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A series of silver-modified phosphotungstic acid catalysts AgxH3-xPW12O40 (x=1, 2, 3) were synthesized by incorporating silver nitrate into phosphotungstic acid. The structure, stability and acidity of the catalysts synthesized were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermal gravimetric analysis-derivative thermogravimetric analysis (TGA-DTG) and 31P-TMPO magic-angle spinning nuclear magnetic resonance (31P-TMPO MAS-NMR) spectroscopy. The effects of various reaction parameters, such as methanol/oil molar ratio, amount of catalyst, reaction time, and reaction temperature on the catalytic transesterification of soybean oil and methanol with Ag2HPW12O40 were investigated. The results demonstrated that Ag2HPW12O40 had the best catalytic activity, superior biodiesel yield and excellent durability. The high catalytic activity of the catalyst was attributed to Brønsted-Lewis acid synergy. With 6 wt.% Ag2HPW12O40 catalyst, the yield of biodiesel reached 96.4% with a methanol/soybean oil molar ratio of 32/1, a reaction temperature of 150℃ and a reaction time of 20 h.

Self-healing polymer materials are able to self-repair damage and recover themselves after cracks generating to maintain their structural and functional integrity. According to whether additional repair agent is added, self-healing polymers are mainly divided into two categories, namely extrinsic- and intrinsic-based polymers.The key materials of electrochemical energy storage devices will experience irreversible mechanical damage in extreme condition applications, for example, the energy storage device more prone to physical damage inwearable devices during the multiple bending and deformation processes. These problems severely reduce the stability of energy storage and delivery, and shorten the life of the devices. Therefore, the application of self-healing polymers in electrochemical energy storage devices to improve the stability and life of devices has become one of the research hotspots in recent years. Herein,this article summarizes the repair mechanism of self-healing polymer materials (capsule-based, vascular-based, and intrinsic polymers), with main focus on intrinsic self-healing polymer and its research progress in the field of electrochemical energy storage, which based on molecular interactions to achieve multi-time reversible healing without any additional repair agent.The self-healing electrode and electrolyte system were reviewed respectively, and then the self-healing mechanism and its influence on the electrochemical performance of energy storage devices were described. The research progress of self-healing functional polymer as high specific energy electrode binder, interface modification layer and self-healing electrolyte were summarized in detail. Finally, the future perspectives regarding the future development of self-healing polymer materials were also discussed.

This article presents the data of the published paper: High resolution vibronic state-specific dissociation of NO2+ in the 10.0–15.5 eV energy range by synchrotron double imaging photoelectron photoion coincidence (Phys. Chem. Chem. Phys., 2020, 22, 1974) This article presents the data of the published paper: High resolution vibronic state-specific dissociation of NO2+ in the 10.0–15.5 eV energy range by synchrotron double imaging photoelectron photoion coincidence (Phys. Chem. Chem. Phys., 2020, 22, 1974)

