Humidification strategy for polymer electrolyte membrane fuel cells – A review

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.apenergy.2018.08.125 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Polymer electrolyte membrane fuel cells are promising power sources because of their advantage such as high efficiency, zero emission and low operating temperature. Water management is one of the critical issues for polymer electrolyte membrane fuel cells and has received significant attention. The membrane within the fuel cell needs to stay in hydrated state to have high ion conductivity and durability, which requires proper humidification. Both internal and external methods have been utilized to humidify the polymer electrolyte membrane. Numerous studies on fuel cell humidification have been conducted in the past decades, especially in recent years. This review aims to summarize the main humidification methods and the related studies. The internal humidification methods are classified as physical methods and chemical methods. The external humidification methods include gas bubbling humidification, direct water injection, enthalpy wheel humidification, membrane humidifiers, and exhaust gas recirculation. The working principle and performance of each method are introduced and the advantage and drawback are summarized. Further, the humidification methods for alkaline anion exchange membrane fuel cells are also briefly reviewed, because of more recent studies showing their potential of using non-precious metal catalysts. This review can help to choose proper humidification strategy for specific polymer electrolyte membrane fuel cell application and may inspire further investigations.National Natural Science Foundation of China ["51706153"]Natural Science Foundation of Tianjin City ["17JCZDJC3100"]Natural Sciences and Engineering Research Council of Canad

Knockdown of ZNF268, which Is Transcriptionally Downregulated by GATA-1, Promotes Proliferation of K562 Cells

The human ZNF268 gene encodes a typical KRAB-C2H2 zinc finger protein that may participate in hematopoiesis and leukemogenesis. A recent microarray study revealed that ZNF268 expression continuously decreases during erythropoiesis. However, the molecular mechanisms underlying regulation of ZNF268 during hematopoiesis are not well understood. Here we found that GATA-1, a master regulator of erythropoiesis, repressed the promoter activity and transcription of ZNF268. Electrophoretic mobility shift assays and chromatin immunoprecipitation assays showed that GATA-1 directly bound to a GATA binding site in the ZNF268 promoter in vitro and in vivo. Knockdown of ZNF268 in K562 erythroleukemia cells with specific siRNA accelerated cellular proliferation, suppressed apoptosis, and reduced expression of erythroid-specific developmental markers. It also promoted growth of subcutaneous K562-derived tumors in nude mice. These results suggest that ZNF268 is a crucial downstream target and effector of GATA-1. They also suggest the downregulation of ZNF268 by GATA-1 is important in promoting the growth and suppressing the differentiation of K562 erythroleukemia cells

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Numerical Study on the Fatigue Limit of Metallic Glasses under Cyclic Tension-Compression Loading

Numerical study was performed to determine the fatigue limit of metallic glass under tension-compression cyclic loading. A revised free-volume theory which considers the hydrostatic stress was utilized to make the predictions. Systematical simulations showed that a higher strain amplitude is prone to making the sample completely damaged earlier. However, lower strain fluctuations could result in a longer fatigue life. Shear banding evolution history described by free-volume localization could reasonably explain the mechanical responses of different samples. In addition, compressive loading could give rise to a higher stress than that under tensile loading because of hydrostatic stress contribution. In the end, a correlation between fatigue life and applied strain amplitude was plotted which could supply a guidance for designing the engineering application of metallic glass under periodic loading

A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN

Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time

A Novel Closed-Loop System for Vehicle Speed Prediction Based on APSO LSSVM and BP NN

Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization–least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training time is 15 min within 5 s, which is advantageous over BPNN. Furthermore, fuel consumption increases by 6.95% compared with the dynamic-programming algorithm and decreased by 5.6%~10.9% compared with the low accuracy of speed prediction. Overall, the proposed method is crucial for optimizing EMS as it is not only effective in improving prediction accuracy but also capable of reducing training time

Effect of Cyclic Cryogenic Treatment on Wear Resistance, Impact Toughness, and Microstructure of 42CrMo Steel and Its Optimization

Cyclic cryogenic treatment, a major cycle accompanied by zero or more subsidiary cycles, was conducted on the hardened 42CrMo steel using orthogonal design method to investigate the effect of different parameters (cryogenic temperature, holding time, and cycles number) of cryogenic treatment on wear resistance and impact toughness of the steel. Range analysis was performed to obtain the influencing order of the three parameters and their optimum values. The results show that after cryogenic treatment, the steel exhibits higher wear resistance and impact toughness, whereas no significant change in hardness. For wear resistance, the influencing order of parameters is cryogenic temperature, holding time, and cycles number, and the optimum values of the parameters are −160°C, 24 h and two cycles, respectively. For impact toughness, the influencing order of parameters is cryogenic temperature, cycles number, and holding time, and the optimum values are −120°C, 24 h and three cycles, respectively. The wear topography and fracture topography were examined using scanning electronic microscopy (SEM) to investigate the wear mechanism and fracture mechanism of the steel after cryogenic treatment, respectively. The results show that after cryogenic treatment, the wear mechanism is the combination of abrasive wear and adhesive wear with oxidative wear, and the fracture mechanism is a quasicleavage fracture. The microstructure was also examined by SEM to investigate the influencing mechanism of cryogenic treatment for improving wear resistance and impact toughness of the steel. It suggests that more precipitation of fine carbides dispersively distributed in the matrix is responsible for the beneficial effect of cryogenic treatment on wear resistance and impact toughness of the steel