Effect of coke orientation on the electrochemical properties of lithium-ion battery anode

Journal of Applied Electrochemistry - In this study, Needle and Regular coke, which has different crystalline and orientation characteristics, were graphitized, and the effect of crystal changes on...     

Production of NiO,NiO/Ag,NiO/Au,and NiO/Pt hollow spheres by using block copolymer stabilized microspheres as a template

Hollow spheres of nickel oxide (NiO) and silver, gold, and platinum nanoparticle loaded NiO composites were successfully produced by using polystyrene (PS) latexes as hard template. Due to the presence of tertiary amine based diblock copolymer stabilizer on the surface of PS, the tertiary amine functional groups provided homogene deposition of nickel hydroxide, and then the precursor NiO salt production on the surface of PS latexes with a controlled precipitation technique. Then, NiO and NiO/metal NP hollow spheres were produced by calcination at 600 °C. Thermogravimetric analysis indicated that the amounts of NiO and NiO-composite after calcination were in the range of 21.1–29.7 wt%. The diameters of metal oxide spheres were in the range of 2.0–2.7 μm and the shell thickness were in the range of 250–350 nm. These structures had very low densities due to their porous and hollow structures and had outer layers with highly rough surfaces due to formation of nanosheets, which may offer important advantages for catalysis studies.     

Micro-end-milling of single-crystal silicon

Ductile-regime machining of silicon using micro-end-mill is almost impossible because of the brittle properties of silicon, crystal orientation effects, edge radius of the cutter and the hardness of tool materials. Micro-end-milling can potentially be used to create desired three dimensional (3D) free form surface features using the ductile machining technology for single-crystal silicon. There is still a lack of fundamental understanding of micro-end-milling of single-crystal silicon using diamond-coated tool, specifically basic understanding of material removal mechanism, cutting forces and machined surface integrity in micro-scale machining of silicon. In this paper, further research to understand the chip formation mechanism was conducted. An analysis was performed to discover how the chips are removed during the milling process. Brittle and ductile cutting regimes corresponding to machined surfaces and chips are discussed. Experiments have shown that single-crystal silicon can be ductile machined using micro-end-milling process. Forces generated when micro-end-milling single-crystal silicon are used to determine the performance of the milling process. Experimental results show that the dependence of the cutting force on the uncut chip thickness can be well described by a polynomial function order n. As cutting regime becomes more brittle, the cutting force has more complex function.     

Effect of TiO2 on high-temperature thermoelectric properties of ZnO

The as-sintered Zn_1−xTi_xO (0.01 ≤ x ≤ 0.05) samples contained a solid solution of Zn_1−xTi_xO with a wurtzite structure and a small amount of the cubic spinel Zn₂TiO₄. The amount of Zn₂TiO₄ increased with an increase in TiO₂ content. The density and grain size increased with the small TiO₂ content (≤0.01), and then they decreased gradually by further increasing the TiO₂ content. The addition of TiO₂ to ZnO led to a significant increase in the electrical conductivity and a decrease in the absolute value of the Seebeck coefficient, resulting in an increase in the power factor. The highest value of power factor (7.6 × 10⁻⁴ W m⁻¹ K⁻²) was attained for Zn_0.98Ti_0.02O at 1073 K. It is demonstrated that the TiO₂ addition is fairly effective for enhancing thermoelectric properties.     

Pseudo-inversion: closure properties and decidability

We consider a pseudo-inversion operation inspired by biological events, such as DNA sequence transformations, where only parts of a string are reversed. We define the pseudo-inversion of a string \(w = uxv\) to be the set of all strings \(v^Rxu^R\), where \(uv \ne \lambda \) and consider the operation from a formal language theoretic viewpoint. We show that regular languages are closed under the pseudo-inversion operation whereas context-free languages are not. Furthermore, we study the iterated pseudo-inversion operation and show that the iterated pseudo-inversion of a context-free language is recognized by a nondeterministic reversal-bounded multicounter machine. Finally, we introduce the notion of pseudo-inversion-freeness and examine closure properties and decidability problems for regular and context-free languages. We demonstrate that pseudo-inversion-freeness is decidable in polynomial time for regular languages and undecidable for context-free languages.     

