Polyoxometalate-based layered nano-tubular arrays: facile fabrication and superior performance for catalysis

RSC Advances ◽  
2015 ◽  
Vol 5 (31) ◽  
pp. 24550-24557 ◽  
Author(s):  
Hongpeng Zhen ◽  
Xiaolin Li ◽  
Lijuan Zhang ◽  
Huan Lei ◽  
Chao Yu ◽  
...  
Keyword(s):  
Superior Performance ◽  
One Dimensional ◽  
Keggin Type ◽  
Facile Fabrication ◽  
Tubular Arrays

Keggin-type polyoxoanion PW12O403−-containing one-dimensional nano-tubular arrays fabricated within porous templates show a superior performance just through simple filtrating processes.

scholarly journals Measurement Matrix Optimization via Mutual Coherence Minimization for Compressively Sensed Signals Reconstruction

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Ziran Wei ◽  
Jianlin Zhang ◽  
Zhiyong Xu ◽  
Yong Liu ◽  
Krzysztof Okarma
Keyword(s):  
Optimization Methods ◽  
Superior Performance ◽  
Gram Matrix ◽  
Sparse Signals ◽  
Two Dimensional ◽  
Measurement Matrix ◽  
Mutual Coherence ◽  
One Dimensional ◽  
Two Dimensional Image ◽  
Matrix Optimization

For signals reconstruction based on compressive sensing, to reconstruct signals of higher accuracy with lower compression rates, it is required that there is a smaller mutual coherence between the measurement matrix and the sparsifying matrix. Mutual coherence between the measurement matrix and sparsifying matrix can be expressed indirectly by the property of the Gram matrix. On the basis of the Gram matrix, a new optimization algorithm of acquiring a measurement matrix has been proposed in this paper. Firstly, a new mathematical model is designed and a new method of initializing measurement matrix is adopted to optimize the measurement matrix. Then, the loss function of the new algorithm model is solved by the gradient projection-based method of Gram matrix approximating an identity matrix. Finally, the optimized measurement matrix is generated by minimizing mutual coherence between measurement matrix and sparsifying matrix. Compared with the conventional measurement matrices and the traditional optimization methods, the proposed new algorithm effectively improves the performance of optimized measurement matrices in reconstructing one-dimensional sparse signals and two-dimensional image signals that are not sparse. The superior performance of the proposed method in this paper has been fully tested and verified by a large number of experiments.

Facile Fabrication and Magnetic Properties of a One-Dimensional Magnetite Peapod in a Lipid Nanotube

2012 ◽  
Vol 4 (5) ◽  
pp. 2439-2444 ◽  
Author(s):  
Youn-Gyu Han ◽  
Masaru Aoyagi ◽  
Masumi Asakawa ◽  
Toshimi Shimizu
Keyword(s):  
Magnetic Properties ◽  
One Dimensional ◽  
Facile Fabrication ◽  
Lipid Nanotube

Different Crystallographic One-dimensional MnO2 Nanomaterials and Their Superior Performance in Catalytic Phenol Degradation

2013 ◽  
Vol 47 (11) ◽  
pp. 5882-5887 ◽  
Author(s):  
Edy Saputra ◽  
Syaifullah Muhammad ◽  
Hongqi Sun ◽  
H. M. Ang ◽  
M. O. Tadé ◽  
...  
Keyword(s):  
Phenol Degradation ◽  
Superior Performance ◽  
One Dimensional

scholarly journals Recognition of Noisy Radar Emitter Signals Using a One-Dimensional Deep Residual Shrinkage Network

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7973
Author(s):  
Shengli Zhang ◽  
Jifei Pan ◽  
Zhenzhong Han ◽  
Linqing Guo
Keyword(s):  
Recognition Rate ◽  
Superior Performance ◽  
Signal Recognition ◽  
Noisy Environments ◽  
Soft Thresholding ◽  
One Dimensional ◽  
Signal Features ◽  
Different Types ◽  
Noisy Signals ◽  
Thresholding Function

Signal features can be obscured in noisy environments, resulting in low accuracy of radar emitter signal recognition based on traditional methods. To improve the ability of learning features from noisy signals, a new radar emitter signal recognition method based on one-dimensional (1D) deep residual shrinkage network (DRSN) is proposed, which offers the following advantages: (i) Unimportant features are eliminated using the soft thresholding function, and the thresholds are automatically set based on the attention mechanism; (ii) without any professional knowledge of signal processing or dimension conversion of data, the 1D DRSN can automatically learn the features characterizing the signal directly from the 1D data and achieve a high recognition rate for noisy signals. The effectiveness of the 1D DRSN was experimentally verified under different types of noise. In addition, comparison with other deep learning methods revealed the superior performance of the DRSN. Last, the mechanism of eliminating redundant features using the soft thresholding function was analyzed.

scholarly journals Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6350
Author(s):  
Bin Wu ◽  
Shibo Yuan ◽  
Peng Li ◽  
Zehuan Jing ◽  
Shao Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Attention Mechanism ◽  
Superior Performance ◽  
Radar Signal ◽  
Signal Recognition ◽  
Electromagnetic Environment ◽  
One Dimensional ◽  
The Given ◽  
And Storage

As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.

