A numerical study of dynamic behaviors of a unitized regenerative fuel cell during gas purging

The gas purging states affect electricity output and energy storage capacity of unitized regenerative fuel cells. In this study, a model of unitized regenerative fuel cell is established. Cell voltages and operating temperatures influences on the dynamic distribution of thermal fluid during purging process and the discharge of residual liquid water in electrolytic cell mode are investigated. The motivation of the present study is better understanding the gas purging characteristics and its effect on reaction behaviors of unitized regenerative fuel cells. Simulation results reveal a significant influence of purging gas temperature on the water flooding and a great effect of operating voltage on the water diffusion. The operating temperature of electrolytic cell model almost has little effect on purging results at different cell temperature and the same purging gas temperature. When the purging gas temperature is changed, higher temperatures of cell and purging gas facilitate liquid water discharging out from the cell regions. In cell water flooding situation, when having large liquid content, the purging gas has little effects on the water expelling process.     

Novel one- and two-dimensional InSbS3 semiconductors for photocatalytic water splitting: The role of edge electron states

Photocatalytic water splitting has become a promising technology to solve environmental pollution and energy shortage. Exploring stable and efficient photocatalysts are highly desired. Herein, we propose novel low-dimensional InSbS₃ semiconductors with good stability based on density functional theory. Such InSbS₃ structures could be obtained from their bulk crystal by suitable exfoliation methods. Our calculations indicate that two-dimensional (2D) and one-dimensional (1D) InSbS₃ nanostructures have moderate band gaps (2.54 and 1.97 eV, respectively) and suitable band edge alignments, which represents sufficient redox capacity for photocatalytic water splitting. 2D InSbS₃ monolayer possesses oxygen evolution reaction (OER) activity and 1D InSbS₃ single-nanochain possesses hydrogen evolution reaction (HER) activity under acidic conditions. Interestingly, two edge electron states can be introduced when the dimension of InSbS₃ is reduced from 2D to 1D and the new electron states can exist in arbitrary-width nanoribbons, which can effectively promote the process of HER. Moreover, InSbS₃ monolayer and single-nanochain also exhibit large solar-to-hydrogen efficiency, high carrier mobility, and excellent optical absorption properties, which can facilitate the process of photocatalytic reactions. Our findings can stimulate the synthesis and applications of low-dimensional InSbS₃ semiconductors for overall water splitting.     

Simply constructed highly dispersed cobalt nanoparticles in diverse N-doped graphitic carbon with remarkable performances for water electrolysis

Metal/carbon composite materials are highly promising electrocatalysts for water electrolysis. In this work, three composites of metal cobalt nanoparticles highly dispersed in N-doped carbon materials were facilely constructed by pyrolysis of different phenylenediamine based Schiff base-Co complexes (PDBs). Interestingly, the composites derived from PDBs based on different phenylenediamine exhibited different morphologies. The superior case is that rodlike composite catalyst was derived from o-phenylenediamine based PDBs. The obtained catalyst exhibited remarkable performances for both cathodic hydrogen evolution reaction (HER) and anodic oxygen evolution reaction (OER), as well as overall water electrolysis. Only 172 and 289 mV of overpotentials and 1.57 V of cell voltage were exhibited at 10 mA cm^?2 for HER, OER and water splitting in 1.0 M KOH, respectively. The catalyst also displayed robust stability and high Faraday efficiency, and thus are potential high-performance catalyst for commercial water electrolysis.     

Approbation of an innovative method of pretreatment of dark fermentation feedstocks

According to the International Energy Agency, only a small part of the full potential of biomass energy is currently used in the world. The annual amount of agricultural waste in the Russian Federation is estimated at about 152 million tons, and the energy potential of animal waste is 201 PJ/year. Anaerobic digestion is an efficient method of converting organic waste into renewable energy sources. Previously, the positive effect of pretreatment of various organic feedstocks in vortex layer apparatus (VLA) on the characteristics of anaerobic digestion and energy efficiency was shown. Currently, there is a significant interest in the world in obtaining biohydrogen from organic waste using the dark fermentation (DF) process. During pretreatment in the VLA, the iron working bodies are abraded and iron particles are introduced into the feedstock of the DF reactor. This may have a positive effect on the production rate and yield of hydrogen, which has not been previously studied. This work is aimed at evaluating the possibility of using the VLA as a method for pretreatment of a dark fermentation feedstock for the intensification of biohydrogen production. To achieve this goal, an experimental setup was constructed. It consisted of a 45 L DF reactor, a VLA and a process control system to collect data on the DF process parameters every 5 min. At a hydraulic retention time in the DF reactor of 24 h and in the VLA of 30 s, the hydrogen content in the biogas increased from 51.1% to 52.2%. At the same time, the pH increased from 3.85 to 4.8–4.9, and the hydrogen production rate increased by 16% to 1.941 L/(L day). The hydrogen yield was 80.9 ml/g VS. Thus, pretreatment of the feedstock in VLA can be an effective way to intensify the DF process; however, further study of the VLA operating modes is required in order to optimize the concentrations of iron particles introduced into the feedstock for the most efficient continuous production of dark fermentative biohydrogen.     

