Showing papers by "National Taiwan University of Science and Technology published in 2014"

Nanoscale nickel oxide/nickel heterostructures for active hydrogen evolution electrocatalysis

Abstract: There is ongoing research into new electrocatalysts for hydrogen production from water splitting. Here, the authors report the electrocatalytic performance of nickel/nickel oxide heterostructures on carbon nanotubes, and are able to assemble a water electrolyzer operated by a single-cell 1.5 V battery.

Atomic mechanism of the semiconducting-to-metallic phase transition in single-layered MoS2.

Abstract: Phase transitions can be used to alter the properties of a material without adding any additional atoms and are therefore of significant technological value. In a solid, phase transitions involve collective atomic displacements, but such atomic processes have so far only been investigated using macroscopic approaches. Here, we show that in situ scanning transmission electron microscopy can be used to follow the structural transformation between semiconducting (2H) and metallic (1T) phases in single-layered MoS2, with atomic resolution. The 2H/1T phase transition involves gliding atomic planes of sulphur and/or molybdenum and requires an intermediate phase (α-phase) as a precursor. The migration of two kinds of boundaries (β- and γ-boundaries) is also found to be responsible for the growth of the second phase. Furthermore, we show that areas of the 1T phase can be controllably grown in a layer of the 2H phase using an electron beam.

Monolayer behaviour in bulk ReS2 due to electronic and vibrational decoupling

Abstract: Monolayers of transition metal dichalcogenides have emerged as interesting two-dimensional materials. Here, the authors show that in a new member of this family of compounds, rhenium disulphide, the layers in the bulk are vibrationally and electronically decoupled, so that they behave almost as monolayers.

Electrical Transport Properties of Single-Layer WS2

Abstract: We report on the fabrication of field-effect transistors based on single layers and bilayers of the semiconductor WS2 and the investigation of their electronic transport properties. We find that the doping level strongly depends on the device environment and that long in situ annealing drastically improves the contact transparency, allowing four-terminal measurements to be performed and the pristine properties of the material to be recovered. Our devices show n-type behavior with a high room temperature on/off current ratio of similar to 10(6). They show clear metallic behavior at high charge carrier densities and mobilities as high as similar to 140 cm(2)/(V s) at low temperatures (above 300 cm(2)/(V s) in the case of bilayers). In the insulating regime, the devices exhibit variable range hopping, with a localization length of about 2 nm that starts to increase as the Fermi level enters the conduction band. The promising electronic properties of WS2, comparable to those of single layer MoS2 and WSe2, together with its strong spin-orbit coupling, make it interesting for future applications in electronic, optical, and valleytronic devices.

Cross-Linking with Diamine Monomers To Prepare Composite Graphene Oxide-Framework Membranes with Varying d-Spacing

Abstract: Three diamine monomers (ethylenediamine, butylenediamine, and p-phenylenediamine) were selected for cross-linking graphene oxide (GO) to prepare composite graphene oxide-framework (GOF) membranes through filtration using a pressure-assisted self-assembly technique. The membranes were applied to separate an ethanol–water mixture by pervaporation. Unmodified GO comprised only hydrogen bonds and π–π interactions, but after cross-linking it with a diamine, attenuated total reflectance–Fourier transform infrared and X-ray photoelectron spectroscopy demonstrated that the diamine was chemically bonded both to GO and the membrane support. Moreover, GO hydrophilicity was substantially altered; water contact angle increased from 24.4° to 80.6° (from cross-linking with an aliphatic structure of diamine to cross-linking with an aromatic structure). Results of X-ray diffraction showed that d-spacing in GOF layers varied from 10.4 to 8.7 A. For GOFs presoaked in 90 wt % ethanol–water, covalent bonds between the layer a...

Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization

Abstract: In this paper, we study a probabilistically robust transmit optimization problem under imperfect channel state information (CSI) at the transmitter and under the multiuser multiple-input single-output (MISO) downlink scenario. The main issue is to keep the probability of each user's achievable rate outage as caused by CSI uncertainties below a given threshold. As is well known, such rate outage constraints present a significant analytical and computational challenge. Indeed, they do not admit simple closed-form expressions and are unlikely to be efficiently computable in general. Assuming Gaussian CSI uncertainties, we first review a traditional robust optimization-based method for approximating the rate outage constraints, and then develop two novel approximation methods using probabilistic techniques. Interestingly, these three methods can be viewed as implementing different tractable analytic upper bounds on the tail probability of a complex Gaussian quadratic form, and they provide convex restrictions, or safe tractable approximations, of the original rate outage constraints. In particular, a feasible solution from any one of these methods will automatically satisfy the rate outage constraints, and all three methods involve convex conic programs that can be solved efficiently using off-the-shelf solvers. We then proceed to study the performance-complexity tradeoffs of these methods through computational complexity and comparative approximation performance analyses. Finally, simulation results are provided to benchmark the three convex restriction methods against the state of the art in the literature. The results show that all three methods offer significantly improved solution quality and much lower complexity.

Direct in situ observation of Li2O evolution on Li-rich high-capacity cathode material, Li[Ni(x)Li((1-2x)/3)Mn((2-x)/3)]O2 (0 ≤ x ≤ 0.5).

Abstract: High-capacity layered, lithium-rich oxide cathodes show great promise for use as positive electrode materials for rechargeable lithium ion batteries. Understanding the effects of oxygen activating reactions on the cathode’s surface during electrochemical cycling can lead to improvements in stability and performance. We used in situ surfaced-enhanced Raman spectroscopy (SERS) to observe the oxygen-related surface reactions that occur during electrochemical cycling on lithium-rich cathodes. Here, we demonstrate the direct observation of Li2O formation during the extended plateau and discuss the consequences of its formation on the cathode and anode. The formation of Li2O on the cathode leads to the formation of species related to the generation of H2O together with LiOH and to changes within the electrolyte, which eventually result in diminished performance. Protection from, or mitigation of, such devastating surface reactions on both electrodes will be necessary to help realize the potential of high-capaci...

Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective

Abstract: The rapid progress of mobile, wireless communication and sensing technologies has enabled the development of context-aware ubiquitous learning (u-learning) environments, which are able to detect the real-world learning status of students as well as the environmental contexts. Accordingly, appropriate information can be provided to individual students in the right place and at the right time. However, researchers have indicated that, to support students to learn in real-world contexts in smart ways, more factors need to be taken into account when designing and developing learning systems. In this paper, the definition and criteria of smart learning environments are presented from the perspective of context-aware ubiquitous learning. A framework is also presented to address the design and development considerations of smart learning environments to support both online and real-world learning activities. Moreover, some emerging technologies that might facilitate the development of smart learning environments as well as the features and criteria of smart learning are addressed. Finally, research issues related to smart learning are provided.

Distributed Constrained Optimization by Consensus-Based Primal-Dual Perturbation Method

Abstract: Various distributed optimization methods have been developed for solving problems which have simple local constraint sets and whose objective function is the sum of local cost functions of distributed agents in a network. Motivated by emerging applications in smart grid and distributed sparse regression, this paper studies distributed optimization methods for solving general problems which have a coupled global cost function and have inequality constraints. We consider a network scenario where each agent has no global knowledge and can access only its local mapping and constraint functions. To solve this problem in a distributed manner, we propose a consensus-based distributed primal-dual perturbation (PDP) algorithm. In the algorithm, agents employ the average consensus technique to estimate the global cost and constraint functions via exchanging messages with neighbors, and meanwhile use a local primal-dual perturbed subgradient method to approach a global optimum. The proposed PDP method not only can handle smooth inequality constraints but also non-smooth constraints such as some sparsity promoting constraints arising in sparse optimization. We prove that the proposed PDP algorithm converges to an optimal primal-dual solution of the original problem, under standard problem and network assumptions. Numerical results illustrating the performance of the proposed algorithm for a distributed demand response control problem in smart grid are also presented.

