A three-dimensional network of graphene/silicon/graphene sandwich sheets as anode for Li-ion battery

Abstract(#br)A freestanding porous three-dimensional (3D) network composed of graphene/silicon/graphene sandwich sheets is proposed to prevent the expansion induced pulverization for Si-based anode in a lithium-ion battery. The architecture ensures the attachment of Si active material, improves the conductivity, and absorbs the Si volume expansions. The 3D Graphene and Si in this architecture work synergistically to contribute to the capacity, while the nanoscale of Si lowers the expansion during lithiation. And the 3D graphene with an interconnected skeleton, in addition to active material, also acts as the current collector as well as a stable support for Si

Accelerating Sequence Searching: Dimensionality Reduction Method

Similarity search over long sequence dataset becomes increasingly popular in many emerging applications, such as text retrieval, genetic sequences exploring, etc. In this paper, a novel index structure, namely Sequence Embedding Multiset tree (SEM - tree), has been proposed to speed up the searching process over long sequences. The SEM-tree is a multi-level structure where each level represents the sequence data with different compression level of multiset, and the length of multiset increases towards the leaf level which contains original sequences. The multisets, obtained using sequence embedding algorithms, have the desirable property that they do not need to keep the character order in the sequence, i.e. shorter representation, but can reserve the majority of distance information of sequences. Each level of the tree serves to prune the search space more efficiently as the multisets utilize the predicability to finish the searching process beforehand and reduce the computational cost greatly. A set of comprehensive experiments are conducted to evaluate the performance of the SEM-tree, and the experimental results show that the proposed method is much more efficient than existing representative methods.Computer Science, Artificial IntelligenceComputer Science, Information SystemsSCI(E)6ARTICLE3301-3222

Squeezing Long Sequence Data for Efficient Similarity Search

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Claim: An Efficient Method for Relaxed Frequent Closed Itemsets Mining over Stream Data

Recently, frequent itemsets mining over data streams attracted much attention. However, mining closed itemsets from data stream has not been well addressed. The main difficulty lies in its high complexity of maintenance aroused by the exact model definition of closed itemsets and the dynamic changing of data streams. In data stream scenario, it is sufficient to mining only approximated frequent closed itemsets instead of in full precision. Such a compact but close-enough frequent itemset is called a relaxed frequent closed itemsets. In this paper, we first introduce the concept of RC (Relaxed frequent Closed Itemsets), which is the generalized form of approximation. We also propose a novel mechanism CLAIM, which stands for C Losed approximated Itemset Mining, to support efficiently mining of RC. The CLAIM adopts bipartite graph model to store frequent closed itemsets, use Bloom filter based hash function to speed up the update of drifted itemsets, and build a compact HR-tree structure to efficiently maintain the RCs and support mining process. An experimental study is conducted, and the results demonstrate the effectiveness and efficiency of our approach at handling frequent closed itemsets mining for data stream. ? Springer-Verlag Berlin Heidelberg 2007.EI664-6754443 LNC

Selective photoelectrochemical oxidation of glucose to glucaric acid by single atom Pt decorated defective TiO2

Photoelectrochemical oxidation provides a promising strategy for glucaric acid production. Here, selective oxidation of glucose to glucaric acid is realized on the photoanode of defective TiO2 decorated with single-atom Pt via a photoelectrochemical strategy

Interface-coupling of CoFe-LDH on MXene as high-performance oxygen evolution catalyst

Oxygen evolution reaction (OER) is the bottleneck reaction of the overall water splitting process despite the intensive research in the past decades. Efficient yet stable low-cost OER catalysts have been widely explored but further improvement is still highly demanded. Herein, a type of hybrid OER catalyst was prepared by the growth of CoFe-LDH (layered double hydroxide) on the surface of Ti 3 C 2 MXene nanosheets, which exhibits superior OER performance than the state-of-the-art RuO 2 . The enhancement of the OER performance could be attributed to the combination of oxygen-breaking ability of CoFe-LDH and metallic conductivity of Ti 3 C 2 MXene substrate. Meanwhile, the direct growth of CoFe-LDH on the hydroxyl-rich surface of MXene effectively prevents itself from aggregation, exposing more CoFe-LDH edge active sites. What\u27s more important is that the intimate interface between CoFe-LDH and Ti 3 C 2 MXene brings in efficient charge transfer and oxygen activation, which is supported by the DFT calculation results. The direct growth of CoFe-LDH on MXene endows the insulating LDH with metallic features with the O 2p states become distributed above the Fermi level which is mediated by the possible anionic redox process. This work demonstrates the great potential of MXene-based hybrid nanostructure for energy conversion applications