40SiO2–40P2O5–20ZrO2 sol-gel infiltrated sSEBS membranes with improved methanol crossover and cell performance for direct methanol fuel cell applications

Membranes commonly used in direct methanol fuel cell (DMFC) are expensive and show a great permeability to methanol which reduces fuel utilization and leads to mixed potential at the cathode. In this work, sulfonated styrene-ethylene-butylene-styrene (sSEBS) modified membranes with zirconia silica phosphate sol-gel phase are developed and studied in order to evaluate their potential use in DMFC applications. The synthesized hybrid membranes and sSEBS are subjected to an exhaustive physicochemical characterization by liquid uptake, ion exchange capacity, atomic force microscopy, X-ray photoelectron spectroscopy and dynamic mechanical and thermogravimetric analyses. Likewise, the potential use of the prepared membranes in DMFC is evaluated by means of electrochemical characterizations in single cell, determining the limiting methanol crossover current densities, proton conductivities and DMFC performances. The hybrid membranes show lower water and methanol uptakes, higher stiffness, water retention capability, upper power density and lower methanol crossover than sSEBS and Nafion 112.     

Experimental investigation on the effect of tangential force on wear and rolling contact fatigue behaviors of wheel material

The study aims to explore the effect of tangential force on wear and rolling contact fatigue (RCF) behaviors of wheel material using a JD-1 wheel/rail simulation facility. The normal, tangential and lateral forces between the wheel/rail rollers are controlled, and the magnetic power brake was used to generate the tangential forces (16–330 N). The results indicate that the surface hardness and wear loss of wheel rollers increase with the tangential force increasing. The surface cracks mouths are perpendicular to the resultant directions of the frictional forces. There are visible secondary cracks and multilayer cracks and the interlayer material of multilayer cracks are easy to break. The compositions of wear debris consist of Fe₂O₃, Fe₃O₄ and iron.     

Emotional design and positive emotions in multimedia learning: An eyetracking study on the use of anthropomorphisms

The present eyetracking study examined the influence of emotions on learning with multimedia. Based on a 2 × 2 experimental design, participants received experimentally induced emotions (positive vs. neutral) and then learned with a multimedia instructional material, which was varied in its design (with vs. without anthropomorphisms) to induce positive emotions and facilitate learning. Learners who were in a positive emotional state before learning had better learning outcomes in comprehension and transfer tests and showed longer fixation durations on the text information of the learning environment. Although anthropomorphisms in the learning environment did not induce positive emotions, the eyetracking data revealed that learners' attention was captured by this design element. Hence, learners in a positive emotional state who learned with the learning environment that included anthropomorphisms showed the highest learning outcome and longest fixation on the relevant information of the multimedia instruction. Results indicate an attention arousing effect of expressive anthropomorphisms and the relevance of emotional states before learning.     

Effect of fiber orientation on Liquid–Gas flow in the gas diffusion layer of a polymer electrolyte membrane fuel cell

In this study, the lattice Boltzmann method was used to simulate the three-dimensional intrusion process of liquid water in the gas diffusion layer (GDL) of a polymer electrolyte membrane fuel cell (PEMFC). The GDL was reconstructed by the stochastic method and used to investigate fiber orientation's influence on liquid water transport in the GDL of a PEMFC. The fiber orientation can be described by the angle between a single fiber and the in-plane direction; three different samples were simulated for three different fiber orientation ranges. The simulated permeability correlated well with the anisotropic characteristics of reconstructed carbon papers. It was concluded that the fiber orientation had a significant effect on the liquid invasion pattern in the GDL by changing the pore shape and distribution of the GDL. The results indicated that the stochastically reconstructed GDL, taking into account the fiber orientation, better demonstrates the mass transport properties of the GDL.     

Ambient effects on the electrical conductivity of carbon nanotubes

We show that the electrical conductivity of single walled carbon nanotubes (SWCNT) networks is affected by oxygen and air humidity under ambient conditions by more than a magnitude. Later, we intentionally modified the electrical conductivity by functionalization with iodine and investigated the changes in the band structure by optical absorption spectroscopy.Measuring in parallel the tubes electrical conductivity and optical absorption spectra, we found that conduction mechanism in SWCNT is comparable to that of intrinsically conducting polymers. We identified, in analogy to conducting polymers, in the infrared spectra a new absorption band which is responsible for the increased conductivity, leading to a closing gap in semiconducting SWCNT.We could show that by different functionalizations of the same SWCNT starting material the properties like conductivity can be dramatically changed, leading to different imaginable applications. We investigated here, an ultraviolet sensor with weakly modified SWCNT.     

