Investigation on hydrogen-fueled combustion characteristics and thermal performance in a micro heat-recirculation combustor inserted with block

Numerical investigation on the premixed H₂/air combustion in a micro heat-recirculation combustor inserted with/without block is conducted. Effects of block setting, heat-recirculation, and flow rate on combustion characteristics and thermal performance are depicted and analyzed. The results demonstrate that the block enhances the flame stability and preheating effect, which also reduces the heat loss via exhaust gas, while it shortens reactants residence time. The combustor setting with a transverse block gains a better thermal performance than that inserted with a longitudinal block. With the increase of transverse block height, the high-temperature zone is broadened and radiation is improved. However, the block with a height of 10 mm separates the fluid field and weakens the effects of heat recirculation, leading to a lower outer wall temperature. Furthermore, the appropriate block insertion method and height contribute to the significant improvement of heat transfer, radiant efficiency and further optimization of micro power generator.     

Numerical and experimental investigation of a thermosiphon solar water heater system thermal performance used in domestic applications

The thermosiphon is a passive heat exchange method, which circulates a fluid within a system without the need for any electrical or mechanical pumps. The thermosiphon is based on natural convection where the thermal expansion occurs when the temperature difference has a corresponding difference in density across the loop. Thermosiphons are used in different applications such as solar energy collection, automotive systems, and electronics. The current study aims to investigate thermosiphon thermal performance used in domestic applications. The thermal performance of a thermosiphon has been studied by many researchers; however, according to the knowledge of the authors, the influence of the amount of the working fluid on the thermal output has not yet been investigated. Therefore, the influence of the amount of working fluid within the riser pipe has been investigated on the thermal performance of the thermosiphon. In the current study, a computational fluid dynamics model is involved. This model has been validated by comparison with experimental findings. The maximum variation between numerical and experimental results is 14.2% and 11.2% for the working fluid at the inlet and outlet of the absorber pipe, respectively. Furthermore, the results show that the amount of working fluid inside the closed thermosiphon has a great influence on the thermal performance of the system. Additionally, it is found that Case-B, when the amount of working fluid is less than by 10% compared to the traditional model, is the best case among all cases under study. Furthermore, a correlation equation to predict water temperature at the exit of the absorber pipe has been established with an accuracy of 95.05%.     

Mild hydrogen production from the hydrolysis of Al–Bi–Zn composite powder

The development of mild hydrogen-generating materials is of great significance to improve the working life of hydrogen–oxygen fuel cells. In this study, Al–Bi–Zn composite powders were designed by phase diagram calculation and then prepared via the gas atomization method. The results indicated that the conversion yield of Al–12Bi–7Zn (wt.%) powder reached 98% with stable hydrogen production within 280 min at 50 °C. When composite powder reacted with NaCl solution to produce hydrogen, the dormant period of the reaction process was significantly shortened, but the conversion rate was slightly reduced. Additionally, the evolution of powder morphology during the reaction was investigated. The results showed that the continuous cracking of the powder led to the continuous exposure of fresh Al to react.     

Novel cobalt-free Pr2Ni1-xNbxO4 (x = 0, 0.05, 0.10, and 0.15) perovskite as the cathode material for IT-SOFC

The cobalt-free cathode materials with high activity is critical to the commercial application of medium temperature solid oxide fuel cells (IT-SOFC). Herein, a series of cobalt-free Nb-doped Pr₂Ni_1-xNb_xO₄ (x = 0, 0.05, 0.10, and 0.15) perovskite oxides were successfully prepared, and the effects of Nb-doping on the structure, thermal stability and electrochemical properties of the cathode material are studied in detail. The Pr₂Ni_1-xNb_xO₄ exhibits a K₂NiF₄ type structure with Fmmm space group. The Nb⁵⁺ cations that doped into Pr₂NiO₄ replaces the Ni site, which increases the surface oxygen content, then effectively eliminates the phase transition of Pr₂NiO₄ and significantly improves the ORR catalytic activity. The Pr₂Ni_0.9Nb_0.1O₄ was found to occupy the lowest polarization resistance of 0.057 Ω cm², and the peak power density of single cells supported by the electrolyte is 0.576 W cm^?2 at 700 °C, which has good long-term stability.     

In situ growth of nickel phosphide nanoparticles on inner wall of graphitic carbon nitride tubes for efficient photocatalytic hydrogen evolution

A facile two-step approach is employed to prepare novel Ni₂P@CNT hybrid photocatalyst, which is assembled by nickel phosphide (Ni₂P) nanoparticles on the inner wall of graphitic carbon nitride tube (CNT). This unique microstructure endows Ni₂P@CNT with close interfacial interaction, promotes efficient separation of photoexcited charge carriers and provides enriched sites for photocatalytic reaction. Moreover, the hybrid system is found to exhibit more superior photocatalytic hydrogen evolution activity than pure CNT and Pt-decorated CNT (Pt@CNT). As a consequence, the work illustrates the essential role of experimental process on the final morphology and performance, which is expected to pave a new method to construct various kind of excellent photocatalyst.     

