Engineering PtRu bimetallic nanoparticles with adjustable alloying degree for methanol electrooxidation: enhanced catalytic performance

Abstract(#br)PtRu bimetal is of particularly attractive in various electrocatalytic reactions owing to its synergistic effect, ligand effect and strain effect. Here, PtRu nanoalloy supported on porous graphitic carbon (PC) has been successfully prepared via a very facile method involving co-reduction the precursors of Pt and Ru at 300 °C by H 2 (PtRu/PCL) followed by thermal treatment at high temperature (700 °C, PtRu/PC–H). Specifically, the electrocatalytic performance of PtRu/PC nanoalloy could be dramatically enhanced through high-temperature annealing. This strategy has synthesized smaller Pt and PtRu nanoparticles (ca. L and Pt/PC nanocatalysts. The mass activity and specific activity on PtRu/PC–H nanoalloy can be increased to 1674.2 mA mg −1 Pt and 4.4 mA cm −2 for MOR, it is 4.08 and 8.80 times higher than that of the Pt/PC nanocatalyst, respectively. From in-situ FTIR spectra, we can discover PtRu/PC–H nanoalloy generates CO 2 at a lower potential of −150 mV than those on PtRu/PC–L (0 mV) and Pt/PC (50 mV) nanocatalysts, dramatically improves the ability of cleavage C–H bond and alleviates the CO ads poisoning on active sites. The PtRu/PCH nanocatalyst exhibits maximum power density of 83.7 mW cm −2 in single methanol fuel cell test, which more than threefold than that of commercial Pt/C as the anode catalyst. Those experimental results open an effective and clean avenue in the development and preparation of high-performance Pt-based nanocatalysts for direct methanol fuel cells

High Wind Speed Inversion Model of CYGNSS Sea Surface Data Based on Machine Learning

In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface. The L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) methods, respectively, to finally construct a sea surface high wind speed inversion model. The three models for high wind speed inversion are certified by the test data collected during Typhoon Bavi in 2020. The results show that all three machine learning models can be used for high wind speed inversion on sea surface, among which the CNN method has the highest inversion accuracy with a mean absolute error of 2.71 m/s and a root mean square error of 3.80 m/s. The experimental results largely meet the operational requirements for high wind speed inversion accuracy

Wind Direction Retrieval Using Support Vector Machine from CYGNSS Sea Surface Data

In view of the difficulty of wind direction retrieval in the case of the large space and time span of the global sea surface, a method of sea surface wind direction retrieval using a support vector machine (SVM) is proposed. This paper uses the space-borne global navigation satellite systems reflected signal (GNSS-R) as the remote sensing signal source. Using the Cyclone Global Navigation Satellite System (CYGNSS) satellite data, this paper selects a variety of feature parameters according to the correlation between the features of the sea surface reflection signal and the wind direction, including the Delay Doppler Map (DDM), corresponding to the CYGNSS satellite parameters and geometric feature parameters. The Radial Basis Function (RBF) is selected, and parameter optimization is performed through cross-validation based on the grid search method. Finally, the SVM model of sea surface wind direction retrieval is established. The result shows that this method has a high retrieval classification accuracy using the dataset with wind speed greater than 10 m/s, and the root mean square error (RMSE) of the retrieval result is 26.70°