This dataset provides 30-year averaged climate data for both historical and future periods, with a spatial resolution of 0.01° × 0.01°. Historical data (1991–2020) are based on the China Surface Climate Standard Dataset and were interpolated using ANUSPLIN software. Future climate data are derived from CMIP6 simulations, bias-corrected using the Delta downscaling method. The dataset includes 10 models (9 Global Climate Models, namely, GCMs, and 1 ensemble model), 3 scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), and 3 future periods (2021–2040, 2041–2070, 2071–2100). For each period (or scenario), 28 climate variables are provided, including: 5 monthly basic climate variables (mean temperature, maximum temperature, minimum temperature, precipitation, and percentage of sunshine), and 23 bioclimatic variables based on the basic variables (for details, see the dataset documentation file).The data quality was strictly evaluated. The ANUSPLIN interpolated historical data showed a strong correlation with observations (all correlation coefficients above 0.91). The historical interpolations generated by the ANUSPLIIN software showed a good fit (above 0.91) with observations. The bias correction improved the accuracy of most GCM original simulations, reducing the bias by 0.69%–58.63%. This dataset aims to provide high-resolution, bias-corrected long-term historical and future climate data for climate and ecological research. All computations were performed using R, and the corresponding code can be found in the dataset folder: “Code”.All data are provided in GeoTIFF (.tif) format, where each file for the basic climate variables contains 12 bands, representing monthly data in ascending order (e.g., Band 1 corresponds to January). To facilitate data storage, all files are provided in compressed archives, following a consistent naming convention:(1) Historical data: China_Variable_1km_1991–2020.tifWhere, Variable represents the abbreviation of the 28 climate variables.Example: China_pr_1km_1991–2020.tif.(2) Future data: China_Variable_Model_VariantLabel_1km_StartYear-EndYear_Scenario.tifWhere, Variable is the 28 climate variables; Model is the GCM name; VariantLabel is r1i1p1f1 in this study; StartYear-EndYear is the future period; Scenario is the SSP climate scenarioExample: China_tasmin_MRI-ESM2-0_r1i1p1f1_1km_2071–2100_SSP585.tif. This dataset provides 30-year averaged climate data for both historical and future periods, with a spatial resolution of 0.01° × 0.01°. Historical data (1991–2020) are based on the China Surface Climate Standard Dataset and were interpolated using ANUSPLIN software. Future climate data are derived from CMIP6 simulations, bias-corrected using the Delta downscaling method. The dataset includes 10 models (9 Global Climate Models, namely, GCMs, and 1 ensemble model), 3 scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), and 3 future periods (2021–2040, 2041–2070, 2071–2100). For each period (or scenario), 28 climate variables are provided, including: 5 monthly basic climate variables (mean temperature, maximum temperature, minimum temperature, precipitation, and percentage of sunshine), and 23 bioclimatic variables based on the basic variables (for details, see the dataset documentation file).The data quality was strictly evaluated. The ANUSPLIN interpolated historical data showed a strong correlation with observations (all correlation coefficients above 0.91). The historical interpolations generated by the ANUSPLIIN software showed a good fit (above 0.91) with observations. The bias correction improved the accuracy of most GCM original simulations, reducing the bias by 0.69%–58.63%. This dataset aims to provide high-resolution, bias-corrected long-term historical and future climate data for climate and ecological research. All computations were performed using R, and the corresponding code can be found in the dataset folder: “Code”.All data are provided in GeoTIFF (.tif) format, where each file for the basic climate variables contains 12 bands, representing monthly data in ascending order (e.g., Band 1 corresponds to January). To facilitate data storage, all files are provided in compressed archives, following a consistent naming convention:(1) Historical data: China_Variable_1km_1991–2020.tifWhere, Variable represents the abbreviation of the 28 climate variables.Example: China_pr_1km_1991–2020.tif.(2) Future data: China_Variable_Model_VariantLabel_1km_StartYear-EndYear_Scenario.tifWhere, Variable is the 28 climate variables; Model is the GCM name; VariantLabel is r1i1p1f1 in this study; StartYear-EndYear is the future period; Scenario is the SSP climate scenarioExample: China_tasmin_MRI-ESM2-0_r1i1p1f1_1km_2071–2100_SSP585.tif.

Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important ecological security barrier in the north of China, and a key area for resource development, environmental protection and ecological security in China. Studying its vegetation phenological changes can know its vegetation growth status, which is of great significance for understanding the characteristics of climate change and extreme climate events in the region. Based on the normalized differential vegetation index (NDVI) data product in MOD13Q1 product, this study use Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exports NDVI long time series data from 2000 to 2021, and dynamic threshold method was used to obtain Inner Mongolia vegetation phenology data set from 2001 to 2020. The dataset includes remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. And the spatial resolution is 250 m. It provides data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change.  Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important ecological security barrier in the north of China, and a key area for resource development, environmental protection and ecological security in China. Studying its vegetation phenological changes can know its vegetation growth status, which is of great significance for understanding the characteristics of climate change and extreme climate events in the region. Based on the normalized differential vegetation index (NDVI) data product in MOD13Q1 product, this study use Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exports NDVI long time series data from 2000 to 2021, and dynamic threshold method was used to obtain Inner Mongolia vegetation phenology data set from 2001 to 2020. The dataset includes remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. And the spatial resolution is 250 m. It provides data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change. 

This article presents the data of the published paper: Threshold photoelectron spectroscopy of the methoxy radical (J. Chem. Phys. 153, 031101, 2020). This article presents the data of the published paper: Threshold photoelectron spectroscopy of the methoxy radical (J. Chem. Phys. 153, 031101, 2020).

primary energy and alternative energy patent data primary energy and alternative energy patent data

The Great Wall Station and Zhongshan Station are Chinese permanent scientific research stations in Antarctica, located on the King George Island and in the Larsemann Hills. These two stations are representative observation stations for the study of climate change in the coastal areas of the West Antarctica and the East Antarctica respectively. After the completion of the two stations, surface meteorological observation is carried out according to the specifications of the China Meteorological Administration. This dataset collected the surface meteorological observation data of the Great Wall Station and Zhongshan Station from 1985 to 2022. The data has been quality controlled according to the "Specifications for surface meteorological observation". Nowadays long-term continuous surface meteorological observation data has been obtained including surface air temperature, relative humidity, air pressure, wind speed, wind direction and cloud amount. This dataset can be used for the research of weather processes, climate change and numerical weather forecast in the Antarctic. The Great Wall Station and Zhongshan Station are Chinese permanent scientific research stations in Antarctica, located on the King George Island and in the Larsemann Hills. These two stations are representative observation stations for the study of climate change in the coastal areas of the West Antarctica and the East Antarctica respectively. After the completion of the two stations, surface meteorological observation is carried out according to the specifications of the China Meteorological Administration. This dataset collected the surface meteorological observation data of the Great Wall Station and Zhongshan Station from 1985 to 2022. The data has been quality controlled according to the "Specifications for surface meteorological observation". Nowadays long-term continuous surface meteorological observation data has been obtained including surface air temperature, relative humidity, air pressure, wind speed, wind direction and cloud amount. This dataset can be used for the research of weather processes, climate change and numerical weather forecast in the Antarctic.

