RESUMEN
Exploring larger surface area electrode materials is crucial for the development of an efficient supercapacitors (SCs) with superior electrochemical performance. Herein, a cost-effective strategy was adopted to synthesize a series of ZIF8 nanocrystals, and their size effect as a function of surface area was also examined. The resultant ZIF8-4 nanocrystal exhibits a uniform hexagonal structure with a large surface area (2800 m2 g-1) and nanometre size while maintaining a yield as high as 78%. The SCs performance was explored by employing different aqueous electrolytes (0.5 M H2SO4 and 1 M KOH) in a three-electrode set-up. The SC performance using a basic electrolyte (1 M KOH) was superior owing to the high ionic mobility of K+. The optimized ZIF8-4 nanocrystal electrode showed a faradaic reaction with a highest capacitance of 1420 F g-1 at 1 A g-1 of current density compared to other as-prepared electrodes in the three-electrode assembly. In addition, the resultant ZIF8-4 was embedded into a symmetric supercapacitor (SSC), and the device offered 350 F g-1 of capacitance with a maximum energy and power density of 43.7 W h kg-1 and 900 W kg-1 at 1 A g-1 of current density, respectively. To determine the practical viewpoint and real-world applications of the ZIF8-4 SSC device, 7000 GCD cycles were performed at 10 A g-1 of current density. Significantly, the device exhibited a cycling stability around 90% compared to the initial capacitance. Therefore, these findings provide a pathway for constructing large surface area ZIF8-based electrodes for high-value-added energy storage applications, particularly supercapacitors.
RESUMEN
In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications.