Progressive NO2 Sensors with Rapid Alarm and Persistent Memory-Type Responses for Wide-Range Sensing Using Antimony Triselenide Nanoflakes

Antimony triselenide (Sb₂Se₃) nanoflake-based nitrogen dioxide (NO₂) sensors exhibit a progressive bifunctional gas-sensing performance, with a rapid alarm for hazardous highly concentrated gases, and an advanced memory-type function for low-concentration (<1 ppm) monitoring repeated under potentially fatal exposure. Rectangular and cuboid shaped Sb₂Se₃ nanoflakes, comprising van der Waals planes with large surface areas and covalent bond planes with small areas, can rapidly detect a wide range of NO₂ gas concentrations from 0.1 to 100 ppm. These Sb₂Se₃ nanoflakes are found to be suitable for physisorption-based gas sensing owing to their anisotropic quasi-2D crystal structure with extremely enlarged van der Waals planes, where they are humidity-insensitive and consequently exhibit an extremely stable baseline current. The Sb₂Se₃ nanoflake sensor exhibits a room-temperature/low-voltage operation, which is noticeable owing to its low energy consumption and rapid response even under a NO₂ gas flow of only 1 ppm. As a result, the Sb₂Se₃ nanoflake sensor is suitable for the development of a rapid alarm system. Furthermore, the persistent gas-sensing conductivity of the sensor with a slow decaying current can enable the development of a progressive memory-type sensor that retains the previous signal under irregular gas injection at low concentrations.     

基于PBFT区块链技术的电网企业仓储系统

提出了一种基于实用拜占庭容错(PBFT)算法的区块链技术,首先对传统的实用拜占庭容错算法原理进行了阐述,该传统算法包含前期、需求、预准备、准备、确认、答复6个阶段,但传统算法具有实时性差、缺乏惩罚机制、带宽高的缺点。针对出现的这些问题,又对传统算法进行了改进,具体涉及记账节点、共识过程以及视图切换过程。通过测试进一步证明了该改进算法的实用性,并将该算法应用于电网企业中,构建的虚拟仓库实现了联储联备,降低了库存资金的耗费,并且提高了电网企业库存管理的效率。     

Experimental investigation of supercapacitor based regenerative energy storage for a fuel cell vehicle equipped with an alternator

Recently, researchers have devoted more attention to supercapacitors (SCs) to integrate with batteries in energy storage systems (ESSs) for vehicle applications. In this study, we attempted to characterize the use of SCs in the ESS for a PEM fuel cell vehicle equipped with an alternator to maximize the performance of regenerative braking. We applied lithium-ion batteries (LIBs) and SCs as energy storage devices to examine their effect on ESS. Then we used a hysteresis brake to apply controllable braking force on the flywheel to form hybrid braking (HB) and made efforts to study its behavior to suggest a braking control strategy. We also ran the whole system over the rotational speed to cover the range of driving speed. At last, we sized the SCs for the most commonly used fuel cell electric vehicle (FCEV) in Korea, i.e., Hyundai NEXO, based on the results obtained from the above study by alternator efficiencies.     

Performance enhancement of methanol reforming reactor through finned surfaces and diffused entry for on-board hydrogen generation

Hydrogen-rich combustion in engines helps in reducing pollutants significantly. But hydrogen usage on a moving vehicle is not getting large-scale user acceptance mainly due to its poor energy storage density resulting in shorter driving ranges. This storage issue led to the hunt for mediums that can efficiently produce on-board hydrogen. Methanol proves to be an efficient alcohol fuel for producing hydrogen through steam reforming reaction. The heat energy required for such endothermic reaction is obtained through exhaust engine waste energy and this process is collectively known as thermochemical recuperation. However, the conventional reactor used for this process faces a lot of problems in terms of efficiency and methanol conversion. In this study, an attempt has been made to improve the design of the reactor for on-board hydrogen generation using engine exhaust heat for addressing the challenges related to performance and hydrogen yield. For enhancing the heat transfer, a finned surface (straight & wavy) was introduced in the reactor which resulted in an increment in methanol conversion significantly. It was found that wavy fin improved the methanol conversion up to 96.8% at an exhaust inlet temperature of 673 K. Also, a diffusing inlet section was introduced to increase the residence time of reactant gases while passing through the catalyst zone. Under given inlet conditions, the methanol conversion for 6° diffuse inlet reactor goes up to 87.9% as compared to 75.4% for the conventional reactor.     

自重湿陷性黄土地基预钻孔浸水处理的可靠度设计方法

为解决传统预浸水法存在的浸水时间长、浸水处理范围难以确定等不足,基于土体中水分运移规律,依据可靠度理论、极限状态设计方法及复合 Poisson 过程原理,提出一种消除黄土湿陷性的处理浸水方法——预钻孔浸水法。给出了利用预钻孔浸水法对自重湿陷性黄土地基进行浸水时,水平向及竖直向浸水影响范围的确定模型;在此基础上结合达西定律给出了浸水孔设计参数如孔深、孔间距及浸水孔个数的确定方法。结合铜川某工程,设计进行了现场预钻孔浸水试验,对该方法的合理性进行了验证,并通过现场钻探、现场勘探、室内湿陷性试验等方法对该方法的处理效果进行了评价。该浸水方法具有浸水时间短、浸水影响范围可根据浸水孔布设进行控制等优点,且浸水处理效果良好,完全符合施工要求。     

