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The Raman spectrum of liquid ethyldibromosilane, CH 3 CH 2 SiHBr 2 , was recorded at various temperatures between 298 and 218 K, and the spectrum of the crystal was obtained at 210 K from a sample sealed in a capillary. Additional spectra of the amorphous and annealed crystals, which were deposited on a copper finger cooled with boiling liquid nitrogen, were recorded. The infrared spectra were recorded of the vapor and amorphous and crystalline solid in the range 4000-50 cm −1 and mid-infrared spectra isolated at 4.8 K in argon and nitrogen matrices were also observed. These vibrational spectra show that two conformers, anti and gauche, are present in the vapor and in the liquid, but only the anti conformer remains in the crystalline solid. Three conformer pairs in the Raman spectrum of the liquid phase were used to obtain the enthalpy difference, which gave an average value of 128 ± 17 cm −1 (1.53 ± 0.2 kJ mol −1 ) with the anti form lower in energy. At ambient temperature it is estimated that there is 52 ± 2% of the gauche conformer present in the liquid. The optimized geometries, infrared and Raman intensities, and scaled vibrational wavenumbers for the anti and gauche conformers were obtained from ab initio MP2/6-31G(d) calculations with full electron correlation. The conformational energy difference was also obtained from ab initio MP2/6-311 + G(d,p) calculations which gave a predicted energy difference of 97 cm −1 with the anti form the conformer of lower energy. These spectroscopic and theoretical results are discussed and compared with the corresponding quantities for some similar molecules.

In recent years, the analysis of surface electromyography (sEMG) signals by feature engineering and machine learning has developed rapidly. However, when feature engineering is applied to feature extraction of sEMG signals, important feature information in the signals will inevitably be omitted, which will reduce the performance of signal analysis and recognition. Therefore, this paper proposes a method to complete classification of sEMG hand movements based on convolutional neural network (CNN) and stacking ensemble learning. In this method, a primary classifier based on CNN is designed to extract sEMG data features, which avoid omission of important feature information. A secondary classifier based on the stacking method is designed to integrate three primary classifiers trained with time domain, frequency domain and time-frequency domain data of the sEMG signal respectively. Then, several experiments on NinaPro DB5 dataset is performed to evaluate the proposed models. When the window length is 200ms, primary classifier is trained and tested with the sEMG signal data divided by the 80ms, 100ms, and 125ms sliding length. The best accuracy can reach 71%. The primary classifier and the secondary classifier trained and tested with sEMG signal data divided by window lengths of 200ms and 300ms in the case of a sliding length of 100ms. When the window length is 200ms, the best primary classifier accuracy and the best secondary classifier accuracy can be 70.92% and 72.09%, respectively. On the window length of 300ms, the best primary classifier accuracy and the best secondary classifier accuracy can reach 75.02% and 76.02%, respectively. Finally, the model designed is compared with Linear Discriminant Analysis (LDA), Long Short Term Memory-CNN (LCNN), Support Vector Machine (SVM), and Random Forests. Under the same conditions, the average accuracy of the secondary classifier is 11.5%, 13.6%. and 10.1% higher than LDA, SVM, and LCNN, respectively. Also, the average accuracy rate is 3.05% higher than SVM and Random Forests.INDEX TERMS Surface electromyography, movements classification, convolutional neural network, ensemble learning.

The development of inexpensive metal-free catalysts for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is highly desirable for fuel cells and rechargeable metal–air batteries. Black phosphorus (BP), as a new kind of two-dimensional (2D) layer material, has recently shown excellent OER electrocatalytic activity. However, atomistic understanding of the catalytic mechanism is lacking. Here, on the basis of ab initio calculations, we find that pristine phosphorene shows poor ORR/OER performances. However, oxidation can effectively tune the adsorption strength of reactive intermediates and thus change its OER/ORR electrocatalytic performance. For OER, the higher the local oxidation degree (D local O) of phosphorene, the better the OER activity. Therefore, the oxidized phosphorene site with highest D local O shows the best OER catalytic property. In contrast, there exists an optimum D local O for ORR. These findings provide new insights for better understanding and improving the catalytic performances of BP-based electrocatalysts and could stimulate more theoretical and experimental studies in this area.

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