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Lithium-ion batteries with high energy density and good safety are in urgent demand nowadays. Silicon has the advantages of high theoretical capacity, low cost, and vast reserves. It is regarded as an ideal anode material for high-energy lithium-ion batteries. Unfortunately, its huge volume change during cycling results in unstable cycle behavior and limits its application. To develop a robust electrode by employing effective binders is still a big challenge. Furthermore, both conventional binders and organic liquid electrolytes bring serious safety risks due to their flammability. Herein, a flame-retardant binder is designed and prepared by cross-linking polyacrylic acid (PAA) with a flame-retardant epoxy resin (FREP) containing phosphorus and nitrogen elements. Compared with PAA, it has better wettability to liquid electrolytes. Moreover, the three-dimensional PAA-FREP polymer binder not only provides sufficient mechanical strength to buffer the volume change of Si powders but also enhances the interfacial adhesion between the active film and Cu current collector via epoxy groups. Most importantly, the FREP component in the binder leads to admirable flame retardance. In these ways, the multifunctional binder exhibits excellent mechanical, electrochemical, and safety performances.

Mining maximal frequent itemsets is an important research topic in data mining. The existing FPMAX algorithm for mining maximal frequent itemsets based on FP-tree necessitate to construct conditional subset trees recursively. While the amount of frequent itemsets is large, it will cause huge consumption both in terms of runtime and memory. Therefore, this paper proposes an improved algorithm FPMAX-direct for mining maximal frequent itemsets based on FP-tree. This novel algorithm will simplify the construction of the conditional subset trees by adding directly the branch of FP-tree whose support is no less than a user-specified minimum support threshold to the maximal frequent candidate itemsets. Experimental results show that FPMAX-direct algorithm outperforms FPMAX algorithm in dense datasets.

Antibiotics are often misused or overused, resulting in large residue inputs in the environment. Electricity and antibiotics were regarded as potentially important factors, which impact on the microbial community during treatment of antibiotics in three-dimensional biofilm-electrode reactors (3D-BERs). Unfortunately, only a few studies have been reported yet. Four 3D-BERs and one 3D-BR (reactor with biological sludge and no voltage) were designed to assess the effect of low current, sulfamethoxazole (SMX), and tetracycline (TC) on microbial populations. The 3D-BERs achieved excellent removal efficiencies of 72.20–93.52 and 82.61–95.80% for SMX and TC, respectively. Microorganisms were classified into 58 phyla, 125 classes, 166 orders, 187 families, and 220 genera. Proteobacteria held the overwhelming predominance, followed by Bacteroidetes, Chloroflexi, Actinobacteria, Verrucomicrobia, Firmicutes, and Acidobacteria. The 3D-BERs achieved higher richness of microbial composition compared with the 3D-BR. Microbial communities and relative abundance at the phyla level were affected by low current. The microbial communities in the 3D-BERs were similar at the genus level, even with different antibiotic concentrations. However, the relative abundances and compositions of the microbial communities decreased during the treatment of antibiotics. To increase the performance of 3D-BERs, the function of microorganisms should be investigated in future studies.