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Supercapacitors have become a new type of energy storage device widely used due to their high efficiency, fast speed and good cycle stability, and electrode material is a key issue restricting their development. The porous niobium nitride fibers were prepared by electrospinning combined with reduction nitride technology using pentachloride as raw material, and Nb4N5||Nb4N5 symmetrical button capacitance was prepared. In order to improve the electrochemical performance of electrode material, NaHCO3 was added into Na2SO4 electrolyte. The results show that the prepared niobium nitride fiber exhibits a tetragonal phase, continuous and porous on the surface. The porous niobium nitride electrode has two mechanisms of electric double layer and pseudocapacitive energy storage. The specific capacitance of the capacitor increases to 187 F·g-1 with the NaHCO3 addition of 15 mmol·dm-3. The buffering effect inhibits the dissolution of niobium nitride, effectively reduces the solution resistance, the solution impedance R1 and the diffusion impedance WR reduce to 1.22 Ω and 1.47 Ω respectively and the ionic conductivity improves. The carrier concentration increases to 6.58×1024 cm3, and the relaxation time of the capacitor is shortened to 0.24 s at the same time.

In order to solve the problem of power supply of wireless monitoring node in coal mine working face， a thermoelectric energy harvesting device for the surface temperature difference of downhole equipment is designed based on the thermal electric generator(TEG) and boost management module in this paper. The device is used to charge the lithium ion battery. Experimental results show that the circuit can continue energy harvesting from input sources as low as 100 mV. The stable output power of TEG can be increased by several tens of times using air cooling or water cooling. The bq25505 device is specifically designed to efficiently extract the power generated from the TEG. It can significantly increase the discharge working time of the lithium battery, and the energy conversion efficiency of the module is up to 67%. Therefore, the device can effectively improve the working cycle of the downhole wireless node.

Ethanol is a feasible medium for diffusion separation of carbon isotopes. In order to obtain the separation coefficient of gas diffusion separation of ethanol, the application of elemental analysis isotope ratio mass spectrometry (EA-IRMS) to measure the carbon isotope abundance in ethanol in gas diffusion separation experiment was studied. Through literature investigation, this study optimized the preparation process of ethanol samples, developed the method of EA-IRMS for the determination of carbon isotope abundance of ethanol, tested the stability, and realized the measurement of carbon isotope abundance of ethanol samples. Based on the separation experiment of gas diffusion method, the carbon isotope abundances of refined ethanol and lean ethanol in multiple separation experiments are obtained. The basic total separation coefficient of carbon isotopes by ethanol diffusion can be calculated by formula derivation. This study provides an analytical basis for the future experiment of diffusion separation of carbon isotopes with ethanol as medium.

When the wind turbine blades are running under normal conditions, the blades will vibrate due to periodic aerodynamic forces, and the use time of blades will be reduced. In order to study the vibration characteristics of wind turbine blades, this paper selects 5 operating conditions (wind speed range from 15 m/s to 40 m/s) under different wind speed conditions. The CFD method is used to simulate the NREL PHASE VI blade, and the vibration and vibration displacement curves under different wind speeds are obtained. The results show that: 1) the main vibration modes of the blade are waving and shimmy, and the high order blade vibration modes have complex deformation of bending and torsion. 2) When the inlet velocity increases from 15 m/s to 40 m/s, the uneven distribution of pressure distribution on suction surface is increasing, and the maximum pressure difference is about 3 000 Pa when the inlet velocity is 40 m/s. 3) when the flow velocity is 15 m/s, the minimum amplitude is 0.525 4 mm, and when the flow velocity is 40 m/s, the vibration reaches the maximum of 3.628 2 mm, which is about 6.9 times of the minimum amplitude. The vibration curves of the 5 operating conditions are all attenuated trend and the blades tend to stabilize vibration, and when the wind speed is larger, the aerodynamic force generated by the wind flow has a greater force on the blade, and the amplitude of the blade tends to increase. The results of this study can provide a reference for the design of wind turbines.