陶瓷刀具干式车削淬硬钢试验研究

通过切削试验。得到了陶瓷刀具CC650干式车削渗碳淬硬钢20CrMnTi的磨损曲线。并利用扫描电子显微镜。观察了刀具的磨破损形貌，对刀具磨损区进行了元素含量的能谱分析。得出了刀具的磨损机理。     

A novel approach for sand liquefaction prediction via local mean-based pseudo nearest neighbor algorithm and its engineering application

The prediction method plays crucial roles in accurate prediction of sand liquefaction. Recently, machine learning has been widely used for prediction of sand liquefaction, and the Local Mean-based Pseudo Nearest Neighbor (LMPNN) algorithm, one of machine learning techniques, showed good performance in pattern recognition. In this study, we propose a sand liquefaction prediction model based on the LMPNN algorithm, which is the first work of applying the LMPNN algorithm to sand liquefaction prediction. Then, our proposed prediction model is used for evaluation of site liquefaction grade in Tongzhou District of China. And the comparison between our proposed prediction model with the liquefaction evaluation method in the Chinese code is made, which will provide an important approach to predicting the sand liquefaction grades for the major construction project sites. Extensive experiments on grade prediction demonstrate that the effectiveness of our proposed prediction model based on the LMPNN algorithm. In addition, shaking table test of an engineering site model is conducted for evaluating whether this engineering site model is liquefaction and non-liquefaction or not. And the experiment result of the shaking table test is the same as that of our proposed prediction model based on LMPNN algorithm, which further demonstrates the effectiveness of our proposed prediction model. Consequently, our proposed prediction model is proved to have a good prospect of engineering application in the liquefaction prediction.     

Performance enhancement of efficient process based on Carry-Skip Adder for IoT applications

IoT provides a platform for every device to be connected by means of a stable internet connection. The interoperability of the devices helps to communicate and exchange data between one another and increases the power consumption of devices. When creating a new IoT system or rebuilding the existing ones, the low power circuit design is considered as an essential factor. In the low power circuit design, the most challenging aspect to overcome is to reduce the leakage power, because the battery-operated devices are fast in draining energy when left long in the standby mode. Reducing the delay of a ripple carry adder can be only accomplished with Carry-Skip Adder (CSA) with minimal effort when compared to other techniques like a carry-look ahead adder. The carry skip adder belongs to the family of bypass adders, and its main aim is to improve the worst-case delay of the IoT device based on its area and power consumption. The CSA used in the proposed method for the IoT processor helps to increase the performance of the system relative to average power dissipation, leakage power, power delay product, and propagation delay.     

Automated ergonomic risk monitoring using body-mounted sensors and machine learning

Workers in various industries are often subject to challenging physical motions that may lead to work-related musculoskeletal disorders (WMSDs). To prevent WMSDs, health and safety organizations have established rules and guidelines that regulate duration and frequency of labor-intensive activities. In this paper, a methodology is introduced to unobtrusively evaluate the ergonomic risk levels caused by overexertion. This is achieved by collecting time-stamped motion data from body-mounted smartphones (i.e., accelerometer, linear accelerometer, and gyroscope signals), automatically detecting workers’ activities through a classification framework, and estimating activity duration and frequency information. This study also investigates various data acquisition and processing settings (e.g., smartphone’s position, calibration, window size, and feature types) through a leave-one-subject-out cross-validation framework. Results indicate that signals collected from arm-mounted smartphone device, when calibrated, can yield accuracy up to 90.2% in the considered 3-class classification task. Further post-processing the output of activity classification yields very accurate estimation of the corresponding ergonomic risk levels. This work contributes to the body of knowledge by expanding the current state in workplace health assessment by designing and testing ubiquitous wearable technology to improve the timeliness and quality of ergonomic-related data collection and analysis.     

A computational study of the effects of multiphase dynamics in catalytic upgrading of biomass pyrolysis vapor

A recurring challenge among the variety of existing biomass‐to‐biofuel conversion technologies is the need to ensure optimal and homogeneous contact between the various phases involved. The formulation of robust design rules from an empirical standpoint alone remains difficult due to the wide range of granular flow regimes coexisting within a given reactor. In this work, a volume‐filtered Eulerian‐Lagrangian framework is employed that solves chemically reacting flows in the presence of catalytic particles. The simulation strategy is used to quantify the role of the particle clustering on catalytic upgrading of biomass pyrolysis vapor in risers. It is shown that particle clustering can reduce the catalytic conversion rate of biomass pyrolysis vapors by up to about 50%. The simulation results are also compared with an engineering model based on continuously stirred tank reactor (CSTR). A one‐dimensional Reynolds‐averaged transport equation is derived, and the unclosed terms that account for the heterogeneity caused by clusters are evaluated. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3341–3353, 2018