A Maximum Power Point Tracking Method Based on Perturb-and-Observe Combined With Particle Swarm Optimization

Abstract: Conventional maximum power point tracking (MPPT) methods such as perturb-and-observe (P&O) method can only track the first local maximum point and stop progressing to the next maximum point. MPPT methods based on particle swarm optimization (PSO) have been proposed to track the global maximum point (GMP). However, the problem with the PSO method is that the time required for convergence may be long if the range of the search space is large. This paper proposes a hybrid method, which combines P&O and PSO methods. Initially, the P&O method is employed to allocate the nearest local maximum. Then, starting from that point on, the PSO method is employed to search for the GMP. The advantage of using the proposed hybrid method is that the search space for the PSO is reduced, and hence, the time that is required for convergence can be greatly improved. The excellent performance of the proposed hybrid method is verified by comparing it against the PSO method using an experimental setup.

An Augmented Reality-Based Mobile Learning System to Improve Students' Learning Achievements and Motivations in Natural Science Inquiry Activities

Abstract: Introduction Recently, the advancement and popularity of handheld devices and sensing technologies has enabled researchers to implement more effective learning methods (Ogata, Li, Hou, Uosaki, El-Bishouty, & Yano, 2011). Several studies have reported the importance of conducting contextual learning and experiential learning in real-world environments, encouraging the use of mobile and sensing technologies in outdoor learning activities (Chu, Hwang, Tsai, & Tseng, 2010; Hung, Hwang, Lin, Wu, & Su, 2013; Yang, 2006). For example, Chu, Hwang, Huang, and Wu (2008) developed a learning system that guided students to learn about the characteristics and life cycle of plants on a school campus using mobile communication and RFID (Radio Frequency Identification). Most of mobile learning studies emphasize the adoption of digital learning aids in real-life scenarios (Sharples, Milrad, Arnedillo-Sanchez, & Vavoula, 2009; Ogata & Yano, 2004; Wong & Looi, 2011). However, regarding supplementary mobile learning aids, the interaction between digital learning aids and the actual environment needs to be emphasized to enable students to effectively manage and incorporate personal knowledge (Wu, Lee, Chang, & Liang, 2013). For example, it is expiated that students can select a virtual learning object from the actual environment using a mobile learning aid, which allows them to obtain a first-hand understanding of the learning environment and, subsequently, increases their learning motivations and experiences. Such a learning support technology is achievable through the use of Augmented Reality (AR), which combines human senses (e.g., sight, sound, and touch) with virtual objects to facilitate real-world environment interactions for users to achieve an authentic perception of the environment (Azuma, 1997). For example, users who employ mobile devices with AR facilities to seek a target building on a street are able to see additional information surrounding individual buildings when they browse the buildings via the camera of their mobile device. Researchers have documented the potential of employing such facilities to assist students in learning in real-world environments in comparisons with traditional instructions (Andujar, Mejias, & Marquez, 2011; Chen, Chi, Hung, & Kang, 2011; Kamarainen, Metcalf, Grotzer, Browne, Mazzuca, Tutwiler, & Dede, 2013; Platonov, Heibel, Meier, & Grollmann, 2006), which showed that AR technology contributed to improve academic achievement compared to traditional teaching methods. On the other hand, numerous educators have contended that computer technology cannot support the learning process entirely; instead, the primary function of computer technology involves a knowledge building tool for students (Jonassen, Carr, & Yueh, 1998). Effective learning strategies remain the most crucial factor for increasing learning motivation. Therefore, effective learning strategies supplemented with appropriate computer technology can greatly enhance learning motivation (Chu, Hwang, & Tsai, 2010; Jonassen, 1999; Hwang, Tsai, Chu, Kinshuk, & Chen, 2012; Jonassen et al., 1998). Previous studies have highlighted that inquiry-based learning strategies supplemented by computer technology in a scenario-based learning environment can effectively increase learning motivation (Shih, Chuang, & Hwang, 2010; Soloway & Wallace, 1997). Inquiry-based learning strategies are student-centric knowledge exploration activities; the teacher serves as a guide, employing structured methods that train and encourage students to learn proactively (Hwang, Wu, Zhuang, & Huang, 2013; Soloway & Wallace, 1997). When students acquire the methods for problem-solving, they use the obtained information to establish a hypothesis or to plan solutions to the problem (Looi, 1998). Consequently, in this study, an innovative learning approach is proposed to support inquiry-based learning activities with mobile AR. …