As the most complex component in the transmission system, the operating state of the wind turbine gearbox has a tremendous impact on the monitoring of the health status and operation control of the wind turbine equipment. Abnormalities in wind turbines that lead to downtime not only result in a loss of electrical energy, but also a significant increase in maintenance costs. Therefore, with the wind turbine gearbox as the main object of study, the following studies were carried out: For microscopic local conditions in gearbox gear systems, a method for obtaining modal data using finite element simulation analysis of single tooth faults is proposed. Using a combination of deep auto-encoder structures and BP structures for secondary training strategies, a linear and non-linear performance evaluation method is proposed, which takes into account the relationship between performance and efficiency. Hyper-parameter configuration in deep transfer structures is often arbitrary, so a hierarchical transfer network structure hyper-parameter searching method is proposed to address the gearbox planetary system fault classification problem. The algorithm is validated using the classical LeNet-5 reconfiguration transfer application on a modal dataset of the planetary system. Finally, a stability validation and results analysis of the algorithm performance is carried out. A compressed sensing-based sparse signal decomposition method is proposed, and the structure of the transfer network is redesigned to achieve deep migration learning from rolling bearing faults to gear faults. A new network architecture was designed using a plug-and-play attention module. Pre-training models were designed and produced for fault data to improve the accuracy and recognition speed of fault diagnosis model classification. Finally, the effects of the same number of samples in the source and target domains and different distributions of sample features on the performance of the transfer learning method and the effects of hyper-parameters on the final performance of the network structure are verified.

As a new type of two-dimensional nanomaterials, MXene has been widely investigated since its discovery at 2011 due to its excellent physical and chemical properties, such as high conductivity, good lubricity, electromagnetism and other special properties. Hence, in addition to the performance of the traditional two-dimensional materials, MXene has been extensively used in the fields of energy storage, catalysis, lubrication, electromagnetic shielding, sensor, water purification and so on,and certain results and progress were achieved. The latest researches of MXene at structure, property and preparation methods, as well as the related achievements in lithium ion battery, supercapacitor and others at our country and overseas in recent years were reviewed in this paper. Moreover, the shortcomings of current research were summarized, and the future research direction were prospected as well.

A kind of wireless power transmission device is designed to include a total power supply and a plurality of radio energy transmission nodes by means of magnetic coupling resonant radio power transmission technology for the power supply characteristics and requirements of the underground displacement measurement sensor string. Each node includes a coil loop, a micro control unit, a receiver circuit, a transmitter circuit, a switch switching circuit and a wireless communication module. The micro control unit controls the connection of the switch switching coil and the back end circuit according to the instruction received by the communication module. This allows each node to have three operating states, namely, receive status, send status, and relay status. In the experiment, the coil was wound and the transmission characteristics were measured with a network analyzer. Then, the coil is connected with the node circuit to verify the performance of the wireless power transmission device. The result shows that the device is stable and reliable to meet the power supply requirement of the sensor string.

Solar radiation controls biological, chemical and other processes in the atmosphere, hydrosphere, biosphere and lithosphere. Solar radiation is energy source for forest ecosystem, power to maintain and develop the ecosystem, and has important effects on plant photosynthesis, transpiration and carbon exchange. Gongga Mountain is located in southeast edge of Tibetan Plateau, and is a typical and representative alpine ecosystem. According to the protocols for standard radiation observation and measurement of Chinese Ecosystem Research Network (CERN), the Alpine Ecosystem Observation and Experiment Station of Gongga Mountain, Chinese Academic of Sciences has been carrying out long-term radiation monitoring. In this dataset, we report 22 radiation indicators (total 97 KB) collected from the automatic radiation system after data processing and quality control and assessment during 1998–2018. It provides basic data for carbon cycle, water cycle and energy cycle of alpine ecosystem under global change. Solar radiation controls biological, chemical and other processes in the atmosphere, hydrosphere, biosphere and lithosphere. Solar radiation is energy source for forest ecosystem, power to maintain and develop the ecosystem, and has important effects on plant photosynthesis, transpiration and carbon exchange. Gongga Mountain is located in southeast edge of Tibetan Plateau, and is a typical and representative alpine ecosystem. According to the protocols for standard radiation observation and measurement of Chinese Ecosystem Research Network (CERN), the Alpine Ecosystem Observation and Experiment Station of Gongga Mountain, Chinese Academic of Sciences has been carrying out long-term radiation monitoring. In this dataset, we report 22 radiation indicators (total 97 KB) collected from the automatic radiation system after data processing and quality control and assessment during 1998–2018. It provides basic data for carbon cycle, water cycle and energy cycle of alpine ecosystem under global change.

