Simultaneous Formation of CH₃NH₃PbI₃ and electron transport layers using antisolvent method for efficient perovskite solar cells

A new antisolvent method was developed to prepare CH₃NH₃PbI₃ and electron transport layers for making efficient hybrid perovskite solar cells. By directly using [6,6]-phenyl-C61-butyric acid methyl ester in chlorobenzene solution as antisolvent, CH₃NH₃PbI₃ and electron transport layers were simultaneously formed in the films. This method not only simplifies the fabrication process of devices, but also produces uniform perovskite films and improves the interfacial structures between CH₃NH₃PbI₃ and electron transport layers. Large perovskite grains were observed in these films, with the average grain size of >1 μm. The so-formed CH₃NH₃PbI₃/electron transport layers demonstrated good optical and charge transport properties. And perovskite solar cells fabricated using these simultaneously-formed layers achieved a higher power conversion efficiency of 16.58% compared to conventional antisolvent method (14.92%). This method reduces nearly 80% usage of chlorobenzene during the fabrication, offering a more facile and environment-friendly approach to fabricate efficient perovskite solar cells than the conventional antisolvent method

Key technologies research on of soil-structure interaction base story isolated structure response in 3D seismic zone

The development of karst in Karst area leads to poor stability of stratum. If earthquake occurs, the area will produce destructive disaster. In order to improve the stability capacity of the grassroots in the region, this study investigates the seismic response of inter-story isolation structures considering soil-structure interaction (SSI) in three-dimensional earthquakes. A model of the inter-story isolation structure incorporating SSI was developed, and one-dimensional, two-dimensional, and three-dimensional ground motions were applied to compare the seismic response under different input conditions. A three-dimensional isolation system was introduced and compared with traditional horizontal isolation structures to address excessive tensile and compressive stresses on the isolation structure during three-dimensional ground motion. The results demonstrate that the seismic response to three-dimensional earthquakes surpasses one-dimensional and two-dimensional inputs. Furthermore, adding a three-dimensional isolation structure effectively isolates vertical ground motion and reduces structural seismic response. Moreover, it minimizes soil stresses on the foundation compared to traditional horizontal isolation structure, enhancing foundation stability. This study will provide theoretical value and practical guidance for the research on key technology of SSI base story isolation structure response in Karst Plateau 3D Seismic zone

New common fixed point theorems for maps on cone metric spaces

AbstractIn this paper, we study the uniqueness and existence of a common fixed point for a pair of mappings in cone metric space. The results extend and improve recent related results

Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design

Combining sample plot and image data has been widely used to map forest carbon density at local, regional, national and global scales. When mapping is conducted using multiple spatial resolution images at different scales, field observations have to be collected at the corresponding resolutions to match image values in pixel sizes. Given a study area, however, to save time and cost, field observations are often collected from sample plots having a fixed size. This will lead to inconsistency of spatial resolutions between sample plots and image pixels and impede the mapping and product quality assessment. In this study, a methodological framework was proposed to conduct mapping and accuracy assessment of forest carbon density at four spatial resolutions by combining remotely sensed data and reference values of sample plots from a systematical, nested and clustering sampling design. This design led to one field observation dataset at a 30 m spatial resolution sample plot level and three other reference datasets by averaging the observations from three, five and seven sample plots within each of 250 m and 500 m sub-blocks and 1000 m blocks, respectively. The datasets matched the pixel values of a Landsat 8 image and three MODIS products. A sequential Gaussian co-simulation (SGCS) and a sequential Gaussian block co-simulation (SGBCS), an upscaling algorithm, were employed to map forest carbon density at the spatial resolutions. This methodology was tested for mapping forest carbon density in Huang-Feng-Qiao forest farm of You County in Eastern Hunan of China. The results showed that: First, all of the means of predicted forest carbon density values at four spatial resolutions fell in the confidence intervals of the reference data at a significance level of 0.05. Second, the systematical, nested and clustering sampling design provided the potential to obtain spatial information of forest carbon density at multiple spatial resolutions. Third, the relative root mean square error (RMSE) of predicted values at the plot level was much greater than those at the sub-block and block levels. Moreover, the accuracies of the up-scaled estimates were much higher than those from previous studies. In addition, at the same spatial resolution, SGCSWA (scaling up the SGCS and Landsat derived 30 m resolution map using a window average (WA)) resulted in smallest relative RMSEs of up-scaled predictions, followed by combinations of Landsat images and SGBCS. The accuracies from both methods were significantly greater than those from the combinations of MODIS images and SGCS. Overall, this study implied that the combinations of Landsat 8 images and SGCSWA or SGBCS with the systematical, nested and clustering sampling design provided the potential to formulate a methodological framework to map forest carbon density and conduct accuracy assessment at multiple spatial resolutions. However, this methodology needs to be further refined and examined in other forest landscapes

Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods

The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest types, plays an important role in maintaining the balance of the regional carbon cycle. However, accurately estimating forest ecosystem aboveground biomass density (AGB) and mapping its spatial variability at a scale of river basin remains a great challenge. In this study, we attempted to map the current AGB in the Xiangjiang River Basin in central southern China. Three approaches, including a multivariate linear regression (MLR) model, a logistic regression (LR) model, and an improved k-nearest neighbors (kNN) algorithm, were compared to generate accurate estimates and their spatial distribution of forest ecosystem AGB in the basin. Forest inventory data from 782 field plots across the basin and remote sensing images from Landsat 5 in the same period were combined. A stepwise regression method was utilized to select significant spectral variables and a leave-one-out cross-validation (LOOCV) technique was employed to compare their predictions and assess the methods. Results demonstrated the high spatial heterogeneity in the distribution of AGB across the basin. Moreover, the improved kNN algorithm with 10 nearest neighbors showed stronger ability of spatial interpolation than other two models, and provided greater potential of accurately generating population and spatially explicit predictions of forest ecosystem AGB in the complicated basin

Estimation of Vegetation Productivity Using a Landsat 8 Time Series in a Heavily Urbanized Area, Central China

Estimating the net primary production (NPP) of vegetation is essential for eco-environment conservation and carbon cycle research. Remote sensing techniques, combined with algorithm models, have been proven to be promising methods for NPP estimation. High-precision and real-time NPP monitoring in heterogeneous areas requires high spatio-temporal resolution remote sensing data, which are not easy to acquire by single remote sensors, especially in cloudy weather. This study proposes to fuse images of different sensors to provide high spatio-temporal resolution data for NPP estimation in cloud-prone areas. Firstly, the time series Normalized Difference Vegetation Index (NDVI) with a spatial resolution of 30 m and a temporal resolution of 16 days, are obtained by the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Then, the time series NDVI data, combined with meteorological data are input into an improved Carnegie–Ames–Stanford Approach (CASA) model for NPP estimation. This method is validated by a case study of a heavily urbanized area, in the middle reaches of the Yangtze River in China. The results indicate that the NPP estimated by the fused NDVI data has more detailed spatial information than by using the MODIS data. The results show a strong correlation between the actual Landsat8 NDVI and the fused NDVI images, which means that the accuracy of synthetic NDVI images (a 16 day interval and a 30 m resolution) is reliable, and it can provide superior inputs for accurate estimations of a NPP time series. The correlation coefficient (R) and root mean square error between the NPP, based on the fused NDVI and the measured NPP, are 0.66 and 14.280 g C/(m2·yr), respectively, indicating a good consistency. The small discrepancy is caused by the uncertainties of fused NDVI, measurement errors, conversion errors, and other factors in the CASA model. In this study, we achieved NPP with high spatial and temporal resolutions, which can provide higher accuracies of NPP data for analyzing the carbon cycling heavily urbanized areas, compared with similar studies using mono-temporal NPP data. The spatio-temporal fusion technique is an effective way of generating high spatio-temporal resolution images from different sensors, thereby providing enough data for NPP monitoring in urbanized areas

Mapping Paddy Rice Using a Convolutional Neural Network (CNN) with Landsat 8 Datasets in the Dongting Lake Area, China

Rice is one of the world’s major staple foods, especially in China. Highly accurate monitoring on rice-producing land is, therefore, crucial for assessing food supplies and productivity. Recently, the deep-learning convolutional neural network (CNN) has achieved considerable success in remote-sensing data analysis. A CNN-based paddy-rice mapping method using the multitemporal Landsat 8, phenology data, and land-surface temperature (LST) was developed during this study. First, the spatial⁻temporal adaptive reflectance fusion model (STARFM) was used to blend the moderate-resolution imaging spectroradiometer (MODIS) and Landsat data for obtaining multitemporal Landsat-like data. Subsequently, the threshold method is applied to derive the phenological variables from the Landsat-like (Normalized difference vegetation index) NDVI time series. Then, a generalized single-channel algorithm was employed to derive LST from the Landsat 8. Finally, multitemporal Landsat 8 spectral images, combined with phenology and LST data, were employed to extract paddy-rice information using a patch-based deep-learning CNN algorithm. The results show that the proposed method achieved an overall accuracy of 97.06% and a Kappa coefficient of 0.91, which are 6.43% and 0.07 higher than that of the support vector machine method, and 7.68% and 0.09 higher than that of the random forest method, respectively. Moreover, the Landsat-derived rice area is strongly correlated (R2 = 0.9945) with government statistical data, demonstrating that the proposed method has potential in large-scale paddy-rice mapping using moderate spatial resolution images