Directly Observing the Reversible Unfolding and Refolding of an Alpha/Beta Protein by Single-Molecule Atomic Force Microscopy

Digitalitzat per Artypla

Quasi-solid-state electrolyte for rechargeable high-temperature molten salt iron-air battery

Molten salts are a unique type of electrolyte enabling high-temperature electrochemical energy storage (EES) with unmatched reversible electrode kinetics and high ion-conductivities, and hence impressive storage capacity and power capability. However, their high tendency to evaporate and flow at high temperatures challenges the design and fabrication of the respective EES devices in terms of manufacturing cost and cycling durability. On the other hand, most of these EES devices require lithium-containing molten salts as the electrolyte to enhance performances, which not only increases the cost but also demands a share of the already limited lithium resources. Here we report a novel quasi-solid-state (QSS) electrolyte, consisting of the molten eutectic mixture of Na2CO3-K2CO3 and nanoparticles of yttrium stabilized zirconia (YSZ) in a mass ratio of 1:1. The QSS electrolyte has relatively lower volatility in comparison with the pristine molten Na2CO3-K2CO3 eutectic, and therefore significantly suppresses the evaporation of molten salts, thanks to a strong interaction at the interface between molten salt and YSZ nanoparticles at high temperatures. The QSS electrolyte was used to construct an iron-air battery that performed excellently in charge-discharge cycling with high columbic and energy efficiencies. We also propose and confirm a redox mechanism at the three-phase interlines in the negative electrode. These findings can help establish a simpler and more efficient approach to designing low-cost and high-performance molten salt metal-air batteries with high stability and safety

Knowledge Domain and Emerging Trends in Organic Photovoltaic Technology: A Scientometric Review Based on CiteSpace Analysis

To study the rapid growth of research on organic photovoltaic (OPV) technology, development trends in the relevant research are analyzed based on CiteSpace software of text mining and visualization in scientific literature. By this analytical method, the outputs and cooperation of authors, the hot research topics, the vital references and the development trend of OPV are identified and visualized. Different from the traditional review articles by the experts on OPV, this work provides a new method of visualizing information about the development of the OPV technology research over the past decade quantitatively

The long non-coding RNA PANDAR regulates cell proliferation and epithelial-to-mesenchymal transition in glioma

Glioma is one of the most aggressive intracranial tumors in the central nervous system. The long non-coding RNA P21-associated ncRNA DNA damage activated (PANDAR) has been reported to be an oncogene or tumor suppressor in several cancers. However, the prognostic value and biological function of PANDAR in glioma have not been described. Here, we report that expression of PANDAR is significantly upregulated in glioma tissues and cell lines. PANDAR expression was correlated with tumor size (p=0.044) and World Health Organization (WHO) grades (p=0.005), as shown by chi-squared test. Moreover, significant upregulation of PANDAR was found to correlate with poor prognosis in glioma, as shown using Kaplan-Meier method and Cox multivariate survival analysis. Furthermore, PANDAR knockdown suppressed cell proliferation, G1/S transition, migration and invasion, and promoted apoptosis in glioma cell lines (U251 and U87). PANDAR knockdown decreased expression of CDK4, Bcl-2, N-cadherin and Vimentin, but increased E-cadherin expression in glioma cells. In conclusion, our data suggest PANDAR as a potential prognostic biomarker and therapeutic candidate for glioma

Fabrication of Lanthanum Strontium Manganite Ceramics via Agar Gel Casting and Solid State Sintering

Fabricating lanthanum strontium manganite (LSM) ceramics with certain shapes is important for the design and construction of high-temperature energy conversion and storage devices. Here, we describe a low-cost and environmentally friendly method for fabricating LSM ceramics via agar gel casting and high temperature sintering. This new approach uses temperature tuning to fabricate LSM gel bodies, not only by manufacturing in the secondary process but also by remolding and recycling during the gel casting process. The effect of the sintering temperature on the properties of LSM ceramics was investigated as well. As a result, the porosity and compressive strength of LSM ceramics sintered at 1000 °C are ~60% and 5.6 MPa, respectively. When the sintering temperature increases to 1200 °C, the porosity decreases to ~28%, whereas the compressive strength increases to 25 MPa, which is able to meet the requirement of cathode-supported SOFCs (solid oxide fuel cells)

The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine

The extraction and classification of crops is the core issue of agricultural remote sensing. The precise classification of crop types is of great significance to the monitoring and evaluation of crops planting area, growth, and yield. Based on the Google Earth Engine and Google Colab cloud platform, this study takes the typical agricultural oasis area of Xiangride Town, Qinghai Province, as an example. It compares traditional machine learning (random forest, RF), object-oriented classification (object-oriented, OO), and deep neural networks (DNN), which proposes a random forest combined with deep neural network (RF+DNN) classification framework. In this study, the spatial characteristics of band information, vegetation index, and polarization of main crops in the study area were constructed using Sentinel-1 and Sentinel-2 data. The temporal characteristics of crops phenology and growth state were analyzed using the curve curvature method, and the data were screened in time and space. By comparing and analyzing the accuracy of the four classification methods, the advantages of RF+DNN model and its application value in crops classification were illustrated. The results showed that for the crops in the study area during the period of good growth and development, a better crop classification result could be obtained using RF+DNN classification method, whose model accuracy, training, and predict time spent were better than that of using DNN alone. The overall accuracy and Kappa coefficient of classification were 0.98 and 0.97, respectively. It is also higher than the classification accuracy of random forest (OA = 0.87, Kappa = 0.82), object oriented (OA = 0.78, Kappa = 0.70) and deep neural network (OA = 0.93, Kappa = 0.90). The scalable and simple classification method proposed in this paper gives full play to the advantages of cloud platform in data and operation, and the traditional machine learning combined with deep learning can effectively improve the classification accuracy. Timely and accurate extraction of crop types at different spatial and temporal scales is of great significance for crops pattern change, crops yield estimation, and crops safety warning

The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine

The extraction and classification of crops is the core issue of agricultural remote sensing. The precise classification of crop types is of great significance to the monitoring and evaluation of crops planting area, growth, and yield. Based on the Google Earth Engine and Google Colab cloud platform, this study takes the typical agricultural oasis area of Xiangride Town, Qinghai Province, as an example. It compares traditional machine learning (random forest, RF), object-oriented classification (object-oriented, OO), and deep neural networks (DNN), which proposes a random forest combined with deep neural network (RF+DNN) classification framework. In this study, the spatial characteristics of band information, vegetation index, and polarization of main crops in the study area were constructed using Sentinel-1 and Sentinel-2 data. The temporal characteristics of crops phenology and growth state were analyzed using the curve curvature method, and the data were screened in time and space. By comparing and analyzing the accuracy of the four classification methods, the advantages of RF+DNN model and its application value in crops classification were illustrated. The results showed that for the crops in the study area during the period of good growth and development, a better crop classification result could be obtained using RF+DNN classification method, whose model accuracy, training, and predict time spent were better than that of using DNN alone. The overall accuracy and Kappa coefficient of classification were 0.98 and 0.97, respectively. It is also higher than the classification accuracy of random forest (OA = 0.87, Kappa = 0.82), object oriented (OA = 0.78, Kappa = 0.70) and deep neural network (OA = 0.93, Kappa = 0.90). The scalable and simple classification method proposed in this paper gives full play to the advantages of cloud platform in data and operation, and the traditional machine learning combined with deep learning can effectively improve the classification accuracy. Timely and accurate extraction of crop types at different spatial and temporal scales is of great significance for crops pattern change, crops yield estimation, and crops safety warning