Platinum nanoparticles decorated on graphitic carbon nitride-ZIF-67 composite support: An electrocatalyst for the oxidation of butanol in fuel cell applications

In the present study, we report an eco-friendly and simple route to design and synthesize novel nanocomposite catalyst based on platinum nanoparticles anchored on binary support of graphitic carbon nitride (g-C₃N₄) and cobalt-metal-organic framework (ZIF-67). For this purpose, ZIF-67 was prepared by precipitation method and g-C₃N₄ was prepared through thermal polymerization method. Later, ZIF-67 and g-C₃N₄ were hybridized through sonication to get homogeneous g–C₃N₄–ZIF-67 nanocomposite support material. Platinum nanoparticles (PtNPs) were uniformly deposited on g–C₃N₄–ZIF-67 by an electrochemical method. The as-developed nanocatalyst was characterized by morphological, structural and electrochemical techniques. The electrocatalytic activity of PtNPs@g–C₃N₄–ZIF-67 nanocatalyst towards butanol oxidation was evaluated via CV, CA, LSV and EIS in an alkaline medium. Results revealed that the proposed catalyst showed greatly enhanced electrooxidation of butanol in terms of high magnificent current density, lower oxidation potential, excellent long-term stability, large surface area, low charge transfer resistance and less toxic ability. Enhanced catalytic performance of the proposed catalyst could be ascribed to the synergistic effect of g–C₃N₄–ZIF-67 nanocomposite and PtNPs. The PtNPs@g–C₃N₄–ZIF-67 catalyst holds promising potential applications to be used as an anodic electrocatalyst for the development of high-performance alkaline fuel cells.     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.     

超高效液相色谱-串联质谱法同时检测糕点中6种甜味剂

目的建立超高效液相色谱-串联质谱法同时测定糕点中6种常用合成甜味剂的分析方法。方法选用超纯水作为提取溶剂,涡旋和超声提取后,低温离心,取部分上清液加入正己烷除脂,Waters Atlantis■T3色谱柱、甲醇-5 mmol/L甲酸铵(含0.1%甲酸)作为流动相、亲水亲脂平衡型固相萃取柱HLB(hydrophile-lipophile balance)净化。结果6种甜味剂在质量浓度为10~200 ng/mL的曲线范围内呈良好线性关系,相关系数r均大于0.999,平均加标回收率在85.0%-98.2%之间,相对平均偏差(relative standard deviation,RSD)为1.3%~6.7%。结论该方法具有前处理简单、灵敏度高、检测速度快等优点,适合糖精钠、甜蜜素、三氯蔗糖、阿斯巴甜、阿力甜、纽甜的检测,但不适用于安赛蜜的检测。     

Understanding the impact of High Speed Railway on urban innovation performance from the perspective of agglomeration externalities and network externalities

Transportation infrastructure and innovation play an important role in promoting regional economic growth. However, previous studies have mainly focused on the economic agglomeration by increased accessibility, but ignored the important on inter-city economic linkages and the network externalities it generated. Based on the externality perspective, this paper uses the propensity score matching model (PSM-DID) and spatial econometric model to analyze the heterogeneous impact of HSR opening on urban innovation performance and compare the differential effects of agglomeration externalities and network externalities on knowledge spillovers. The empirical results suggest the following: (1) The opening of HSR significantly improves urban innovation performance, but there is significant spatial heterogeneity. (2) The opening of HSR not only promotes the agglomeration of production factors to cities, but also strengthens inter-city economic connections, thus improving urban innovation performance through agglomeration externalities and network externalities. However, compared to agglomeration externalities, network externalities have a superior strength of impact on urban innovation performance. (3) From the dynamic effect perspective, the opening of HSR has a hysteresis effect on urban innovation of 2–3 years, and differences exist between cities.     

Performance improvement of microbial fuel cell through artificial intelligence

The current work introduces an enhancement in the performance of the microbial fuel cell through estimating the optimal set of controlling parameters. The maximization of both power density (PD) and the percentage of chemical oxygen demand (COD) removal were considered as the enhancement in the cell's performance. Three main parameters in terms of performance as well as commercialization are the system's inputs; the Pt which takes the range of 0.1‐0.5 mg/cm², the degree of sulphonation in sulfonated‐poly‐ether‐ether‐ketone that changes in the range of 20‐80%, and the rate of aeration of cathode which varies between 10 and 150 mL/min. From the experimental dataset, two robust adaptive neuro‐fuzzy inference system models based on the fuzzy logic technique have been constructed. The comparisons between the models' outputs and the experimental data showed well‐fitting in both training and testing datasets. The mean squared errors of the PD model, for testing and whole datasets, were found 2.575 and 0.909 while for the COD model it showed 19.242 and 6.791, respectively. Then, based on the two fuzzy models, a Particle Swarm Optimization algorithm has been used to determine the best parameters that maximize both of the PD and the COD removal of the cell. The optimization process was utilized for single and multi‐object optimization processes. In the single optimization, the resulting maximums of the PD and the COD removal were found 62.844 (mW/m²) and 99.99 (%), respectively. Whereas, in the multi‐object optimization, the values of 61.787 (mW/m²) and 96.21 (%) were reached as the maximums for the PD and COD, respectively. This implies that, in both cases of optimization processes, the adopted methodology can efficiently enhance the microbial fuel cell performances than the previous work.