5 research outputs found

    High Throughput Screening of Organic Electrode Materials for Lithium Battery by Theoretical Method

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    Screening the appropriate organic electrode material of a lithium battery from the organic structure database by the theoretical method efficiently is crucial for the further experimental study. Unfortunately, the density functional theory is not appropriate due to that it fails to calculate the van der Waals interaction between the organic molecules. In this work, dispersion-corrected density functional theory (DFT-D2) was applied to study nine experimentally reported organic electrode materials, and the theoretical method successfully predicted their potentials, which suggests that it is a feasible method to search and investigate the organic electrode material. The method is further applied to investigate 31 organic crystallines selected from the CCDC (Cambridge Crystallographic Data Centre) database. The theoretical results show that the potentials range from 0.01 eV to 2.76 V, while the capacities distribute from 150 to 623 mAh·g<sup>–1</sup>, and most of the band gaps are smaller than 2.5 eV, which indicates that they are typical organic semiconductors with high electronic conductivity. The materials with a relatively high potential, high capacity, and small band gap are highligthed, including BAKGOJ, MEHROH, SUQDEN, and NUXGIW, which may be further investigated by experimenters

    Study of Lithium Migration Pathways in the Organic Electrode Materials of Li-Battery by Dispersion-Corrected Density Functional Theory

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    Organic materials have been considered a promising alternative as electrodes for rechargeable lithium-ion batteries. However, there are some obvious shortcomings, especially poor dynamics performance. Approaches to understand the reason for the poor dynamic performance are the main point of the present work. In this paper, an organic electrode material,C<sub>12</sub>H<sub>4</sub>N<sub>4</sub>, is selected as a sample, and studied by dispersion-corrected density functional theory (DFT-D2). The calculation results show that the band gaps of delithiated and lithiated states are about 0.9 and 1.0 eV, respectively, which is consistent with the conventional conjugated organic materials implying the good electronic conductivity. The Li-ion migration pathway forms a complicated three-dimensional (3D) network. The migration energy barrier is higher than 0.53 eV, which is obviously higher than that of the inorganic electrode material, demonstrating the poor ionic conductivity. In organic materials, although the steric hindrance is lowered due to the large intermolecular space, the coulomb potential is significantly improved at the same time, which is the main reason for the high energy barrier of Li-ion migration. Effective ways to lower the lithium migration energy barrier and improve the ionic conductivity should be considered when synthesizing new organic electrode materials

    Waste PET Plastic-Derived CoNi-Based Metal–Organic Framework as an Anode for Lithium-Ion Batteries

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    Recycling waste PET plastics into metal–organic frameworks is conducive to both pollution alleviation and sustainable economic development. Herein, we have utilized waste PET plastic to synthesize CoNi-MOF applied to lithium battery anode materials via a low-temperature solvothermal method for the first time. The preparation process is effortless, and the sources’ conversion rate can reach almost 100%. In addition, the anode performance of MOFs with various Co/Ni mole ratios was investigated. The as-synthesized Co0.8Ni-MOF exhibits excellent crystallinity, purity, and electrochemical performance. The initial discharge and charge capacities are 2496 and 1729 mAh g–1, respectively. Even after 200 cycles, the Co0.8Ni-MOF electrode can exhibit a high Coulombic efficiency of over 99%. Consequently, given the environmental and economic benefits, the Co0.8Ni-MOF derived from waste PET plastic is thought to be an appealing anode material for lithium-ion batteries

    Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction

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    Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein–ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein–ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein–ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein–ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed

    Prediction of Carbon Dioxide Reduction Catalyst Using Machine Learning with a Few-Feature Model: WLEDZ

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    Cu-based alloy catalysts are widely used in the field of carbon dioxide reduction reaction (CO2RR), due to the good selectivity and low overpotential. In order to achieve efficient exploration of alloy catalysts for CO2RR, a machine learning (ML) model, based on a gradient boosting regression (GBR) algorithm, is developed. By implementing a rigorous feature selection process, the dimensionality of feature space is reduced from thirteen to five, including work function (W), local electronegativity (Loc_EN), electronegativity (EN), interplanar spacing (D), and atomic number (Z), which is referred to as the WLEDZ model. The few-feature model has a high performance as that with many features, and the ML model successfully and rapidly predicts the adsorption energy of the key intermediates (HCOO, CO, and COOH) in the CO2RR process. In addition, eight Cu-based bimetallic catalysts are predicted with highly promising alternatives. This demonstrates that the WLEDZ few-feature ML model can screen highly promising bimetallic alloy for CO2RR and can also be used for the design of other types of catalysts
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