Tailoring Layer Number of 2D Porphyrin‐Based MOF Towards Photo‐Coupled Electroreduction of CO 2

2021 ◽  
pp. 2107293
Author(s):  
Deren Yang ◽  
Shouwei Zuo ◽  
Haozhou Yang ◽  
Yue Zhou ◽  
Qichen Lu ◽  
...  
Keyword(s):  
Layer Number

Layer Number Dependence of Charge Density Wave Phase Transition Between Nearly-Commensurate and Incommensurate Phases in 1T-TaS2

2020 ◽  
Vol 124 (49) ◽  
pp. 27176-27184
Author(s):  
Yasushi Ishiguro ◽  
Kirill Bogdanov ◽  
Naoko Kodama ◽  
Mizuki Ogiba ◽  
Tatsuya Ohno ◽  
...  
Keyword(s):  
Phase Transition ◽  
Charge Density ◽  
Charge Density Wave ◽  
Density Wave ◽  
Layer Number ◽  
Wave Phase ◽  
Charge Density Wave Phase ◽  
Incommensurate Phases

Layer Number-Dependent Enhanced Photoluminescence from a Quantum Dot Metamaterial Optical Resonator

2021 ◽  
Vol 3 (1) ◽  
pp. 468-475
Author(s):  
Haruka Takekuma ◽  
Junfu Leng ◽  
Kazutaka Tateishi ◽  
Yang Xu ◽  
Yinthai Chan ◽  
...  
Keyword(s):  
Quantum Dot ◽  
Optical Resonator ◽  
Layer Number ◽  
Enhanced Photoluminescence

Graphene and graphite work function depending on layer number on Re

2020 ◽  
Vol 101 ◽  
pp. 107576 ◽  
Author(s):  
E.V. Rut'kov ◽  
E.Y. Afanas'eva ◽  
N.R. Gall
Keyword(s):  
Work Function ◽  
Layer Number

Layer-Number Dependent Antibacterial and Osteogenic Behaviors of Graphene Oxide Electrophoretic Deposited on Titanium

2017 ◽  
Vol 9 (14) ◽  
pp. 12253-12263 ◽  
Author(s):  
Jiajun Qiu ◽  
Hao Geng ◽  
Donghui Wang ◽  
Shi Qian ◽  
Hongqin Zhu ◽  
...  
Keyword(s):  
Graphene Oxide ◽  
Layer Number

The critical layer number of epitaxially grown Cu and Ni films with strained structure

2005 ◽  
Vol 239 (3-4) ◽  
pp. 259-261 ◽  
Author(s):  
J.C. Li ◽  
W. Liu ◽  
Q. Jiang
Keyword(s):  
Critical Layer ◽  
Layer Number

BP-AdaBoost Algorithm Based on Variational Mode Decomposition Optimized by Envelope Entropy for Diagnosing the Working Conditions of a Slideway Seedling-Picking Mechanism

2021 ◽  
Vol 37 (4) ◽  
pp. 665-675
Author(s):  
Zhitao He ◽  
Haiyang Zhang ◽  
Jun Wang ◽  
Xin Jin ◽  
Song Gao ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Working Conditions ◽  
Working Condition ◽  
Recognition Accuracy ◽  
Variational Mode Decomposition ◽  
Adaboost Algorithm ◽  
Layer Number ◽  
Entropy Minimization ◽  
Mode Decomposition ◽  
Penalty Factor

Highlights A method of monitoring the working conditions of a slideway seedling-picking mechanism based on variational mode decomposition (VMD), envelope entropy, and energy entropy is proposed. Based on the criterion of envelope entropy minimization, the combination of the decomposition layer number and penalty factor in VMD is optimized to yield a satisfactory decomposition effect of the analyzed vibration signal. The BP-AdaBoost algorithm is used to improve the working condition classification performance for the slideway seedling-picking mechanism. The working-condition identification effect with the proposed method are compared with those through EMD-based, LMD-based, and EEMD-based methods. Abstract . The slideway seedling-picking mechanism is a type of rotating machinery. This study proposes a novel method of identifying the working conditions of slideway seedling-picking mechanisms for early fault diagnosis by utilizing a back-propagation adaptive boosting (BP-AdaBoost) algorithm based on variational mode decomposition (VMD) optimized by the envelope entropy. The experimental results demonstrate that the proposed method can effectively verify the four working conditions (normal state, slideway failure, cam failure, and spring failure). The overall recognition accuracy reaches 90.0% under the optimal combination of the decomposition layer number K and penalty factor a in VMD determined through the envelope entropy minimization criterion. Classification comparisons with empirical mode decomposition (EMD), local mean decomposition (LMD) and ensemble empirical mode decomposition (EEMD) integrated into the BP-AdaBoost algorithm indicate that the overall recognition accuracy of the proposed method is 18.1%, 16.9%, and 15.6% higher than the accuracies of the three conventional methods, respectively. Compared with the K-means, support vector machine (SVM) algorithms, BP-AdaBoost algorithm demonstrates a more dependable capability for identifying the working conditions. This study provides a useful reference for monitoring the working conditions of slideway seedling-picking mechanisms. Keywords: BP-AdaBoost algorithm, Energy entropy, Envelope entropy, Slideway seedling-picking mechanism, Variational mode decomposition, Working conditions.

Thermal Behavior of Polyelectrolyte Multilayer Microcapsules. 1. The Effect of Odd and Even Layer Number

2005 ◽  
Vol 109 (39) ◽  
pp. 18250-18259 ◽  
Author(s):  
Karen Köhler ◽  
Dmitry G. Shchukin ◽  
Helmuth Möhwald ◽  
Gleb B. Sukhorukov
Keyword(s):  
Thermal Behavior ◽  
Polyelectrolyte Multilayer ◽  
Layer Number
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