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Plasma-Induced Exfoliation Provides Onion-Like Graphene-Surrounded MoS2 Nanosheets for a Highly Efficient Hydrogen Evolution Reaction
(2020-03-11)
Nguyen, Van-Truong
;
Le, Phuoc Anh
;
Hsu, Yung-Chi
;
Wei, Kung-Hwa
;
交大名義發表
With the goal of obtaining sustainable earth-abundant electrocatalyst materials displaying high performance in the hydrogen evolution reaction (HER), here we propose a facile one-pot plasma-induced electrochemical process for the fabrication of new core-shell structures of ultrathin MoS2 nanosheets engulfed within onion-like graphene nanosheets (OGNs@MoS2). The resultant OGNs@MoS2 structures not only increased the number of active sites of the semiconducting MoS2 nanosheets but also enhanced their conductivity. Our OGNs@MoS2 composites exhibited high HER performance, characterized by a low overpotential of 118 mV at a current density of 10 mA cm(-2), a Tafel slope of 73 mV dec(-1), and long-time stability of 10(5) s without degradation; this performance is much better than that of the sheet-like graphene-wrapped MoS2 composite GNsgMoS(2) (182 mV, 82 mV dec(-1)) and is among the best ever reported for composites involving MoS2 and graphene nanosheets prepared through a simple one-batch process and using a low temperature and a short time for the HER. This approach appears to be an effective and simple strategy for tuning the morphologies of composites of graphene and transition metal dichalcogenide materials for a broad range of energy applications.
Tunable nitrogen-doped graphene sheets produced with in situ electrochemical cathodic plasma at room temperature for lithium-ion batteries
(2019-06-01)
Yen, Po-Jen
;
Ilango, P. Robert
;
Chiang, Ya-Chi
;
Wu, Chia-Wei
;
Hsu, Yung-Chi
This paper describes a facile and tunable method, performed at room temperature and amenable to mass-production, for the preparation of nitrogen-doped graphene sheets using in situ electrochemical cathodic plasma exfoliation of a graphite/melamine composite. The plasma generated at the submerged surface of the graphite/melamine composite electrode not only exfoliated the graphite granules into graphene sheets but also induced the doping of nitrogen atoms. The sizes and morphologies of the graphene sheets, comprising four to five layers of graphene, were confirmed using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Tuning the concentration of melamine in the graphite/melamine composites led to different levels of nitrogen-doping in the graphene sheets; the stability of the nitrogen-doped graphene sheets was investigated in various harsh environments. Furthermore, the use of the 4.63 at% nitrogen-doped graphene sheets as anode materials resulted in lithium-ion batteries displaying excellent performance, with a capacity of 488 mA h g(-1) at 100 mA g(-1) after 100 cycles that was better than that of the device incorporating pristine graphene sheets (344 mA h g(-1) at 100 mA g(-1) after 100 cycles). This method is a new and tunable approach for producing massive amounts of nitrogen-doped graphene sheets from graphite powder at room temperature. (C) 2019 Elsevier Ltd. All rights reserved.
Using Different Ions to Tune Graphene Stack Structures from Sheet- to Onion-Like During Plasma Exfoliation, with Supercapacitor Applications
(2019-04-23)
Yen, Po-Jen
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Sahoo, Sumanta Kumar
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Chiang, Ya-Chi
;
Huang, Shih-Yu
;
Wu, Chia-Wei
In this article, we report a facile and simple approach for tuning graphene nanosheet structures (GNS) with different ions in the electrolytes through cathodic plasma exfoliation process in electrochemical reactions. We obtained sheet- and onion-like GNS when aqueous electrolyte NaOH and H2SO4, respectively, were present during plasma exfoliation in the electrochemical reactions, as evidenced from scanning electron microscopy and transmission electron microscopy images. Moreover, the onion-like GNS exhibited a specific surface area of 464m(2) g(-1) and a supercapacitive performance of 67.1F g(-1), measured at a scan rate of 5mV s(-1) in 1M NaCl; these values were much higher than those (72m(2) g(-1) and 21.6F g(-1), respectively) of the sheet-like GNS. This new approach for efficiently generating tunable stacked graphene structures with different ions, in the cathodic plasma exfoliation process, has promising potentials for use in energy storage devices.