电信专业学术英文写作模块化教学实践

学术英文写作是当今电子信息专业研究生必修的一门课程。然而传统的以教师为中心的灌输式教学已不能跟上网络迅猛发展的趋势,尤其是当疫情来临的特殊形势下。此外,现有的学术英文写作教学研究仅提供宏观的设计,缺乏具体的实际可操作性。为此,本文以引言写作逻辑为教学模块案例,探索了如何充分利用网络资源的优势,开展以学生为中心的课前课中课下有机融合的灵活的教学设计。所探索的引言写作技巧和详实的教学设计,为模块化网络教学提供一个切实可行的参考方案。     

Statistical optimization of operating parameters of microbial electrolysis cell treating dairy industry wastewater using quadratic model to enhance energy generation

The performance of Microbial electrolysis cell (MEC) is affected by several operating conditions. Therefore, in the present study, an optimization study was done to determine the working efficiency of MEC in terms of COD (chemical oxygen demand) removal, hydrogen and current generation. Optimization was carried out using a quadratic mathematical model of response surface methodology (RSM). Thirteen sets of experimental runs were performed to optimize the applied voltage and hydraulic retention time (HRT) of single chambered batch fed MEC operated with dairy industry wastewater. The operating conditions (i.e) an applied voltage of 0.8 V and HRT of 2 days that showed a maximum COD removal response was chosen for further studies. The MEC operated at optimized condition (HRT- 2 days and applied voltage- 0.8 V) showed a COD removal efficiency of 95 ± 2%, hydrogen generation of 32 ± 5 mL/L/d, Power density of 152 mW/cm² and current generation of 19 mA. The results of the study implied that RSM, with its high degree of accuracy can be a reliable tool for optimizing the process of wastewater treatment. Also, dairy industry wastewater can be considered to be a potential source to generate hydrogen and energy through MEC at short HRT.     

An active balancer with rapid bidirectional charge shuttling and adaptive equalization current control for lithium-ion battery strings

This article proposes an active balancer, which features bidirectional charge shuttling and adaptive equalization current control, to fast counterbalance the state of charge (SOC) of cells in a lithium-ion battery (LIB) string. The power circuit consists of certain bidirectional buck-boost converters to transfer energy among the different cells back and forth. Owing to the characterization of the open-circuit voltage (OCV) vs SOC in LIB being relatively smooth near the SOC middle range, the SOC-inspected balance strategy can achieve more precise and efficient equilibrium than the voltage-based control. Accordingly, a compensated OCV-based SOC estimation is put forward to take into account the discrepancy of SOC estimation. Besides, the varied-duty-cycle (VDC) and curve-fitting modulation (CFM) methods are devised herein to tackle the problems of slow equalization rate and low balance efficacy, which arise from the diminution in balancing current as the SOC difference between the cells decreases in the later duration of equalization especially. The proposed strategies have taken the battery nonlinear characteristic and circuit parameter nonideality into account and can adaptively modulate the duty cycle with the SOC difference to keep balancing current constant throughout the balancing cycle. Simulated and experimental results are given to demonstrate the feasibility and effectiveness of the same prototype constructed. Compared with the fixed duty cycle and the VDC methods, the proposed CFM has the best balancing efficiency of 81.4%, and the balance time is shortened by 27.1% and 18.6%, respectively.     

Application of robust machine learning methods to modeling hydrogen solubility in hydrocarbon fuels

Having accurate information about the hydrogen solubility in hydrocarbon fuels and feedstocks is very important in petroleum refineries and coal processing plants. In the present work, extreme gradient boosting (XGBoost), multi-layer perceptron (MLP) trained with Levenberg–Marquardt (LM) algorithm, adaptive boosting support vector regression (AdaBoost?SVR), and a memory-efficient gradient boosting tree system on adaptive compact distributions (LiteMORT) as four novel machine learning methods were used for estimating the hydrogen solubility in hydrocarbon fuels. To achieve this goal, a database containing 445 experimental data of hydrogen solubilities in 17 various hydrocarbon fuels/feedstocks was collected in wide-spread ranges of operating pressures and temperatures. These hydrocarbon fuels include petroleum fractions, refinery products, coal liquids, bitumen, and shale oil. Input parameters of the models are temperature and pressure along with density at 20 °C, molecular weight, and weight percentage of carbon (C) and hydrogen (H) of hydrocarbon fuels. XGBoost showed the highest accuracy compared to the other models with an overall mean absolute percent relative error of 1.41% and coefficient of determination (R²) of 0.9998. Also, seven equations of state (EOSs) were used to predict hydrogen solubilities in hydrocarbon fuels. The 2- and 3-parameter Soave-Redlich-Kwong EOS rendered the best estimates for hydrogen solubilities among the EOSs. Moreover, sensitivity analysis indicated that pressure owns the highest influence on hydrogen solubilities in hydrocarbon fuels and then temperature and hydrogen weight percent of the hydrocarbon fuels are ranked, respectively. Finally, Leverage approach results exhibited that the XGBoost model could be well trusted to estimate the hydrogen solubility in hydrocarbon fuels.