다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Single-layer metal dichalcogenides are two-dimensional semiconductors that present strong potential for electronic and sensing applications complementary to that of graphene.

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Electronics and optoelectronics of two-dimensional transition metal dichalcogenides.

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Fabrication techniques developed for graphene research allow the disassembly of many layered crystals (so-called van der Waals materials) into individual atomic planes and their reassembly into designer heterostructures, which reveal new properties and phenomena. Andre Geim and Irina Grigorieva offer a forward-looking review of the potential of layering two-dimensional materials into novel heterostructures held together by weak van der Waals interactions. Dozens of these one-atom- or one-molecule-thick crystals are known. Graphene has already been well studied but others, such as monolayers of hexagonal boron nitride, MoS2, WSe2, graphane, fluorographene, mica and silicene are attracting increasing interest. There are many other monolayers yet to be examined of course, and the possibility of combining graphene with other crystals adds even further options, offering exciting new opportunities for scientific exploration and technological innovation. Research on graphene and other two-dimensional atomic crystals is intense and is likely to remain one of the leading topics in condensed matter physics and materials science for many years. Looking beyond this field, isolated atomic planes can also be reassembled into designer heterostructures made layer by layer in a precisely chosen sequence. The first, already remarkably complex, such heterostructures (often referred to as ‘van der Waals’) have recently been fabricated and investigated, revealing unusual properties and new phenomena. Here we review this emerging research area and identify possible future directions. With steady improvement in fabrication techniques and using graphene’s springboard, van der Waals heterostructures should develop into a large field of their own.

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Van der Waals heterostructures

Worldwide commercial interest in carbon nanotubes (CNTs) is reflected in a production capacity that presently exceeds several thousand tons per year. Currently, bulk CNT powders are incorporated in diverse commercial products ranging from rechargeable batteries, automotive parts, and sporting goods to boat hulls and water filters. Advances in CNT synthesis, purification, and chemical modification are enabling integration of CNTs in thin-film electronics and large-area coatings. Although not yet providing compelling mechanical strength or electrical or thermal conductivities for many applications, CNT yarns and sheets already have promising performance for applications including supercapacitors, actuators, and lightweight electromagnetic shields.

/pdf/carbon-nanotubes-present-and-future-commercial-applications-1nhq3xy4hh.pdf

Carbon Nanotubes: Present and Future Commercial Applications

Electrolyte Initiated Instaneous In-Situ Chemical Polymerization of Organic Cathodes for Ultralong-Cycling Magnesium Ion Batteries

Many kinds of real-world multi-way signal, like color images, videos, etc, are represented in tensor form and may often be corrupted by outliers To recover an unknown signal tensor corrupted by outliers, tensor robust principal component analysis (TRPCA) serves as a robust tensorial modification of the fundamental PCA Recently, a successful TRPCA model based on the tubal nuclear norm (TNN) (Lu et al in IEEE Trans Pattern Anal Mach Intell 42:925–938, 2019) has attracted much attention thanks to its superiority in many applications However, TNN is computationally expensive due to the requirement of full singular value decompositions, seriously limiting its scalability to large tensors To address this issue, we propose a new TRPCA model which adopts a factorization strategy Algorithmically, an algorithm based on the non-convex augmented Lagrangian method is developed with convergence guarantee Theoretically, we rigorously establish the sub-optimality of the proposed algorithm We also extend the proposed model to the robust tensor completion problem Both the effectiveness and efficiency of the proposed algorithm is demonstrated through extensive experiments on both synthetic and real data sets

A faster tensor robust PCA via tensor factorization

With the view of achieving a better performance in task assignment and load-balancing, a top-level designed forecasting system for predicting computational times of density-functional theory (DFT)/...

Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse Ansatz via Machine Learning and Cheminformatics

Salient object detection can identify the most distinctive objects in a scene. In this study, a novel graph-based approach is proposed to detect a salient object via reliable boundary seeds and saliency refinement. A natural image is firstly mapped to a graph with superpixels as nodes. Saliency information is then diffused over the graph using seeds. For the reason that the boundary nodes may contain salient nodes, it is not appropriate to use all boundary nodes as the background seeds. Therefore, a boundary saliency measurement is proposed to obtain more accurate background seeds. After that, the information of background seeds is diffused by a two-stage scheme. A background-based map and a foreground-based map are generated based on the two-stage scheme. Furthermore, in order to enhance the detection accuracy, a refinement model is presented to fuse the information of background-based and foreground-based maps. Experiments on seven public datasets show the proposed algorithm out-performs the state-of-the-art salient object detection algorithms.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2018.5013

Salient object detection via reliable boundary seeds and saliency refinement

Electrochemical conversion of CO2 into high-value-added chemicals has been considered a promising route to achieve carbon neutrality and mitigate the global greenhouse effect. However, the lack of highly efficient electrocatalysts has limited its practical application. Herein, we propose an ultrafast and green electric explosion method to batch-scale prepare spherical indium (In) nanocrystals (NCs) with abundant metal defects toward high selective electrocatalytic CO2 reduction (CO2RR) to HCOOH. During the electric explosion synthesis process, the Ar atmosphere plays a significant role in forming the spherical In NCs with abundant metal defects instead of highly crystalline In2O3 NCs formed under an air atmosphere. Analysis results reveal that the In NCs possess ultrafast catalytic kinetics and reduced onset potential, which is ascribed to the formation of rich metal defects serving as effective catalytic sites for converting CO2 into HCOOH. This work provides a feasible strategy to massively produce efficient In-based electrocatalysts for electrocatalytic CO2-to-formate conversion.

Zhong Jin

Papers

Electrolyte Initiated Instaneous In-Situ Chemical Polymerization of Organic Cathodes for Ultralong-Cycling Magnesium Ion Batteries

A faster tensor robust PCA via tensor factorization

Forecasting System of Computational Time of DFT/TDDFT Calculations under the Multiverse Ansatz via Machine Learning and Cheminformatics

Salient object detection via reliable boundary seeds and saliency refinement

Rapid and Green Electric-Explosion Preparation of Spherical Indium Nanocrystals with Abundant Metal Defects for Highly-Selective CO2 Electroreduction.