Education•Beijing, Beijing, China•
About: Beijing Institute of Technology is a education organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Control theory & Laser. The organization has 57184 authors who have published 61876 publications receiving 798331 citations.
Papers published on a yearly basis
TL;DR: Study of the gas adsorption and thermal and chemical stability of two prototypical members, ZIF-8 and -11, demonstrated their permanent porosity, high thermal stability, and remarkable chemical resistance to boiling alkaline water and organic solvents.
Abstract: Twelve zeolitic imidazolate frameworks (ZIFs; termed ZIF-1 to -12) have been synthesized as crystals by copolymerization of either Zn(II) (ZIF-1 to -4, -6 to -8, and -10 to -11) or Co(II) (ZIF-9 and -12) with imidazolate-type links. The ZIF crystal structures are based on the nets of seven distinct aluminosilicate zeolites: tetrahedral Si(Al) and the bridging O are replaced with transition metal ion and imidazolate link, respectively. In addition, one example of mixed-coordination imidazolate of Zn(II) and In(III) (ZIF-5) based on the garnet net is reported. Study of the gas adsorption and thermal and chemical stability of two prototypical members, ZIF-8 and -11, demonstrated their permanent porosity (Langmuir surface area = 1,810 m 2 /g), high thermal stability (up to 550°C), and remarkable chemical resistance to boiling alkaline water and organic solvents.
TL;DR: This paper reviews significant deep learning related models and methods that have been employed for numerous NLP tasks and provides a walk-through of their evolution.
Abstract: Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.
TL;DR: This tutorial review describes the basic design concepts, the recent synthetic advancements and structural studies, and the frontiers of functional exploration of covalent organic frameworks.
Abstract: Covalent organic frameworks (COFs) are a class of crystalline porous polymers that allow the atomically precise integration of organic units to create predesigned skeletons and nanopores. They have recently emerged as a new molecular platform for designing promising organic materials for gas storage, catalysis, and optoelectronic applications. The reversibility of dynamic covalent reactions, diversity of building blocks, and geometry retention are three key factors involved in the reticular design and synthesis of COFs. This tutorial review describes the basic design concepts, the recent synthetic advancements and structural studies, and the frontiers of functional exploration.
TL;DR: This paper presents a probabilistic procedure for estimating the polymethine content of carbon dioxide using a straightforward two-step procedure, and shows good results in both the stationary and the liquid phase.
Abstract: Liming Dai,*,†,‡ Yuhua Xue,†,‡ Liangti Qu,* Hyun-Jung Choi, and Jong-Beom Baek* †Center of Advanced Science and Engineering for Carbon (Case4Carbon), Department of Macromolecular Science and Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106, United States Key Laboratory of Cluster Science, Ministry of Education of China, Beijing Key Laboratory of Photoelectronic/Electrophotonic Conversion Materials, Department of Chemistry, School of Science, Beijing Institute of Technology, Beijing 100081, People’s Republic of China School of Energy and Chemical Engineering/Center for Dimension-Controllable Covalent Organic Frameworks, Ulsan National Institute of Science and Technology (UNIST), 100 Banyeon, Ulsan, 689-798, South Korea
TL;DR: A simple electrochemical approach to luminescent and electrocatalytically active nitrogen-doped GQDs (N-GQDs) with oxygen-rich functional groups is reported, which allows them to be used for biomedical imaging and other optoelectronic applications.
Abstract: Graphene quantum dots (GQDs) represent a new class of quantum dots with unique properties. Doping GQDs with heteroatoms provides an attractive means of effectively tuning their intrinsic properties and exploiting new phenomena for advanced device applications. Herein we report a simple electrochemical approach to luminescent and electrocatalytically active nitrogen-doped GQDs (N-GQDs) with oxygen-rich functional groups. Unlike their N-free counterparts, the newly produced N-GQDs with a N/C atomic ratio of ca. 4.3% emit blue luminescence and possess an electrocatalytic activity comparable to that of a commercially available Pt/C catalyst for the oxygen reduction reaction (ORR) in an alkaline medium. In addition to their use as metal-free ORR catalysts in fuel cells, the superior luminescence characteristic of N-GQDs allows them to be used for biomedical imaging and other optoelectronic applications.
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|Georgios B. Giannakis||137||1321||73517|
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