The electrochemical behavior of BH4- on Cu electrode in 1M NaOH was investigated by cyclic voltammetry(CV) in the potential range of -1.2V to 0.4V versus Hg/HgO. The CV results show that Cu electrode has obvious catalytic activities to the BH4- hydrolysis which belongs to ‘catalytic’ electrode materials. The BH4- electro-oxidation process on Cu is complex and it could associate with the BH4- hydrolysis reaction, followed by oxidation of the intermediate H, then, the intermediate product (e.g. BH3OH−) oxidized, and direct oxidation of BH4- at more positive potentials.

In view of current research on the cracking mechanism of high-energy gas on coal, little attention has been paid to imitating the law of explosive blasting and cracks propagation, and the influence of joint on cracks propagation in the process of high-energy gas impact permeability enhancement has not been taken into account. In this paper, the effects of joint dip angles and joint lengths on cracks size propagation are studied by using a similar simulation test and RFPA2D-dynamic numerical simulation software. In the process of impact permeability enhancement of high-energy gas, the extension direction of the cracks is approximately parallel to the joint, and with the increase in the dip angle and length, the higher the number of cracks, the larger the extension range, and the closer it is to the permeability enhancement holes, the fracture network is formed. When the dip angle of the joint is 30°, the impact permeability enhancement effect results in an obvious zoning phenomenon. When the joint dip angle is 60° and 90°, there is a higher number of cracks and the cracks network is formed, and with the increase in the dip angle, the more the cracks develop and the better the impact permeability enhancement effect.

In order to study the damage evolution law and failure mechanism of mudstone under different stress states, with the help of high-resolution CT scanning equipment, in situ CT scanning experiments of mudstone under uniaxial compression were carried out. Combined with digital core technology and the digital volume image correlation method, the 3D characterization of meso-structure and the evolution process of localized damage in mudstone were analyzed. The research shows that brittle minerals such as quartz in mudstone often exist in the form of agglomerated strips, resulting in the formation of weak structural planes at the contact surfaces of different minerals. There are a large number of primary intergranular pores near the mineral accumulation zone. With the increase in axial load, the connectivity of pores will gradually increase, cracks will gradually emerge, internal pores will develop abnormally, and rocks will reach the critical state of failure; at this time, the throat number and coordination number of pores increase obviously. There was no obvious difference found in the distribution of mineral particles of different sizes, and the slip between mineral zones was mainly dominated by small particles. The accumulated mineral zone was able to easily form a weak surface, and the aggregated mineral zone under loading was easily able to produce local deformation, which is related to the mechanical properties of the mineral zone and its surrounding rock matrix, with the rock failure easily occurring along the junction of the two minerals. The displacement in the polymeric mineral zone was small, the deformation displacement of the rock skeleton dominated by clay minerals near the quartz mineral zone was larger, and the stronger quartz minerals restrained the rock skeleton deformation in the region.

No abstract

Aiming at solving the problem that it is difficult to recognize the quiet period of acoustic emission in rocks, four machine learning algorithms were adopted to develop and improve the recognition method of the quiet period of acoustic emission. In the process of establishing the model, the time domain data of acoustic emission were standardized and processed by box diagram method, so as to clean the abnormal data and reduce the dimension, and the frequency domain data were denoised by wavelet four-layer transform and wavelet packet three-layer energy decomposition, and a group of 8 wavelet packet energy parameters were established as frequency domain characteristic parameters. Based on AE time domain data, frequency domain data, and composite data (time-frequency domain data sets), the grid search traversal parameter technique was used to obtain the optimal parameters of four machine learning models. The accuracy, precision, recall, and F 1 score were used to verify and evaluate the recognition performance of the models. The study results show that the recognition effects of the models are good, the model accuracy of the frequency domain data set is the lowest, and the model accuracy of the composite data set is the highest, with an accuracy of more than 90%. The kernel support vector machine model has the best performance, and its average precision is 0.87. The random forest (RF) model is the best model for recognizing quiet period of acoustic emission.