scholarly journals Study on Growth Behavior of WO3 Crystal Topological Structure and its Electrochromic Properties

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
◽  
◽  
Keyword(s):  
Topological Structure ◽  
Growth Behavior ◽  
Electrochromic Properties

scholarly journals Growth behavior of WO3crystal topological structure and its electrochromic properties

2022 ◽  
Vol 71 (2) ◽  
pp. 028201-028201
Author(s):  
Shao Guang-Wei ◽  
◽  
Yu Rui ◽  
Fu Ting ◽  
Chen Nan-Liang ◽  
...  
Keyword(s):  
Topological Structure ◽  
Growth Behavior ◽  
Electrochromic Properties

INDALLOY 160-190

Alloy Digest ◽  
1984 ◽  
Vol 33 (1) ◽  
Keyword(s):  
Physical Properties ◽  
Tensile Properties ◽  
Precision Measurement ◽  
Heat Treating ◽  
Spray Forming ◽  
Growth Behavior ◽  
Temperature Range

Abstract INDALLOY 160-190 is a bismth-base low-melting alloy that melts through th temperature range 160-190 F. It shrinks immediately upon solidification, grows back to zero in about one hour and then shows additional growth. This shrinkage-growth behavior makes it an ideal alloy for proof casting and precision measurement of internal dimensions. This alloy originally was developed for use by children for casting soldiers and other small objects. It performs best among the low-melting alloys for spraying in the spray forming of masks and molds and in metallizing. This datasheet provides information on composition, physical properties, hardness, and tensile properties. It also includes information on casting, heat treating, machining, and joining. Filing Code: Bi-34. Producer or source: Indium Corporation of America.

Uncovering Atherosclerotic Risk Disease Gene Based on Expression and Network Topological Structure*

2010 ◽  
Vol 37 (8) ◽  
pp. 916-922
Author(s):  
Hong WANG ◽  
Xiao-Li QU ◽  
Yan ZHAO ◽  
Jing ZHANG ◽  
Li-Na CHEN
Keyword(s):  
Topological Structure ◽  
Disease Gene

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

scholarly journals Effect of Oxygen Source on the Various Properties of SnO2 Thin Films Deposited by Plasma-Enhanced Atomic Layer Deposition

Coatings ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 692
Author(s):  
Jong Hyeon Won ◽  
Seong Ho Han ◽  
Bo Keun Park ◽  
Taek-Mo Chung ◽  
Jeong Hwan Han
Keyword(s):  
Electrical Properties ◽  
Atomic Layer Deposition ◽  
Carrier Concentration ◽  
High Mobility ◽  
Impurity Scattering ◽  
Atomic Layer ◽  
Growth Behavior ◽  
Layer Deposition ◽  
Growth Feature ◽  
O2 Plasma

Herein, we performed a comparative study of plasma-enhanced atomic layer deposition (PEALD) of SnO2 films using Sn(dmamp)2 as the Sn source and either H2O plasma or O2 plasma as the oxygen source in a wide temperature range of 100–300 °C. Since the type of oxygen source employed in PEALD determines the growth behavior and resultant film properties, we investigated the growth feature of both SnO2 PEALD processes and the various chemical, structural, morphological, optical, and electrical properties of SnO2 films, depending on the oxygen source. SnO2 films from Sn(dmamp)2/H2O plasma (SH-SnO2) and Sn(dmamp)2/O2 plasma (SO-SnO2) showed self-limiting atomic layer deposition (ALD) growth behavior with growth rates of ~0.21 and 0.07–0.13 nm/cycle, respectively. SO-SnO2 films showed relatively larger grain structures than SH-SnO2 films at all temperatures. Interestingly, SH-SnO2 films grown at high temperatures of 250 and 300 °C presented porous rod-shaped surface morphology. SO-SnO2 films showed good electrical properties, such as high mobility up to 27 cm2 V−1·s−1 and high carrier concentration of ~1019 cm−3, whereas SH-SnO2 films exhibited poor Hall mobility of 0.3–1.4 cm2 V−1·s−1 and moderate carrier concentration of 1 × 1017–30 × 1017 cm−3. This may be attributed to the significant grain boundary and hydrogen impurity scattering.

Information on evolutionary age in redundancy of complex network

2019 ◽  
Vol 33 (27) ◽  
pp. 1950331
Author(s):  
Shiguo Deng ◽  
Henggang Ren ◽  
Tongfeng Weng ◽  
Changgui Gu ◽  
Huijie Yang
Keyword(s):  
Principal Component Analysis ◽  
Complex Networks ◽  
Metabolic Network ◽  
Complex Network ◽  
Cellular Networks ◽  
Topological Structure ◽  
Quantitative Measure ◽  
Principal Component ◽  
Evolutionary Information ◽  
Local Patterns

Evolutionary processes of many complex networks in reality are dominated by duplication and divergence. This mechanism leads to redundant structures, i.e. some nodes share most of their neighbors and some local patterns are similar, called redundancy of network. An interesting reverse problem is to discover evolutionary information from the present topological structure. We propose a quantitative measure of redundancy of network from the perspective of principal component analysis. The redundancy of a community in the empirical human metabolic network is negatively and closely related with its evolutionary age, which is consistent with that for the communities in the modeling protein–protein network. This behavior can be used to find the evolutionary difference stored in cellular networks.

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