Synthesis, Crystal Structure, and Spectral Characterization of Novel Three-dimensional Supramolecular Networks with One-dimensional Channels Based on Keggin-type Polyoxoanions and Mixed-Valence Dibenzotetrathiafulvalenes

2007 ◽  
Vol 62 (2) ◽  
pp. 195-199 ◽  
Author(s):  
Dongmei Shi ◽  
Haijun Pang ◽  
Fanxia Meng ◽  
Yu Sun ◽  
Kun Liu ◽  
...  
Keyword(s):  
Mixed Valence ◽  
Three Dimensional ◽  
Radical Cations ◽  
Water Molecules ◽  
Supramolecular Network ◽  
X Ray Diffraction ◽  
One Dimensional ◽  
X Ray ◽  
Keggin Type ◽  
Supramolecular Networks

A new organic/inorganic salt formed by mixed-valence dibenzotetrathiafulvalene (DBTTF) radical cations and the spherical Keggin-type polyoxometalate anions [H3BW12O40]2− was obtained by electrochemical oxidation of the donor in an acetonitrile and a 1,2-dichloroethane solution containing the polyanion. The compound has been characterized by X-ray diffraction, elemental analysis, EPR, IR and Raman spectroscopy. X-Ray diffraction experiments have revealed that the compound consists of heteropolyanions, water molecules and DBTTF radical cations. The organic radicals form trimers and dimers via π-π stacking; moreover, the polyoxoanions and the organic donors are also held together by hydrogen bonding interactions. In their packing arrangement, a three-dimensional supramolecular network with one-dimensional channels along the b axis is established with uncoordinated water molecules residing in the channels.

FACILE FABRICATION OF ONE-DIMENSIONAL MULTI-COMPONENT NANOSTRUCTURES USING POROUS ANODIZED ALUMINA MEMBRANE

COSMOS ◽  
2010 ◽  
Vol 06 (02) ◽  
pp. 221-234
Author(s):  
LOH PUI YEE ◽  
LIU CHENMIN ◽  
PUA WEICHENG ◽  
KAM FONG YU ◽  
CHIN WEE SHONG
Keyword(s):  
Good Control ◽  
Short Review ◽  
Core Shell ◽  
Alumina Membrane ◽  
One Dimensional ◽  
Metal Metal ◽  
Polymer Metal ◽  
Facile Fabrication ◽  
Multiwall Nanotubes ◽  
Shell Nanowires

In this short review, we report the facile fabrication of various interesting multi-component nanostructures including arrays of core-shell nanowires, multiwall nanotubes, segmented nanowires and multilayer stacked nanodisks, using anodized alumina membrane (AAM). We demonstrate that metallic (Cu, Ni and Au) and polymeric (PPV and PPy) one-dimensional (1D) arrays can be readily prepared by electrochemical deposition into the AAM. By optimizing the experimental design and conditions, we developed techniques to produce various multi-component nanostructures such as polymer/metal or metal/metal core-shell nanowires as well as nanotubes, with reasonably good control over both the length and the shell thickness of the nanostructures. Furthermore, we extend this method to make segmented nanowires as well as multilayer stacked nanodisks. Selective functionalization of the segmented nanowires resulted in end-on or side-on adhesion of nanowires during assembly. We illustrate the possibility of utilizing these 1D arrays to present patterns with luminescent and/or magnetic properties at this length scale.

Facile fabrication of reduced graphene oxide covered ZnCo2O4 porous nanowire array hierarchical structure on Ni-foam as a high performance anode for a lithium-ion battery

RSC Advances ◽  
2016 ◽  
Vol 6 (1) ◽  
pp. 547-554 ◽  
Author(s):  
Yujue Wang ◽  
Yongzhi Zhang ◽  
Junke Ou ◽  
Qian Zhao ◽  
Mei Liao ◽  
...  
Keyword(s):  
High Performance ◽  
Nanowire Array ◽  
Specific Capacity ◽  
Rate Capability ◽  
Lithium Ion ◽  
Superior Performance ◽  
Ni Foam ◽  
High Specific Capacity ◽  
Reduced Graphene ◽  
Facile Fabrication

We have successfully prepared a ZNWG–Ni electrode for LIBs showing superior performance with a high specific capacity, fine rate capability and remarkable cycling stability.

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