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The area and volume of Chinese fir plantation is the largest in China. Based on a long-time biomass production observation on the Chinese fir plantation comprehensive observation field by Huitong National Forest Ecosystem Research Station, Hunan (Huitong Station). The dataset integrated annual biomass production of the Chinese fir plantation in Huitong Station in the period of 2007–2020, which both comprised the dry weight of trunk, branches, leaves, fruits (flowers), bark and aerial roots. The establishment and sharing of this dataset mainly provides data support for the biomass production research of Chinese fir plantation under the background of global change. It is of great significance to deeply understand the structural and functional characteristics of Chinese fir plantation ecosystem and formulate reasonable management measures of Chinese fir plantation. The area and volume of Chinese fir plantation is the largest in China. Based on a long-time biomass production observation on the Chinese fir plantation comprehensive observation field by Huitong National Forest Ecosystem Research Station, Hunan (Huitong Station). The dataset integrated annual biomass production of the Chinese fir plantation in Huitong Station in the period of 2007–2020, which both comprised the dry weight of trunk, branches, leaves, fruits (flowers), bark and aerial roots. The establishment and sharing of this dataset mainly provides data support for the biomass production research of Chinese fir plantation under the background of global change. It is of great significance to deeply understand the structural and functional characteristics of Chinese fir plantation ecosystem and formulate reasonable management measures of Chinese fir plantation.

This dataset is the experimental data of the draft "Rainbow trapping of flexural waves and its application in energy harvesting" (Draft No. lxxb2022-107) accepted by Chinese Journal of Theoretical and Applied Mechanics in 2022. It includes wave field experimental data of the rainbow trapping of flexural waves and experimental data of flexural wave energy harvesting. The experimental instruments used in the acquisition and processing of this dataset include a laser Doppler vibrometer system PSV400 and a power amplifier HVPA05, and the software used includes Matlab, Origin, and WPS. The ownership of the dataset belongs to the Institute of Structural Dynamics and Control, School of Aeronautics, Northwestern Polytechnical University. This dataset is the experimental data of the draft "Rainbow trapping of flexural waves and its application in energy harvesting" (Draft No. lxxb2022-107) accepted by Chinese Journal of Theoretical and Applied Mechanics in 2022. It includes wave field experimental data of the rainbow trapping of flexural waves and experimental data of flexural wave energy harvesting. The experimental instruments used in the acquisition and processing of this dataset include a laser Doppler vibrometer system PSV400 and a power amplifier HVPA05, and the software used includes Matlab, Origin, and WPS. The ownership of the dataset belongs to the Institute of Structural Dynamics and Control, School of Aeronautics, Northwestern Polytechnical University.

NDVI-based grassland biomass model has been widely used for evaluating the growth and productivity of grassland communities, since remote sensing technology is able to monitor wide area with strong timeliness. Based on regression relation between grassland biomass and NDVI from the literature during 2000 – 2018, we built the dataset of regression relation between grassland biomass and NDVI in China. The dataset contains 12 types of grassland regression relationships between biomass and NDVI, each relationship type contains 4 kinds of regression expression, i.e., unitary linear, power, exponential and logarithmic relationships. Besides, grassland regionalization, distribution area, suitable period, NDVI data resources, NDVI temporal resolution and NDVI spatial resolution were compiled. This dataset provides important data resources for evaluating Chinese grassland productivity, grassland ecosystem carrying capacity, carbon cycle, and ecological protection. NDVI-based grassland biomass model has been widely used for evaluating the growth and productivity of grassland communities, since remote sensing technology is able to monitor wide area with strong timeliness. Based on regression relation between grassland biomass and NDVI from the literature during 2000 – 2018, we built the dataset of regression relation between grassland biomass and NDVI in China. The dataset contains 12 types of grassland regression relationships between biomass and NDVI, each relationship type contains 4 kinds of regression expression, i.e., unitary linear, power, exponential and logarithmic relationships. Besides, grassland regionalization, distribution area, suitable period, NDVI data resources, NDVI temporal resolution and NDVI spatial resolution were compiled. This dataset provides important data resources for evaluating Chinese grassland productivity, grassland ecosystem carrying capacity, carbon cycle, and ecological protection.