As a kind of important renewable resources, grassland resources have significant influence on human’s daily life. China is a country with abundant grassland resources. The scientific use of grassland resources would contribute to the sustainable development of animal husbandry, national unity and the stability of the country. However, grassland resources are facing with more and more problems, with the development of agriculture, industry, animal husbandry, population growth, and the impact of global warming. Therefore, obtaining accurate real-time information of the growth condition of grassland is quite important. People can use this information carrying on the scientific management of grassland resources, thus protecting grassland resources and keeping the sustainable development of animal husbandry. Traditional observation method is mainly ground experiment, which would cost lots of time and money. Remote sensing data has the advantage of near-real time, dynamic observation and contains image with large scale. But a single type of remote sensing data cannot meet the needs of high temporal-spatial grassland biomass observation. This study intends to use data fusion method to generate high temporal-spatial remote sensing data. Then combining with ground survey data , we established the parametric and non-parametric model. Eventually we developed the optimal aboveground biomass model for Qinghai Lake Basin and generate the biomass time series with 30 meter resolution and 8 day interval during 2000—2015. We then analyzed the grassland trend in Qinghai Lake basin during the past 16 years. The main work and the conclusions of our findings are as follows: (1) According to the actual situation of Qinghai Lake Basin, we developed the optimal fusion model from three prospects: the selection of generating synthetic NDVI, the comparison between different image (different MODIS product and TM image in different years), and the development of data fusion algorithm. We finally generated the synthetic NDVI series of Qinghai Lake Basin. The selection of the fusion scheme would directly affect the precision of the vegetation index, and then impact the accuracies of the construction of the biomass model. Based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, we used MCD43A4 as the input MODIS file. We then chose data in the same year, in adjacent year, and data with 2-yr intervals. Based on the landcover type, we used decision tree to choose different windows for different vegetation types: 350m for croplands; 950m for forest; 750m for grassland and other vegetation types. We have synthetic NDVI time series with relatively high spatial and temporal resolution. It can tell more spatial details on the vegetation variation compared with MODIS data. (2) Based on the measured data and the fusion vegetation index data, the parametric models and a non-parametric model were established and compared. Finally, the experimental results show that the support vector machine (SVM) model has good accuracy. Based on this model, the data set of 30-m data series of grassland aboveground biomass in Qinghai Lake area in the past 16 years was established. We built the model in the following four steps: We generated the synthetic NDVI series with the optimal data fusion scheme; combined with the 291 field samples and vegetation index data, we generated the biomass estimation model of Qinghai Lake region; We chose the optimal model for biomass estimation according to the test data. We finally generated the biomass series with 320 scenes. Biomass estimation model with synthetic NDVI (r=0.85, RMSE=74.45g/m2) can not only maintain accuracies of the models based on MODIS NDVI (r=0.85, RMSE=73.20g/m2); it can also increase the spatial resolution of the biomass from 500m to 30m, and increase the time resolution up to 8 days. (3) The degradation condition of grassland in in Qinghai Lake area was analyzed. We found that during the past 16 years, grassland resources in this area have changed greatly. Grassland in the south lakeshore and the mountainous area in the northern part of the basin showed large degradation, while in the middle of the Qinghai Lake Basin, grassland showed growing tendencies. Grassland with apparent degradation accounted for 8.5% of the basin,while grassland with apparent growth account for 24.5% of the basin. The degradation of grassland were partly contributed by global warming; while the unscientific use of grassland resources is another critical issue caused the land degradation. In addition, as a tourist hot spot, In recent years, tourists number in Qinghai Lake Basin increased dramatically, which would also contribute to the grassland degradation in the local area. As