Development and behavioral pattern analysis of a mobile guide system with augmented reality for painting appreciation instruction in an art museum

Abstract: A mobile guide system that integrates art appreciation instruction with augmented reality (AR) was designed as an auxiliary tool for painting appreciation, and the learning performance of three groups of visiting participants was explored: AR-guided, audio-guided, and nonguided (ie, without carrying auxiliary devices) The participants were 135 college students, and a quasi-experimental research design was employed Several learning performance factors of the museum visitors aided with different guided modes were evaluated, including their learning effectiveness, flow experience, the amount of time spent focusing on the paintings, behavioral patterns, and attitude of using the guide systems The results showed that compared to the audio- and nonguided participants, the AR guide effectively enhanced visitors' learning effectiveness, promoted their flow experience, and extended the amount of time the visitors spent focusing on the paintings In addition, the visitors' behavioral patterns were dependent upon the guided mode that they used; the visitors who were the most engaged in the gallery experience were those who were using the AR guide Most of the visitors using the mobile AR-guide system elicited positive responses and acceptance attitudes

Highly Efficient Visible Light Photocatalytic Reduction of CO2 to Hydrocarbon Fuels by Cu-Nanoparticle Decorated Graphene Oxide

Abstract: The production of renewable solar fuel through CO2 photoreduction, namely artificial photosynthesis, has gained tremendous attention in recent times due to the limited availability of fossil-fuel resources and global climate change caused by rising anthropogenic CO2 in the atmosphere. In this study, graphene oxide (GO) decorated with copper nanoparticles (Cu-NPs), hereafter referred to as Cu/GO, has been used to enhance photocatalytic CO2 reduction under visible-light. A rapid one-pot microwave process was used to prepare the Cu/GO hybrids with various Cu contents. The attributes of metallic copper nanoparticles (∼4–5 nm in size) in the GO hybrid are shown to significantly enhance the photocatalytic activity of GO, primarily through the suppression of electron–hole pair recombination, further reduction of GO’s bandgap, and modification of its work function. X-ray photoemission spectroscopy studies indicate a charge transfer from GO to Cu. A strong interaction is observed between the metal content of the C...

Model-Free Predictive Current Control for Interior Permanent-Magnet Synchronous Motor Drives Based on Current Difference Detection Technique

Abstract: A model-free predictive current control (PCC) of interior permanent-magnet synchronous motor (IPMSM) drive systems based on a current difference detection technique is proposed. The model-based PCC (MBPCC) of IPMSM requires knowledge of parameters such as resistance, q-axis inductance, and extended back EMF. This paper develops a new model-free approach that alleviates the need for excessive prior knowledge about the system and only utilizes the stator currents as well as the current differences corresponding to different switching states of the inverter. Despite the salient difference of the proposed approach, it adopts a measure similar to that in the MBPCC approach to obtain the next switching state of the inverter by minimizing a cost function. It is noteworthy that the proposed method is easy to implement due to its simplicity and free of any multiplication operation. For comparison purposes, a digital signal processor, TMS320LF2407, is used to execute the two aforementioned current control techniques. Several experimental results show that the proposed method can significantly improve the current-tracking performance.

Properties of Individual Dopant Atoms in Single‐Layer MoS2: Atomic Structure, Migration, and Enhanced Reactivity

Abstract: Single-layered MoS2 doped with Re (n-type) and Au (p-type) are investigated by in situ scanning transmission electron microscopy. Re atoms substituting Mo sites enhance the local chemical affinity, evidenced by agglomeration of other dopant/impurity atoms. Au atoms exist as adatoms and show larger mobility under the electron beam. These behaviors are consistent with density functional theory calculations.

