RESUMEN
Silicon is regarded as the most promising candidate due to its ultrahigh theoretical energy density (4200 mAh g-1). However, the large volume expansion of silicon nanoparticles would result in the destruction of electrodes and a shortened cycle lifetime. Here, inspired by the natural structure of bamboo, the silicon anode with vascular bundle-like structure is proposed to improve the electrochemical performance for the first time. The dense channel wall in the silicon anode can accommodate the volume change of silicon nanoparticles and the transport of ions and electrons is also enhanced. The obtained silicon anodes display excellent mechanical properties (50% compression resilience and the average peel force of 4.34 N) and good wettability. What more, the silicon anodes exhibit high initial coulombic efficiency (94.5%), excellent cycle stability (2100 mAh g-1 after 300 cycles) which stands out among the silicon anodes. Specially, the silicon anode with impressive areal capacity of 36.36 mAh cm-2 and initial coulombic efficiency of 84% is also achieved. This work offers a novel and efficient strategy for the preparation of the flexible electrodes with outstanding performance.
RESUMEN
Silicon is expected to become the ideal anode material for the next generation of high energy density lithium battery because of its high theoretical capacity (4200 mAh g-1 ). However, for silicon electrodes, the initial coulombic efficiency (ICE) is low and the volume of the electrode changes by over 300% after lithiation. The capacity of the silicon electrode decreases rapidly during cycling, hindering the practical application. In this work, a slidable and highly ionic conductive flexible polymer binder with a specific single-ion structure (abbreviated as SSIP) is presented in which polyrotaxane acts as a dynamic crosslinker. The ionic conducting network is expected to reduce the overall resistance, improve ICE and stabilize the electrode interface. Furthermore, the introduction of slidable polyrotaxane increases the reversible dynamics of the binder and improves the long-term cycling stability and rate performance. The silicon anode based on SSIP provides a discharge capacity of ≈1650 mAh g-1 after 400 cycles at 0.5C with a high ICE of upto 92.0%. Additionally, the electrode still exhibits a high ICE of 87.5% with an ultra-high Si loading of 3.84 mg cm-2 and maintains a satisfying areal capacity of 5.9 mAh cm-2 after 50 cycles, exhibiting the potential application of SSIP in silicon-based anodes.
RESUMEN
In order to explore the efficacy of using artificial intelligence (AI) algorithm-based ultrasound images to diagnose iliac vein compression syndrome (IVCS) and assist clinicians in the diagnosis of diseases, the characteristics of vein imaging in patients with IVCS were summarized. After ultrasound image acquisition, the image data were preprocessed to construct a deep learning model to realize the position detection of venous compression and the recognition of benign and malignant lesions. In addition, a dataset was built for model evaluation. The data came from patients with thrombotic chronic venous disease (CVD) and deep vein thrombosis (DVT) in hospital. The image feature group of IVCS extracted by cavity convolution was the artificial intelligence algorithm imaging group, and the ultrasound images were directly taken as the control group without processing. Digital subtraction angiography (DSA) was performed to check the patient's veins one week in advance. Then, the patients were rolled into the AI algorithm imaging group and control group, and the correlation between May-Thurner syndrome (MTS) and AI algorithm imaging was analyzed based on DSA and ultrasound results. Satisfaction of intestinal venous stenosis (or occlusion) or formation of collateral circulation was used as a diagnostic index for MTS. Ultrasound showed that the AI algorithm imaging group had a higher percentage of good treatment effects than that of the control group. The call-up rate of the DMRF-convolutional neural network (CNN), precision, and accuracy were all superior to those of the control group. In addition, the degree of venous swelling of patients in the artificial intelligence algorithm imaging group was weak, the degree of pain relief was high after treatment, and the difference between the artificial intelligence algorithm imaging group and control group was statistically considerable (p < 0.005). Through grouped experiments, it was found that the construction of the AI imaging model was effective for the detection and recognition of lower extremity vein lesions in ultrasound images. To sum up, the ultrasound image evaluation and analysis using AI algorithm during MTS treatment was accurate and efficient, which laid a good foundation for future research, diagnosis, and treatment.
