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γ‐Graphyne is a new nanostructured carbon material with large theoretical Li+ storage due to its unique large conjugate rings, which makes it a potential anode for high‐capacity lithium‐ion batteries (LIBs). In this work, γ‐graphyne‐based high‐capacity LIBs are demonstrated experimentally. γ‐Graphyne is synthesized through mechanochemical and calcination processes by using CaC2 and C6Br6. Brunauer–Emmett–Teller, atomic force microscopy, X‐ray photoelectron spectroscopy, solid‐state 13C NMR and Raman spectra are conducted to confirm its morphology and chemical structure. The sample presents 2D mesoporous structure and is exactly composed of sp and sp2‐hybridized carbon atoms as the γ‐graphyne structure. The electrode shows high Li+ storage (1104.5 mAh g−1 at 100 mA g−1) and rate capability (435.1 mAh g−1 at 5 A g−1). The capacity retention can be up to 948.6 (200 mA g−1 for 350 cycles) and 730.4 mAh g−1 (1 A g−1 for 600 cycles), respectively. These excellent electrochemical performances are ascribed to the mesoporous architecture, large conjugate rings, enlarged interplanar distance, and high structural integrity for fast Li+ diffusion and improved cycling stability in γ‐graphyne. This work provides an environmentally benign and cost‐effective mechanochemical method to synthesize γ‐graphyne and demonstrates its superior Li+ storage experimentally.

LiNiCoMnO (NCM) is regarded as a promising material for next-generation lithium ion batteries due to the high capacity, but its practical applications are limited by the poor electronic conductivity. Here, a one-step method is used to prepare carbon coated LiNiCoMnO (NCM/C) by applying active carbon as reaction matrix. TEM shows LiNiCoMnO particles are homogeneously coated by carbon with a thickness about 10 nm. NCM/C delivers the discharge capacity of 191.2 mAh g at 0.5 C (85 mA g) with a columbic efficiency of 91.1%. At 40 C (6800 mA g), the discharge capacity of NCM/C is 54.6 mAh g, whereas NCM prepared through sol-gel route only delivers 13.2 mAh g. After 100 charge and discharge cycles at 1 C (170 mA g) the capacity retention is 90.3% for NCM/C, whereas it is only 72.4% for NCM. The superior charge/discharge performance of NCM/C owes much to the carbon coating layer, which is not only helpful to increase the electronic conductivity but also contributive to inhibit the side reactions between LiNiCoMnO and the liquid electrolyte.

2D γ-graphyne is mechanosynthesized by using benzene and its effective oxygen evolution reaction performance is demonstrated.

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High energy density is the major demand for next‐generation rechargeable batteries, while the intrinsic low alkali metal adsorption of traditional carbon–based electrode remains the main challenge. Here, the mechanochemical route is proposed to prepare nitrogen doped γ‐graphyne (NGY) and its high capacity is demonstrated in lithium (LIBs)/sodium (SIBs) ion batteries. The sample delivers large reversible Li (1037 mAh g−1) and Na (570.4 mAh g−1) storage capacities at 100 mA g−1 and presents excellent rate capabilities (526 mAh g−1 for LIBs and 180.2 mAh g−1 for SIBs) at 5 A g−1. The superior Li/Na storage mechanisms of NGY are revealed by its 2D morphology evolution, quantitative kinetics, and theoretical calculations. The effects on the diffusion barriers (Eb) and adsorption energies (Ead) of Li/Na atoms in NGY are also studied and imine‐N is demonstrated to be the ideal doping format to enhance the Li/Na storage performance. Besides, the Li/Na adsorption routes in NGY are optimized according to the experimental and the first‐principles calculation results. This work provides a facile way to fabricate high capacity electrodes in LIBs/SIBs, which is also instructive for the design of other heteroatomic doped electrodes.

Data heterogeneity is the obstacle for the resource sharing on Semantic Web (SW), and ontology is regarded as a solution to this problem. However, since different ontologies are constructed and maintained independently, there also exists the heterogeneity problem between ontologies. Ontology matching is able to identify the semantic correspondences of entities in different ontologies, which is an effective method to address the ontology heterogeneity problem. Due to huge memory consumption and long runtime, the performance of the existing ontology matching techniques requires further improvement. In this work, an extended compact genetic algorithm-based ontology entity matching technique (ECGA-OEM) is proposed, which uses both the compact encoding mechanism and linkage learning approach to match the ontologies efficiently. Compact encoding mechanism does not need to store and maintain the whole population in the memory during the evolving process, and the utilization of linkage learning protects the chromosome’s building blocks, which is able to reduce the algorithm’s running time and ensure the alignment’s quality. In the experiment, ECGA-OEM is compared with the participants of ontology alignment evaluation initiative (OAEI) and the state-of-the-art ontology matching techniques, and the experimental results show that ECGA-OEM is both effective and efficient.