The synergetic effect of graphene on Cu2O nanowire arrays as a highly efficient hydrogen evolution photocathode in water splitting

Abstract: A one dimensional (1D) Cu2O straddled with graphene is proposed as a highly promising and stable photocathode for solar hydrogen production. The Cu2O nanowire arrays modified with an optimized concentration of graphene provide much higher improved photocurrent density −4.8 mA cm−2, (which is two times that of bare 1D Cu2O, −2.3 mA cm−2), at 0 V vs. RHE under AM 1.5 illumination (100 mW cm−2) and solar conversion efficiency reaching 3.3% at an applied potential of −0.55 V vs. the Pt counter electrode. Surprisingly, 1D Cu2O with an optimum graphene concentration exhibits an inspiring photocurrent density from 2.1 to 1.1 mA cm−2 at a higher positive potential range of 0.2–0.4 V vs. RHE, which is 300–550% higher compared with that of bare 1D Cu2O. This is the highest value ever reported for a Cu2O-based photocathode at such a positive potential. After 20 minutes of standard solar irradiation, 83% of the initial photocurrent density was retained for the nanocomposite which is more than five times compared to the bare Cu2O (14.5%). A Faradic efficiency of 74% was obtained for the evolved H2 gas measurement. To get evidence for the photostability of the graphene modified photocathode, detailed characterization was carried out. The high PEC performance of the graphene/Cu2O nanocomposite is attributed to the improved crystallinity and the synergetic effect of graphene in absorbing visible light, suppressing the charge recombination and photocorrosion of the photoelectrode by preventing direct contact with the electrolyte. This inexpensive photocathode prepared free of noble metals, showed enhanced high photocurrent density with good stability and is a highly promising photocathode for solar hydrogen production.

Applications, impacts and trends of mobile technology-enhanced learning: a review of 2008-2012 publications in selected SSCI journals

Abstract: The use of mobile technologies in learning has attracted much attention from researchers and educators in the past decade. However, the impacts of mobile learning on students' learning performance are still unclear. In particular, some schoolteachers still doubt the effectiveness of using such new technologies in school settings. In this study, a survey has been conducted by reviewing the 2008-2012 publications in seven well-recognised Social Science Citation Index SSCI journals of technology-enhanced learning to investigate the applications and impacts of mobile technology-enhanced learning. It is found that mobile learning is promising in improving students' learning achievements, motivations and interests. In addition, from the survey, it is found that smartphones and tablet PCs have gradually become widely adopted mobile learning devices in recent years, which could affect the adoption of sensing technologies in the future. Accordingly, several open issues of mobile learning are addressed.

Two-dimensional molybdenum tungsten diselenide alloys: photoluminescence, Raman scattering, and electrical transport.

Abstract: Two-dimensional transition-metal dichalcogenide alloys have attracted intense attention due to their tunable band gaps. In the present work, photoluminescence, Raman scattering, and electrical transport properties of monolayer and few-layer molybdenum tungsten diselenide alloys (Mo1-xWxSe2, 0 ≤ x ≤ 1) are systematically investigated. The strong photoluminescence emissions from Mo1-xWxSe2 monolayers indicate composition-tunable direct band gaps (from 1.56 to 1.65 eV), while weak and broad emissions from the bilayers indicate indirect band gaps. The first-order Raman modes are assigned by polarized Raman spectroscopy. Second-order Raman modes are assigned according to its frequencies. As composition changes in Mo1-xWxSe2 monolayers and few layers, the out-of-plane A1g mode showed one-mode behavior, while B2g(1) (only observed in few layers), in-plane E2g(1), and all observed second-order Raman modes showed two-mode behaviors. Electrical transport measurement revealed n-type semiconducting transport behavior with a high on/off ratio (>10(5)) for Mo1-xWxSe2 monolayers.

