RESUMEN
The objective of this work was to investigate the self-healing properties and mechanical damage characteristics of dissolved salt columns under different humidity and time conditions. Based on the results of electron microscope scanning and uniaxial mechanical tests, the microscopic element distribution of the ore and the microscopic morphology of the minerals were investigated, and the healing, mechanical, and damage properties of the specimens were analyzed, which revealed the microscopic reinforcement mechanism of the damage healing of the dissolved salt columns. The results showed that the healing reinforcement, compressive strength, and modulus of elasticity of dissolved salt columns under uniaxial compression show a tendency to increase, then decrease with the increase of humidity, and gradually increase with the increase of the maintenance time and reach the maximum value at 10% humidity and 30 days of maintenance time, which are 3.48, 8.07, and 650 MPa, respectively. The damage type of the healed specimen as a whole gradually transitioned from tensile damage to shear-slip type, indicating that the brittle damage characteristics of the specimen under loading became more and more significant. Based on the principle of strain equivalence, the damage evolution equation under uniaxial compression of solid potash dissolved salt columns describes the damage evolution law and destruction process of the specimen, and the results of the damage characterization of the dissolved salt columns are consistent with the change rule of the healing properties and mechanical properties with humidity and conservation time. Based on the fine morphological features of the dissolved salt column specimens after self-healing, three different self-healing microscopic mechanisms for damage recovery of solid potash dissolved salt columns are summarized, namely, healing of damaged microcracks based on diffusion, recrystallization healing of brine-filled microfractures, and healing adhesion of crystal particles in dissolved zones. These microstructures effectively transform cracks into isolated sections and play a key role in improving mechanical properties. In addition, the higher the humidity, the thicker adsorbed water film is produced on the fissure surface, which accelerates the transportation of materials on the fissure surface, and the healing rate of the dissolved salt columns increases. However, when the humidity is too high, it causes the evaporation of the liquid film to be less than the recharge of water vapor, which reduces the healing rate of the dissolved salt columns. Thus, suitable humidity produces a more pronounced healing effect than an environment maintained at a constant high humidity level. The research results can provide theoretical guidance for the filling mining of solid potassium salt.
RESUMEN
High voltage, high rate, and cycling-stable cathodes are urgently needed for development of commercially viable sodium ion batteries (SIBs). Herein, we report a facial ball-milling to synthesize a carbon-coated Na3V2(PO4)2F3 composite (C-NVPF). Benefiting from the highly conductive carbon layer, the C-NVPF material exhibits a high reversible capacity (110.6 mA h g-1 at 0.1C), long-term cycle life (54% of capacity retention up to 2000 cycles at 5C), and excellent rate performance (35.1 mA h g-1 at 30C). The present results suggest promising applications of the C-NVPF material as a high-performance cathode for sodium ion batteries.
RESUMEN
Tensor analysis of electroencephalogram (EEG) can extract the activity information and the potential interaction between different brain regions. However, EEG data varies between subjects, and the existing tensor decomposition algorithms cannot guarantee that the features across subjects are distributed in the same domain, which leads to the non-objectivity of the classification result and analysis, In addition, traditional Tucker decomposition is prone to the explosion of feature dimensions. To solve these problems, combined with the idea of feature transfer, a novel EEG tensor transfer algorithm, Tensor Subspace Learning based on Sparse Regularized Tucker Decomposition (TSL-SRT), is proposed in this paper. In TSL-SRT, new EEG samples are considered as the target domain and original samples as the source domain. The target features can be obtained by projecting the target tensor to the source feature space to ensure that all features are in the same domain. Furthermore, to solve the problem of dimension explosion caused by TSL-SRT, a redundant EEG features screening algorithm is adopted to eliminate the redundant features, and achieves 77.8%, 73.2% and 75.3% accuracy on three BCI datasets. By visualizing the spatial basic matrix of the feature space, it can be seen that TSL-SRT is effective in extracting the features of active brain regions in the BCI task and it can extract the multi-domain features of different subjects in the same domain simultaneously, which provides a new method for the tensor analysis of EEG.
RESUMEN
The objective of this work was to investigate the damage characteristics and failure modes of gypsum rock under dynamic impact loading. Split Hopkinson pressure bar (SHPB) tests were performed under different strain rates. The strain rate effects on the dynamic peak strength, dynamic elastic modulus, energy density, and crushing size of gypsum rock were analyzed. A numerical model of the SHPB was established using the finite element software, ANSYS 19.0, and its reliability was verified by comparing it to laboratory test results. The results showed that the dynamic peak strength and energy consumption density of gypsum rock increased exponentially with strain rate, and the crushing size decreased exponentially with the strain rate, both findings exhibited an obvious correlation. The dynamic elastic modulus was larger than the static elastic modulus, but did not show a significant correlation. Gypsum rock fracture can be divided into crack compaction, crack initiation, crack propagation, and breaking stages, and is dominated by splitting failure. With increasing strain rate, the interaction between cracks is noticeable, and the failure mode changes from splitting to crushing failure. These results provide theoretical support for improvements of the refinement process in gypsum mines.
RESUMEN
Tensor analysis can comprehensively retain multidomain characteristics, which has been employed in EEG studies. However, existing EEG tensor has large dimension, making it difficult to extract features. Traditional Tucker decomposition and Canonical Polyadic decomposition(CP) decomposition algorithms have problems of low computational efficiency and weak capability to extract features. To solve the above problems, Tensor-Train(TT) decomposition is adopted to analyze the EEG tensor. Meanwhile, sparse regularization term can then be added to TT decomposition, resulting in a sparse regular TT decomposition (SR-TT). The SR-TT algorithm is proposed in this paper, which has higher accuracy and stronger generalization ability than state-of-the-art decomposition methods. The SR-TT algorithm was verified with BCI competition III and BCI competition IV dataset and achieved 86.38% and 85.36% classification accuracies, respectively. Meanwhile, compared with traditional tensor decomposition (Tucker and CP) method, the computational efficiency of the proposed algorithm was improved by 16.49 and 31.08 times in BCI competition III and 20.72 and 29.45 times more efficient in BCI competition IV. Besides, the method can leverage tensor decomposition to extract spatial features, and the analysis is performed by pairs of brain topography visualizations to show the changes of active brain regions under the task condition. In conclusion, the proposed SR-TT algorithm in the paper provides a novel insight for tensor EEG analysis.