Experimental Study on the Electrochemical Performance of PEMFC under Different Assembly Forces

Proton exchange membrane fuel cell(PEMFC)is of paramount significance to the development of clean energy.The components of PEMFC are assembled using many pairs of nuts and bolts.The assembly champing bolt torque is critical to the electrochemical performance and mechanical stability of PEMFC.In this paper,a PEMFC with the threechannel serpentine flow field was used and studied.The different assembly clamping bolt torques were applied to the PEMFC in three uniform assembly bolt torque and six non-uniform assembly bolt torque conditions,respectively.And then,the electrochemical performance experiments were performed to study the effect of the assembly bolt torque on the electrochemical performance.The test results show that the assembly bolt torque significantly affected the electrochemical performance of the PEMFC.In uniform assembly bolt torque conditions,the maximal power density increased initially as the assembly bolt torque increased,and then decreased on further increasing the assembly torque.It existed the optimum assembly torque which was found to be 3.0 N·m in this work.In non-uniform assembly clamping bolt torque conditions,the optimum electrochemical performance appeared in the condition where the assembly torque of each bolt was closer to be 3.0 N·m.This could be due to the change of the contact resistance between the gas diffusion layer and bipolar plate and mass transport resistance for the hydrogen and oxygen towards the catalyst layers.This work could optimize the assembly force conditions and provide useful information for the practical PEMFC stack assembly.

Risk prevention and control strategies for the severely affected areas of snow disaster in the Three Rivers Source Region(TRSR), China

Historically,frequent and heavy snow disaster(SD)has caused serious livestock death and casualties,resulting in a devastating impact on animal husbandry development in the Three Rivers Source Region(TRSR).From winter in 2018 to spring in 2019,the largest SD occurred in this area over the past 10 years,especially in core zones of the Lancang River Source Region.Field research results show that the main causes of the major SD include weak infrastructure(i.e.,roads,communications,warm sheds,and insufficient forage reserve),low rate of domestic animals for sale before the SD,and low loss settlement rate.SD occurrence could furtherly reduce the ability of disaster prevention,mitigation and relief of disaster loss.In the future,heavily affected SD areas should improve the forecasting ability of snowfall incidents,strengthen infrastructure construction,implement grass and livestock balance strategies,optimize livestock structure,improve loss settlement rate,and develop a modern compound model of animal husbandry development model that combines breeding,slaughtering and deep processing of animal product.

Relationship between sand-dust weather and water dynamics of desert areas in the middle reaches of Heihe River

Sand-dust weather has become an international social-environmental issue of common concern, and constitutes a serious threat to human lives and economic development. In order to explore the responses of natural desert sand and dust to the dynamics of water in desertification, we extracted long-term monitoring data related to precipitation, soil water, groundwater, and sand-dust weather. These data originated from the test stations for desertification control in desert areas of the middle reaches of the Heihe River. We used an algorithm of characteristic parameters, correlations, and multiple regression analysis to establish a regression model for the duration of sand-dust weather. The response char-acteristics of the natural desert sand and dust and changes of the water inter-annual and annual variance were also examined. Our results showed： （1） From 2006 to 2014 the frequency, duration, and volatility trends of sand-dust weather obviously increased, but the change amplitudes of precipitation, soil water, and groundwater level grew smaller. （2） In the vegetative growth seasons from March to November, the annual variance rates of the soil moisture content in each of four studied layers of soil samples were similar, and the changes in the frequency and duration of sand-dust weather were similar. （3） Our new regression equation for the duration of sand-dust weather passed the R test, F test, and t test. By this regression model we could predict the duration of sand-dust weather with an accuracy of 42.9%. This study can thus provide technological support and reference data for water resource management and re-search regarding sand-dust weather mechanisms.

Measuring and evaluating SDG indicators with Big Earth Data

The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic,social,and environmental action.A comprehensive indicator system to aid in the systematic implementation and monitoring of progress toward the Sustainable Development Goals(SDGs)is unfortunately limited in many countries due to lack of data.The availability of a growing amount of multi-source data and rapid advancements in big data methods and infrastructure provide unique opportunities to mitigate these data shortages and develop innovative methodologies for comparatively monitoring SDGs.Big Earth Data,a special class of big data with spatial attributes,holds tremendous potential to facilitate science,technology,and innovation toward implementing SDGs around the world.Several programs and initiatives in China have invested in Big Earth Data infrastructure and capabilities,and have successfully carried out case studies to demonstrate their utility in sustainability science.This paper presents implementations of Big Earth Data in evaluating SDG indicators,including the development of new algorithms,indicator expansion(for SDG 11.4.1)and indicator extension(for SDG 11.3.1),introduction of a biodiversity risk index as a more effective analysis method for SDG 15.5.1,and several new high-quality data products,such as global net ecosystem productivity,high-resolution global mountain green cover index,and endangered species richness.These innovations are used to present a comprehensive analysis of SDGs 2,6,11,13,14,and 15 from 2010 to 2020 in China utilizing Big Earth Data,concluding that all six SDGs are on schedule to be achieved by 2030.