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Heterojunctions of semiconductors and metals are the fundamental building blocks of modern electronics. Coherent heterostructures between dissimilar materials can be achieved by composition, doping...

Polyacetylene materials, particularly two-dimensional polyacetylene thin films, have widespread applications in modern electronics and energy harvesting devices. One way to fabricate polyacetylene ...

With the rapid development of multimedia technology, considerable achievement has been achieved in image retrieval technology. On the one hand, users’ demand for cross-modal retrieval is increasing rapidly. On the other hand, deep hashing algorithms as one of the most prominent high-dimensional reduction methods have received extensive attention. Under such background, the cross-modal retrieval method based on deep hashing came into being. Although the cross-modal hashing learning method has moved a long way in recent years, it still has space of promotion. First, existing cross-modal based hashing algorithms can only map the feature vectors into binary codes with a fixed length. The length of hash codes cannot be modified according to the practical situation. Second, the saturation level of the probability function used in existing methods is too high to concentrate relevant samples very well, which results in low retrieval efficiency. To solve the above problem, a novel variable-length hash code based on adaptive weight was proposed in this paper. The length of hash codes could be adjusted according to the importance of each bit. And a novel probability function based on Cauchy distribution was proposed to generate compact binary codes and make hashing retrieval more efficient. The experiment shows that the proposed cross-modal image retrieval algorithm based on deep hashing outperforms existing related algorithms on the accuracy of retrieval.

The functional implementation of graphene as a solid boundary lubricant requires the ability to control its frictional response across a variety of interfaces. This is challenging, as being a single atomic layer thick, the nanotribological properties of graphene depend highly on the competing interaction strengths with the converse sides of the top and bottom contacts of the interfaces it is placed in between. One method to modulate these interactions is to tune the surface chemistry (of one or both counter-faces) with self-assembled monolayers (SAMs). To fully understand the effects on the graphene/SAM (G-SAM) composite interfaces formed, however, first necessitates a basic understanding of graphene–SAM interactions. To explore graphene–SAM adhesive and frictional interactions over a range of chemical functionalities, SAMs were used to functionalize atomic force microscopy (AFM) tips with varying terminal end-groups (−NH2, −CH3, and –phenyl, compared to unfunctionalized −OH terminated reference tips). AF...

© 2018 American Chemical Society. Molybdenum disulfide (MoS2) is a semiconducting transition metal dichalcogenide that is known to be a catalyst for both the hydrogen evolution reaction (HER) as well as for hydro-desulfurization (HDS) of sulfur-rich hydrocarbon fuels. Specifically, the edges of MoS2nanostructures are known to be far more catalytically active as compared to unmodified basal planes. However, in the absence of the precise details of the geometric and electronic structure of the active catalytic sites, a rational means of modulating edge reactivity remain to be developed. Here we demonstrate using first-principles calculations, X-ray absorption spectroscopy, as well as scanning transmission X-ray microscopy (STXM) imaging that edge corrugations yield distinctive spectroscopic signatures corresponding to increased localization of hybrid Mo 4d states. Independent spectroscopic signatures of such edge states are identified at both the S L2,3and S K-edges with distinctive spatial localization of such states observed in S L2,3-edge STXM imaging. The presence of such low-energy hybrid states at the edge of the conduction band is seen to correlate with substantially enhanced electrocatalytic activity in terms of a lower Tafel slope and higher exchange current density. These results elucidate the nature of the edge electronic structure and provide a clear framework for its rational manipulation to enhance catalytic activity.

Acetic acid (HAc) aqueous was used as solvent in wetness impregnation to prepare CeO2-modified γ-Al2O3 support. With the help of HAc, the dispersion of CeO2 on γ-Al2O3 is significantly improved and the size of CeO2 nanoparticles can be controlled through tuning the concentration of HAc aqueous. XPS analysis shows that the percentages of Ce3 + in CeO2 nanoparticles will vary with the size. Then we load CuO on the as-prepared CeO2-modified γ-Al2O3 support and choose NO reduction with CO as a probe reaction to investigate the influences of impregnation solvent on the catalytic properties. The results demonstrate that the CuO/CeO2/γ-Al2O3 prepared in the solvent with volume ratio of 20:1 (H2O:HAc) has the highest activity in NO + CO reaction. Combing the structural characterizations and catalytic performances, we think that the size of the CeO2 nanoparticles should be a key factor that affects the activities of CuO/CeO2/γ-Al2O3. Furthermore, CuO dispersed on CeO2 nanoparticles with an average size of ca. 5 nm should be the highest active sites for NO + CO reaction.