Janzen-Connell (JC) effects, hypothesized to be mostly driven by negative plant-soil feedbacks (PSFs), are considered to be the key mechanism that regulates tropical forest plant diversity and coexistence. However, intraspecific variation in JC effects may weaken this mechanism, with the strength of PSFs being a potentially key variable process. We conducted a manipulated experiment with seedlings from two populations of Pometia pinnata (Sapindaceae), a tropical tree species in southwest China. We aimed to measure the intraspecific difference in PSF magnitude caused by inoculating the soil from different P. pinnata source populations and growing seedlings under differing light intensity and water availability treatments, and at varying plant densities. We found negative PSFs for both populations with the inoculum soil originating from the same sites, but PSFs differed significantly with the inoculum soil from different sites. PSF strength responded differently to biotic and abiotic drivers; PSF strength was weaker in low moisture and high light treatments than in high moisture and low light treatments. Our study documents intraspecific variation in JC effects: specifically, P. pinnata have less defences to their natively-sourced soil, but are more defensive to the soil feedbacks from soil sourced from other populations. Our results imply that drought and light intensity tended to weaken JC effects, which may result in loss of species diversity with climate change. Janzen-Connell (JC) effects, hypothesized to be mostly driven by negative plant-soil feedbacks (PSFs), are considered to be the key mechanism that regulates tropical forest plant diversity and coexistence. However, intraspecific variation in JC effects may weaken this mechanism, with the strength of PSFs being a potentially key variable process. We conducted a manipulated experiment with seedlings from two populations of Pometia pinnata (Sapindaceae), a tropical tree species in southwest China. We aimed to measure the intraspecific difference in PSF magnitude caused by inoculating the soil from different P. pinnata source populations and growing seedlings under differing light intensity and water availability treatments, and at varying plant densities. We found negative PSFs for both populations with the inoculum soil originating from the same sites, but PSFs differed significantly with the inoculum soil from different sites. PSF strength responded differently to biotic and abiotic drivers; PSF strength was weaker in low moisture and high light treatments than in high moisture and low light treatments. Our study documents intraspecific variation in JC effects: specifically, P. pinnata have less defences to their natively-sourced soil, but are more defensive to the soil feedbacks from soil sourced from other populations. Our results imply that drought and light intensity tended to weaken JC effects, which may result in loss of species diversity with climate change.

Meteorological data is an foundational data for field scientific observation and research. Surface meteorological observation is an important component of meteorological observation. Due to its adherence to unified surface meteorological observation standards, it has better comparability in space, which is also its distinguishing feature from microclimate observation. Henan Xiaolangdi Forest Ecosystem National Observation and Research Station (referred to as Xiaolangdi Station) is located in the transitional zone of the second and third steps of China's landforms in the southern Taihang Mountains. It is a key ecological area of the Yellow River and belongs to a warm temperate sub humid monsoon climate with rich biodiversity. This dataset is based on the raw data collected from the surface meteorological observation field of Xiaolangdi Station. It is the daily meteorological data product from 2018 to 2020 after data processing and quality control. It covers the daily data of temperature, maximum temperature, minimum temperature, relative humidity, wind speed, maximum wind speed, net radiation, direct radiation, air pressure, precipitation, 0-80cm multi-layer soil temperature (total of 17 observation elements). This dataset can provide background information for climate change research and ecological civilization construction, and to provide data support for maintaining ecological security and promoting high-quality development in the Yellow River Basin. Meteorological data is an foundational data for field scientific observation and research. Surface meteorological observation is an important component of meteorological observation. Due to its adherence to unified surface meteorological observation standards, it has better comparability in space, which is also its distinguishing feature from microclimate observation. Henan Xiaolangdi Forest Ecosystem National Observation and Research Station (referred to as Xiaolangdi Station) is located in the transitional zone of the second and third steps of China's landforms in the southern Taihang Mountains. It is a key ecological area of the Yellow River and belongs to a warm temperate sub humid monsoon climate with rich biodiversity. This dataset is based on the raw data collected from the surface meteorological observation field of Xiaolangdi Station. It is the daily meteorological data product from 2018 to 2020 after data processing and quality control. It covers the daily data of temperature, maximum temperature, minimum temperature, relative humidity, wind speed, maximum wind speed, net radiation, direct radiation, air pressure, precipitation, 0-80cm multi-layer soil temperature (total of 17 observation elements). This dataset can provide background information for climate change research and ecological civilization construction, and to provide data support for maintaining ecological security and promoting high-quality development in the Yellow River Basin.