Why do people play mobile social games? An examination of network externalities and of uses and gratifications

Abstract: Purpose – The purpose of this paper is to identify the factors that influence people to play socially interactive games on mobile devices. Based on network externalities and theory of uses and gratifications (U&G), it seeks to provide direction for further academic research on this timely topic. Design/methodology/approach – Based on 237 valid responses collected from online questionnaires, structural equation modeling technology was employed to examine the research model. Findings – The results reveal that both network externalities and individual gratifications significantly influence the intention to play social games on mobile devices. Time flexibility, however, which is one of the mobile device features, appears to contribute relatively little to the intention to play mobile social games. Originality/value – This research successfully applies a combination of network externalities theory and U&G theory to investigate the antecedents of players’ intentions to play mobile social games. This study is ab...

Students' online interactive patterns in augmented reality-based inquiry activities

Abstract: Inquiry learning has been developing for years and many countries have incorporated inquiry learning into the scope of K-12 education. Educators have indicated the importance of engaging students in knowledge-sharing activities during the inquiry learning process. In this study, a location-based augmented reality (AR) environment with a five-step guiding mechanism is developed to guide students to share knowledge in inquiry learning activities. To evaluate the effectiveness of the proposed approach in terms of promoting the knowledge sharing behaviors of students, an experiment has been conducted in an elementary school natural science course. The participants were 57 fourth-grade students from an elementary school in Northern Taiwan, divided into an experimental group of 28 students who learned with the AR-based approach and a control group of 29 students who learned with the conventional in-class mobile learning approach. The students' learning behaviors, including their movements in the real-world environment and interactions with peers, were recorded. Accordingly, the learning patterns and interactions of the two groups were analyzed via lag-sequential analysis and quantitative content analysis. It was found that, in comparison with the conventional inquiry-based mobile learning activity, the AR-based inquiry learning activity is able to engage the students in more interactions for knowledge construction. The findings of this study provide guidance for helping teachers develop effective strategies and learning designs for conducting inquiry-based learning activities.

Improving learning achievements, motivations and problem-solving skills through a peer assessment-based game development approach

Abstract: In this study, a peer assessment-based game development approach is proposed for improving students’ learning achievements, motivations and problem-solving skills. An experiment has been conducted to evaluate the effectiveness of the proposed approach in a science course at an elementary school. A total of 167 sixth graders participated in the experiment, 82 of whom were assigned to the experimental group and learned with the peer assessment-based game development approach, while 85 students were in the control group and learned with the conventional game development approach. From the empirical results, it was found that the proposed approach could effectively promote students’ learning achievement, learning motivation, problem-solving skills, as well as their perceptions of the use of educational computer games. Moreover, it was found from the open-ended questions that most of the students perceived peer assessment-based game development as an effective learning strategy that helped them improve their deep learning status in terms of “in-depth thinking,” “creativity,” and “motivation.”

Accurately predicting building energy performance using evolutionary multivariate adaptive regression splines

Abstract: This paper proposes using evolutionary multivariate adaptive regression splines (EMARS), an artificial intelligence (AI) model, to efficiently predict the energy performance of buildings (EPB). EMARS is a hybrid of multivariate adaptive regression splines (MARS) and artificial bee colony (ABC). In EMARS, MARS addresses learning and curve fitting and ABC carries out optimization to determine the fittest parameter settings with minimal prediction error. The proposed model was constructed using 768 experimental datasets from the literature, with eight input parameters and two output parameters (cooling load (CL) and heating load (HL)). EMARS performance was compared against five other AI models, including MARS, back-propagation neural network (BPNN), radial basis function neural network (RBFNN), classification and regression tree (CART), and support vector machine (SVM). A 10-fold cross-validation approach found EMARS to be the best model for predicting CL and HL with 65% and 45% deduction in terms of RMSE, respectively, compared to other methods. Furthermore, EMARS is able to operate autonomously without human intervention or domain knowledge; represent derived relationship between response (HL and CL) with predictor variables associated with their relative importance.