受老空水威胁工作面巷道防治水实践

针对高庄煤矿3_上1104回风巷为沿空掘巷,巷道掘进期间受3_上1102老空水威胁,掘进开采存在严重的水患安全问题,采用超前打设疏水孔的方案来治理老空水威胁,对巷道采用调向增大护巷煤柱厚度等措施实现巷道安全快速掘进,探放水期间无淹巷情况发生,探放水后巷道顶板和上帮无淋水现象发生。     

Adaptive investigation of the optical properties of polymer fibers from mixing noisy phase shifting microinterferograms using deep learning algorithms

In this article, an adaptive denoising method is suggested to accurate investigate the optical and structural features of polymeric fibers from noisy phase shifting microinterferograms. The mixed class of noise that may produce in the phase-shifting interferometric techniques is established. To our knowledge, this is an early study considered the mixing noises that may occur in microinterferograms. The suggested method utilized the convolution neural networks to detect the noise class and then denoising, it according to its class. Four convolution neural networks (Googlenet, VGG-19, Alexnet, and Alexnet–SVM) are refined to perform the automatic classification process for the noise class in the established data set. The network with the highest validation and testing accuracy of these networks is considered to apply the suggested method on realistic noisy microinterferograms for polymeric fibers, polypropylene and antimicrobial polyethylene terephthalate)/titanium dioxide, recoded using interference microscope. Also, the suggested method is applied on noisy microinterferograms include crazing and nanocomposite material. The demodulated phase maps and the three-dimensional birefringence profiles are calculated for tested fibers according to the suggested method. The obtained results are compared with the published data for these fibers and found to be in good agreements.     

混凝土结构的压电声波检测分析

本文介绍了混凝土结构的压电体波和表面波检测的主要进展,对两种压电声波检测的优缺点进行了总结。体波检测设备一般埋入混凝土内部,需要选择合理的检测部位,检测结果较为精确;声表面波检测无需选择特定的部位,但是检测深度有限。在实际检测工作过程中,可以联合两种方法相互验证。     

Deep Learning Based Process Analytics Model for Predicting Type 2 Diabetes Mellitus

Process analytics is one of the popular research domains that advanced in the recent years. Process analytics encompasses identification, monitoring, and improvement of the processes through knowledge extraction from historical data. The evolution of Artificial Intelligence (AI)-enabled Electronic Health Records (EHRs) revolutionized the medical practice. Type 2 Diabetes Mellitus (T2DM) is a syndrome characterized by the lack of insulin secretion. If not diagnosed and managed at early stages, it may produce severe outcomes and at times, death too. Chronic Kidney Disease (CKD) and Coronary Heart Disease (CHD) are the most common, long-term and life-threatening diseases caused by T2DM. Therefore, it becomes inevitable to predict the risks of CKD and CHD in T2DM patients. The current research article presents automated Deep Learning (DL)-based Deep Neural Network (DNN) with Adagrad Optimization Algorithm i.e., DNN-AGOA model to predict CKD and CHD risks in T2DM patients. The paper proposes a risk prediction model for T2DM patients who may develop CKD or CHD. This model helps in alarming both T2DM patients and clinicians in advance. At first, the proposed DNN-AGOA model performs data preprocessing to improve the quality of data and make it compatible for further processing. Besides, a Deep Neural Network (DNN) is employed for feature extraction, after which sigmoid function is used for classification. Further, Adagrad optimizer is applied to improve the performance of DNN model. For experimental validation, benchmark medical datasets were used and the results were validated under several dimensions. The proposed model achieved a maximum precision of 93.99%, recall of 94.63%, specificity of 73.34%, accuracy of 92.58%, and F-score of 94.22%. The results attained through experimentation established that the proposed DNN-AGOA model has good prediction capability over other methods.     

A Tradeoff Between Accuracy and Speed for K-Means Seed Determination

With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected by the selection in the primary clusters. Lack of attention to this crucial issue has consequences such as creating empty clusters and decreasing the convergence time. Besides, the selection of appropriate initial seeds can reduce the cluster’s inconsistency. In this paper, we present a new method to determine the initial seeds of the k-mean algorithm to improve the accuracy and decrease the number of iterations of the algorithm. For this purpose, a new method is proposed considering the average distance between objects to determine the initial seeds. Our method attempts to provide a proper tradeoff between the accuracy and speed of the clustering algorithm. The experimental results showed that our proposed approach outperforms the Chithra with 1.7% and 2.1% in terms of clustering accuracy for Wine and Abalone detection data, respectively. Furthermore, achieved results indicate that comparing with the Reverse Nearest Neighbor (RNN) search approach, the proposed method has a higher convergence speed.