a kind of important renewable resources, grassland resources have significant influence on human’s daily life. China is a country with abundant grassland resources. The scientific use of grassland resources would contribute to the sustainable development of animal husbandry, national unity and the stability of the country. However, grassland resources are facing with more and more problems, with the development of agriculture, industry, animal husbandry, population growth, and the impact of global warming. Therefore, obtaining accurate real-time information of the growth condition of grassland is quite important. People can use this information carrying on the scientific management of grassland resources, thus protecting grassland resources and keeping the sustainable development of animal husbandry. Traditional observation method is mainly ground experiment, which would cost lots of time and money. Remote sensing data has the advantage of near-real time, dynamic observation and contains image with large scale. But a single type of remote sensing data cannot meet the needs of high temporal-spatial grassland biomass observation. This study intends to use data fusion method to generate high temporal-spatial remote sensing data. Then combining with ground survey data , we established the parametric and non-parametric model. Eventually we developed the optimal aboveground biomass model for Qinghai Lake Basin and generate the biomass time series with 30 meter resolution and 8 day interval during 2000—2015. We then analyzed the grassland trend in Qinghai Lake basin during the past 16 years. The main work and the conclusions of our findings are as follows: (1) According to the actual situation of Qinghai Lake Basin, we developed the optimal fusion model from three prospects: the selection of generating synthetic NDVI, the comparison between different image (different MODIS product and TM image in different years), and the development of data fusion algorithm. We finally generated the synthetic NDVI series of Qinghai Lake Basin. The selection of the fusion scheme would directly affect the precision of the vegetation index, and then impact the accuracies of the construction of the biomass model. Based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm, we used MCD43A4 as the input MODIS file. We then chose data in the same year, in adjacent year, and data with 2-yr intervals. Based on the landcover type, we used decision tree to choose different windows for different vegetation types: 350m for croplands; 950m for forest; 750m for grassland and other vegetation types. We have synthetic NDVI time series with relatively high spatial and temporal resolution. It can tell more spatial details on the vegetation variation compared with MODIS data. (2) Based on the measured data and the fusion vegetation index data, the parametric models and a non-parametric model were established and compared. Finally, the experimental results show that the support vector machine (SVM) model has good accuracy. Based on this model, the data set of 30-m data series of grassland aboveground biomass in Qinghai Lake area in the past 16 years was established. We built the model in the following four steps: We generated the synthetic NDVI series with the optimal data fusion scheme; combined with the 291 field samples and vegetation index data, we generated the biomass estimation model of Qinghai Lake region; We chose the optimal model for biomass estimation according to the test data. We finally generated the biomass series with 320 scenes. Biomass estimation model with synthetic NDVI (r=0.85, RMSE=74.45g/m2) can not only maintain accuracies of the models based on MODIS NDVI (r=0.85, RMSE=73.20g/m2); it can also increase the spatial resolution of the biomass from 500m to 30m, and increase the time resolution up to 8 days. (3) The degradation condition of grassland in in Qinghai Lake area was analyzed. We found that during the past 16 years, grassland resources in this area have changed greatly. Grassland in the south lakeshore and the mountainous area in the northern part of the basin showed large degradation, while in the middle of the Qinghai Lake Basin, grassland showed growing tendencies. Grassland with apparent degradation accounted for 8.5% of the basin,while grassland with apparent growth account for 24.5% of the basin. The degradation of grassland were partly contributed by global warming; while the unscientific use of grassland resources is another critical issue caused the land degradation. In addition, as a tourist hot spot, In recent years, tourists number in Qinghai Lake Basin increased dramatically, which would also contribute to the grassland degradation in the local area.