Despite the recent interest in low-temperature carbonization of coal sources to prepare disordered carbon materials for the anode of lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs), the carbonization mechanism is still poorly understood. Herein, we select bituminous coal as the raw material and investigate the chemical, microcrystalline, and pore structure evolution from coal to the resulting disordered carbon during the carbonization process. These structural evolutions with temperature show an increasing interlayer spacing (3.69–3.82 Å) and defect concentration (1.26–1.90), as well as the generation of abundant nano-microporous structures. These changes are attributed to the migration of the carbon layer and the release of small molecules. Furthermore, a decrease in interlayer spacing and defect concentration occurs between 1000 °C and 1600 °C. In LIBs, samples carbonized at 1000 °C (CC-1000) showed the best electrochemical performance, with a reversible capacity of 384 mAh g–1 at 0.1C and excellent rate performance, maintaining 170 mAh g–1 at 5C. In SIBs, samples carbonized at 1200 °C (CC-1200) had a reversible capacity of 270.1 mAh g–1 at 0.1C and high initial Coulombic efficiency of 86.8%. These results guide the structural regulation of disordered carbon materials derived from coal for targeted applications. Despite the recent interest in low-temperature carbonization of coal sources to prepare disordered carbon materials for the anode of lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs), the carbonization mechanism is still poorly understood. Herein, we select bituminous coal as the raw material and investigate the chemical, microcrystalline, and pore structure evolution from coal to the resulting disordered carbon during the carbonization process. These structural evolutions with temperature show an increasing interlayer spacing (3.69–3.82 Å) and defect concentration (1.26–1.90), as well as the generation of abundant nano-microporous structures. These changes are attributed to the migration of the carbon layer and the release of small molecules. Furthermore, a decrease in interlayer spacing and defect concentration occurs between 1000 °C and 1600 °C. In LIBs, samples carbonized at 1000 °C (CC-1000) showed the best electrochemical performance, with a reversible capacity of 384 mAh g–1 at 0.1C and excellent rate performance, maintaining 170 mAh g–1 at 5C. In SIBs, samples carbonized at 1200 °C (CC-1200) had a reversible capacity of 270.1 mAh g–1 at 0.1C and high initial Coulombic efficiency of 86.8%. These results guide the structural regulation of disordered carbon materials derived from coal for targeted applications.

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.

This dataset is composed of two parts of data stored in two respective Excel files. They are: 1. Data on Main crops AG-biomass and leaf area index (2006-2015); 2. Data on Main crops Root biomass in cultivated layer of soil (2006-2015). This dataset is composed of two parts of data stored in two respective Excel files. They are: 1. Data on Main crops AG-biomass and leaf area index (2006-2015); 2. Data on Main crops Root biomass in cultivated layer of soil (2006-2015).

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.

With waste walnut shell as raw material, biomass based porous active carbon was made by microwave oven method. The effects of microwave power, activation time and mass fraction of phosphoric acid on adsorptive property of biomass based porous active carbon in the process of physical activation of active carbon precursor were studied by response surface method and numerical simulation method, the preparation plan of biomass based porous active carbon was optimized, and the optimal biomass based porous active carbon property was characterized. The results show that three factors affect the adsorptive property of biomass based porous active carbon, but the effect of microwave power is obviously more significant than that of mass fraction of phosphoric acid, and the effect of mass fraction of phosphoric acid is more significant than that of activation time. The optimized preparation conditions are:microwave power is 746W, activation time is 11.2min and mass fraction of phosphoric acid is 85.9% in the process of physical activation of activated carbon precursor by microwave heating method. For the optimal biomass based porous active carbon, the adsorption value of iodine is 1074.57mg/g, adsorption value of methylene blue is 294.4mL/g and gain rate is 52.1%.

The structure and operation mode of electric vehicle charging station of optical storage are introduced. Following this, the control strategy of electric vehicle charging station is proposed, which determines the operation mode of charging station depending on maximum power output of PV system and state of charge of battery, so that the coordinated control of PV power generation, charging and discharging of energy storage system, charging and discharging station requirements and grid connection is realized. For the bi-directional DC-DC converter of energy storage end, the voltage and current double closed-loop control method is adopted, and the bus voltage is layered to avoid the battery charging and discharging frequently. For the DC-AC grid side converter, voltage outer loop and inductance current inner double loop control are utilized. The experimental results show that the control strategy should enable electric vehicle charging station to switch between various operation modes effectively and maintain the DC bus voltage balance of system, and verify the effectiveness of the control strategy.

Concentrating solar power is the ideal way to solve the conflicts between energy and environment. Heat transfer and heat storage are the key links in solar thermal power generation, while molten salt is an excellent heat transfer and heat storage medium. Most of the solar thermal power stations operating at home and abroad use binary nitrates (solar salt) and ternary nitrates (Hitec). However, their low heat transfer and heat storage performance will affect the efficiency of solar energy utilization. The unique spatial structure of nanomaterials enables it to have excellent thermal conductivity and good stability. Introducing nanomaterials as additives into the nitrate molten salt system is expected to improve the thermal properties of the material such as heat transfer and heat storage, thereby improving the efficiency of solar thermal utilization and reducing the cost of power generation. In this paper, the related studies of nano metal particles, nano metal oxides, carbon nanomaterials, and other inorganic nanomaterials as doping additives in nitrate molten salt systems were reviewed. The changes in the thermal properties of molten salts after modification were discussed and the mechanism of action was explored, which can provide references for preparation of energy storage molten salt with excellent thermal properties. In the future research, the measurement of thermophysical properties, mechanism of heat transfer, quantitative structure-activity relationship and industrial pilot will be focused on, so that nitrate molten salt with excellent heat transfer and heat storage performance can be applied in the field of solar thermal power generation, which will play a more important role in the development and utilization of clean energy.