranging from -4.11 to 2.56. MolGAT outperformed other GNN variants, such as the Graph Attention Network, Graph Convolution Network, and AttentiveFP models. The trained model was used to screen a vast chemical data set comprising 581,014 molecules, namely OMDB, QM9, ZINC, CHEMBL, and DELANEY, and identified 23,467 potential redox-active compounds for use in redox flow batteries. Of those, 20,716 molecules were identified as potential catholytes with predicted redox potentials up to 2.87 V, while 2,751 molecules were deemed potential anolytes with predicted redox potentials as low as -2.88 V. This work demonstrates the capabilities of graph neural networks in condensed matter physics and materials science to screen promising redox-active species for further electronic structure calculations and experimental testing.

Fast 3D-lithium-ion diffusion and high electronic conductivity of Li2MnSiO4 surfaces for rechargeable lithium-ion batteries.

High theoretical capacity, high thermal stability, the low cost of production, abundance, and environmental friendliness are among the potential attractiveness of Li2MnSiO4 as a positive electrode (cathode) material for rechargeable lithium-ion batteries. However, the experimental results indicated poor electrochemical performance in its bulk phase due to high intrinsic charge transfer resistance and capacity fading during cycling, which limit its large-scale commercial applications. Herein, we explore the surface stability and various lithium-ion diffusion pathways of Li2MnSiO4 surfaces using the density functional theory (DFT) framework. Results revealed that the stability of selected surfaces is in the following order: (210) > (001) > (010) > (100). Moreover, the Wulff-constructed equilibrium shape revealed that the Li2MnSiO4 (001) surface is the most predominant facet, and thus, preferentially exposed to electrochemical activities. The Hubbard-corrected DFT (DFT + U, with U = 3 eV) results indicated that the bulk insulator with a wide band gap (E g = 3.42 eV) changed into narrow electronic (E g = 0.6 eV) when it comes to the Li2MnSiO4 (001) surface. Moreover, the nudged elastic band analysis shows that surface diffusion along the (001) channel was found to be unlimited and fast in all three dimensions with more than 12-order-of-magnitude enhancements compared with the bulk system. These findings suggest that the capacity limitation and poor electrochemical performance that arise from limited electronic and ionic conductivity in the bulk system could be remarkably improved on the surfaces of the Li2MnSiO4 cathode material for rechargeable lithium-ion batteries.