Designing cost-effective, high-active, and durable noble-metal-free electrocatalysts as alternative electrode materials for methanol oxidation reaction (MOR) is of significant importance for practical applications. Herein, we report a facile one-pot synthetic...
During spinning, the chemical component content of natural fibers has a great influence on the mechanical properties. How to rapidly and accurately measure these properties has become the focus of the industry. In this work, a grey model (GM) for rapid and accurate prediction of the mechanical properties of windmill palm fiber (WPF) was established to explore the effect of chemical component content on the Young’s modulus. The chemical component content of cellulose, hemicellulose, and lignin in WPF was studied using near-infrared (NIR) spectroscopy, and an NIR prediction model was established, with the measured chemical values as the control. The value of RC and RCV were more than 0.9, while the values of RMSEC and RMSEP were less than 1, which reflected the excellent accuracy of the NIR model. External validation and a two-tailed t-test were used to evaluate the accuracy of the NIR model prediction results. The GM(1,4) model of WPF chemical components and the Young’s modulus was established. The model indicated that the increase in cellulose and lignin content could promote the increase in the Young’s modulus, while the increase in hemicellulose content inhibited it. The establishment of the two models provides a theoretical basis for evaluating whether WPF can be used in spinning, which is convenient for the selection of spinning fibers in practical application.
Hollow Pt tetrapods with {111} facets, surface concave topology, and ultrathin walls exhibit superior electro-catalytic activity and stability towards the acidic oxygen reduction reaction.
The paper develops a prediction model based on the surface electromyography (sEMG) signals to recognize the five different arm-related motions. We collect 100 groups of the three-channels signals on Biceps, Triceps, Brachioradialis as the raw data set. Then we extract four features from the data to describe the characteristics of different motions in the data set. In the pre-processing stage, we compare three types of EMD-based filtration methods to denoise signals and choose the most efficient one, then use principle component analysis to reduce the dimension of the data. We use three different methods to classify and predict the processed data for a higher accuracy. The contributions are following: This project successfully automated the pattern recognition of arm-related motions. Moreover, the project generated a valuable data set; Compared performances of EMD, EEMD and CEEMDAN in sEMG signals; Found that FCNN is the most accurate algorithm in this project. Future works include obtain more data from more volunteers to expand the data set.
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