Usability Principles for Augmented Reality Applications in a Smartphone Environment

Through the rapid spread of smartphones, users have access to many types of applications similar to those on desktop computer systems. Smartphone applications using augmented reality (AR) technology make use of users' location information. As AR applications will require new evaluation methods, improved usability and user convenience should be developed. The purpose of the current study is to develop usability principles for the development and evaluation of smartphone applications using AR technology. We develop usability principles for smartphone AR applications by analyzing existing research about heuristic evaluation methods, design principles for AR systems, guidelines for handheld mobile device interfaces, and usability principles for the tangible user interface. We conducted a heuristic evaluation for three popularly used smartphone AR applications to identify usability problems. We suggested new design guidelines to solve the identified problems. Then, we developed an improved AR application prototype of an Android-based smartphone, which later was conducted a usability testing to validate the effects of usability principles.     

The determinants of continuous use of social networking sites: An empirical study on Taiwanese journal-type bloggers’ continuous self-disclosure behavior

The popularity and growth of social networking sites (SNSs) have resulted in fierce competition among SNS providers. A critical concern of SNSs is to identify how to have users continuously use a specific SNS. This study examines the determinants of journal-type bloggers’ continuous self-disclosure. An analysis of 283 samples with continuous self-disclosure experiences in journal-type blogs shows that personal factors, specifically the habit of self-disclosure, are the major factors affecting bloggers’ continuous self-disclosure, followed by self-benefits. In contrast, social factors, such as positive audience feedback and social benefits, are not related to continuous self-disclosure. These results suggest that SNS providers must encourage users to develop the habit of using their web sites regularly. By providing differential benefits to users, SNS providers may attract users to continuously use their services.     

Harmony search-based hidden Markov model optimization for online classification of single trial eegs during motor imagery tasks

This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the development of a novel classification method that can be used to control an interactive robot agent platform via a BCI system. The proposed classification process is an adaptive learning method based on an optimization process of the hidden Markov model (HMM), which is, in turn, based on meta-heuristic algorithms. We utilize an optimized strategy for the HMM in the training phase of time-series EEG data during motor imagery-related mental tasks. However, this process raises important issues of model interpretation and complexity control. With these issues in mind, we explore the possibility of using a harmony search algorithm that is flexible and thus allows the elimination of tedious parameter assignment efforts to optimize the HMM parameter configuration. In this paper, we illustrate a sequential data analysis simulation, and we evaluate the optimized HMM. The performance results of the proposed BCI experiment show that the optimized HMM classifier is more capable of classifying EEG datasets than ordinary HMM during motor imagery tasks.     

Design and fabrication of an electrochemically actuated microvalve

A microfluidic valve based on electrochemical (ECM) actuation was designed, fabricated using UV-LIGA microfabrication technologies. The valve consists of an ECM actuator, polydimethylsiloxane (PDMS) membrane and a micro chamber. The flow channels and chamber are made of cured SU-8 polymer. The hydrogen gas bubbles were generated in the valve microchamber with Pt black electrodes (coated with platinum nanoparticles) and filled with 1 M of NaCl solution. The nano particles coated on the working electrode helps to boost the surface-to-volume ratio of the electrode for faster reversible electrolysis and faster valve operation. To test the functionality of the microvalve, a simple micropump based on ECM principle was also integrated in the system to deliver a microscopic volume of fluid through the valve. The experimental results have showed that an approximately 300 μm deflection of valve membrane was achieved by applying a bias voltage of ?1.5 V across the electrodes. The pressure in the valve chamber was estimated to be about 200 KPa. Experimental results proved that the valve can be easily operated by controlling the electrical signals supplied to the ECM actuators.     

A fast identification algorithm with outliers under Box-Cox transformation-based annealing robust radial basis function networks

In this article, a Box-Cox transformation-based annealing robust radial basis function networks (ARRBFNs) is proposed for an identification algorithm with outliers. Firstly, a fixed Box-Cox transformation-based ARRBFN model with support vector regression (SVR) is derived to determine the initial structure. Secondly, the results of the SVR are used as the initial structure in the fixed Box-Cox transformation-based ARRBFNs for the identification algorithm with outliers. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for the fixed Box-Cox transformation-based ARRBFNs, and applied to adjust the parameters and weights. Hence, the fixed Box-Cox transformation-based ARRBFNs with an ARLA have a fast convergence speed for an identification algorithm with outliers. Finally, the proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with Box-Cox transformation-based radial basis function networks.