A Novel Self-Constructing Evolution Algorithm for TSK-type Fuzzy Model Design
(2010)
Lin, Sheng-Fuu
;
Chang, Jyun-Wei
;
Cheng, Yi-Chang
;
Hsu, Yung-Chi
;
電機工程學系
In this paper, a novel self-constructing evolution algorithm (SCEA) for TSK-type fuzzy model (TFM) design is proposed. The proposed SCEA method is different from the traditional genetic algorithms (GA). A chromosome of the population in GA represents a full solution and only one population presents all solutions. Our method applies a population to evaluate a partial solution locally, and several populations to construct the full solution. Thus, a chromosome represents only partial solution. The proposed SCEA uses the self-constructing learning algorithm to construct the TFM automatically that is based on the input data to decide the input partition. And we also adopted the sequence search-based dynamic evolution (SSDE) method to perform parameter learning. Simulation results have shown that the proposed SCEA method obtains better performance than some existing models.
Using multiple-strategy evolutionary neural fuzzy network to automatically identify disorientation problems: A Web-based system application
(2013)
Hung, Pei-Chia
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Lin, Sheng-Fuu
;
Hsu, Yung-Chi
;
電控工程研究所
;
Institute of Electrical and Control Engineering
For solving users' disorientation problems when using Web-based systems, there is an important issue to understand why they cause such problems. To this end, there is a need to investigate the relationship between users' characteristics and their disorientation problems. However, when facing this challenge, it is difficult to identify which users' characteristics may play important factors or how their characteristics interact with each other to influence their disorientation problems. Thus, this paper tends to propose an automatic architecture for solving this issue. More specifically, this study proposes a multiple-strategy evolutionary neural fuzzy network (MSE-NFN) to not only provides an efficiency way to automatically identify users' disorientation problems but also investigate which users' characteristics greatly influence their disorientation problems. The results indicate that users' experience of using Internet, experience of using navigation tools and different levels of prior knowledge are influential factors to affect their disorientation problems. Moreover, it also demonstrates that the proposed architecture (MSE-NFN) outperform than other existing evolutionary methods. Based on the results, a framework is conducted, which can be used to automatically identify users' disorientation problems when developing the personalized Web-based systems.
Self-organization hybrid evolution learning algorithm for recurrent wavelet-based neuro-fuzzy identifier design
(2013)
Hsu, Yung-Chi
;
Lin, Sheng-Fuu
;
電控工程研究所
;
Institute of Electrical and Control Engineering
In this paper, a recurrent wavelet-based neuro-fuzzy identifier (RWNFI) with a self-organization hybrid evolution learning algorithm (SOHELA) is proposed for solving various identification problems. In the proposed SOHELA, the group-based symbiotic evolution (GSE) is adopted such that each group in the GSE represents a collection of only one fuzzy rule. The proposed SOHELA consists of structure learning and parameter learning. In structure learning, the proposed SOHELA uses the self-organization algorithm (SOA) to determine a suitable rule number in the RWNFI. In parameter learning, the proposed SOHELA uses the data mining-based selection method (DMSM) and the data mining-based crossover method (DMCM) to determine groups and parent groups using the data mining method called the frequent pattern growth (FP-Growth) method. Based on identification simulations, the excellent performance of the proposed SOHELA compares with other various existing models.
USING MULTI-ANGLES EVOLUTIONARY ALGORITHMS FOR TRAINING TSK-TYPE NEURO-FUZZY NETWORKS
(2012-11-01)
Hung, Pei-Chia
;
Lin, Sheng-Fuu
;
Hsu, Yung-Chi
;
電控工程研究所
;
Institute of Electrical and Control Engineering
The development of a global-based method for building robust neuro-fuzzy networks has become an interesting issue. Among the various building methods, the evolutionary algorithms provide robust ways increasing the chances of meeting the optimal solution. However, evolutionary algorithms may only use a single angle to evaluate the searching space to obtain the optimal solutions. It implies that they may slowly or even hardly meet the optimal solution. Thus, the current study provides a novel architecture that uses multiple angles for evaluating the searching space. More specifically, the novel architecture adopts multiple angles to improve the evolutionary process by dynamically adjusting the searching space. By doing so, the proposed architecture can increase the chances of meeting the optimal solution. As shown in the results, the proposed architecture outperforms other existing evolutionary algorithms. Based on the results, a framework is proposed to build a benchmark for developing evolutionary algorithms that consider the multiple angles of the solution space.
A self-organization mining based hybrid evolution learning for TSK-type fuzzy model design
(2012-03-01)
Lin, Sheng-Fuu
;
Chang, Jyun-Wei
;
Hsu, Yung-Chi
;
電機工程學系
;
Department of Electrical and Computer Engineering
In this paper, a self-organization mining based hybrid evolution (SOME) learning algorithm for designing a TSK-type fuzzy model (TFM) is proposed. In the proposed SOME, group-based symbiotic evolution (GSE) is adopted in which each group in the GSE represents a collection of only one fuzzy rule. The proposed SOME consists of structure learning and parameter learning. In structure learning, the proposed SOME uses a two-step self-organization algorithm to decide the suitable number of rules in a TFM. In parameter learning, the proposed SOME uses the data mining based selection strategy and data mining based crossover strategy to decide groups and parental groups by the data mining algorithm that called frequent pattern growth. Illustrative examples were conducted to verify the performance and applicability of the proposed SOME method.
Reinforcement hybrid evolutionary learning for recurrent wavelet-based neurofuzzy systems
(2007-08-01)
Lin, Cheng-Jian
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Hsu, Yung-Chi
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a recurrent wavelet-based neurofuzzy system (RWNFS) with the reinforcement hybrid evolutionary learning algorithm (R-HELA) for solving various control problems. The proposed R-HELA combines the compact genetic algorithm (CGA), and the modified variable-length genetic algorithm (MVGA) performs the structure/parameter learning for dynamically constructing the RWNFS. That is, both the number of rules and the adjustment of parameters in the RWNFS are designed concurrently by the R-HELA. In the R-HELA, individuals of the same length constitute the same group. There are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. Illustrative examples were conducted to show the performance and applicability of the proposed R-HELA method.
Reinforcement evolutionary learning using data mining algorithm with TSK-type fuzzy controllers
(2011-04-01)
Hsu, Chi-Yao
;
Hsu, Yung-Chi
;
Lin, Sheng-Fuu
;
電機工程學系
;
Department of Electrical and Computer Engineering
Reinforcement evolutionary learning using data mining algorithm (R-ELDMA) with a TSK-type fuzzy controller (TFC) for solving reinforcement control problems is proposed in this study. R-ELDMA aims to determine suitable rules in a TFC and identify suitable and unsuitable groups for chromosome selection. To this end, the proposed R-ELDMA entails both structure and parameter learning. In structure learning, the proposed R-ELDMA adopts our previous research - the self-adaptive method (SAM) - to determine the suitability of TFC models with different fuzzy rules. In parameter learning, the data-mining based selection strategy (DSS), which proposes association rules, is used. More specifically, DSS not only determines suitable groups for chromosomes selection but also identifies unsuitable groups to be avoided selecting chromosomes to construct a TFC. Illustrative examples are presented to show the performance and applicability of the proposed R-ELDMA. Crown Copyright (C) 2010 Published by Elsevier B. V. All rights reserved.

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