The state of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs). Recently, the SOH estimation method based on electrochemical impedance spectroscopy (EIS) has been proven effective. In response to different practical applications, two models for SOH estimation are proposed in this paper. Aiming at based on the equivalent circuit model (ECM) method, a variety of ECMs are proposed. Used EIS to predict the ECM, the results show that the improved method ensures the correctness of the ECM and improves the estimation results of SOH. Aiming at a data-driven algorithm, proposes a convolution neural network (CNN) to process EIS data which can not only extract the key points but also simplifies the complexity of manual feature extraction. The bidirectional long short-term memory (BiLSTM) model was used for serial regression prediction. Moreover, the improved Particle Swarm Optimization (IPSO) algorithm is proposed to optimize the model. Comparing the improved model (IPSO-CNN-BiLSTM) with the traditional PSO-CNN-BiLSTM, CNN-BiLSTM and LSTM models, the prediction results are improved by 13.6%, 93.75% and 94.8%, respectively. Besides that, the two proposed methods are 27% and 35% better than the existing gaussion process regression (GPR) model, which indicates that the proposed improved methods are more flexible for SOH estimation with higher precision.

Electrochemical supercapacitors are a promising type of energy storage device with broad application prospects. Developing an accurate model to reflect their actual working characteristics is of great research significance for rational utilization, performance optimization, and system simulation of supercapacitors. This paper presents the fundamental working principle and applications of supercapacitors, analyzes their aging mechanism, summarizes existing supercapacitor models, and evaluates the characteristics and application scope of each model. By examining the current state and limitations of supercapacitor modeling research, this paper identifies future development trends and research focuses in this area.

The detection of glucose concentration has a wide range of applications and plays a significant role in the fields of the food industry, medical health, and illness diagnostics. The utilization of sensor technology for glucose concentration detection is an effective approach. Glucose sensors utilizing nanomaterials, with high sensitivity, strong resistance to interference, and compact size, exhibit tremendous potential in glucose concentration detection. Traditional enzyme-based sensors exhibit superior selectivity and high sensitivity; however, they are deficient in terms of interference resistance capabilities. With the development of nanotechnology, the performance of glucose sensors has been significantly improved. This review discusses the research progress in non-enzymatic electrochemical glucose nanosensors, including noble metal-based glucose sensors and non-noble transition metal compound-based glucose sensors, as well as the applications of multimetallic materials in nanosensors. Additionally, the application of nanosensors based on fluorescence and colorimetric principles in the detection of glucose concentration is introduced in this review. Finally, a perspective on the challenges and prospects of nanosensors in the field of glucose detection is presented.

Graphene has recently attracted a wide range of research interests due to its rigorous two-dimensional structure and extraordinary electrical, thermal and mechanical properties. As a conductive agent, an activated carbon supercapacitor can obtain better performance. This paper summarizes the latest research progress, mainly from two aspects: (1) the preparation of an activated carbon base for a supercapacitor based on waste sugar solution and the relationship between pore structure and activation parameters, and (2) the application of the two-dimensional materials graphene and its composite materials in electric double-layer capacitors, graphene–polymer composite tantalum capacitors, graphene–transition metal oxide composite tantalum capacitors, and asymmetric super capacitors. The studies found that graphene and its composite materials have obvious advantages in improving the cycle efficiency, conversion rate, and energy density of supercapacitors, the overall energy efficiency of mechanical systems, and the chemical properties of nanoelectronics. Therefore, it is urgent to summarize these works in order to promote the next development. Graphene is expected to be effectively and environmentally quantified in the near future, and its application in supercapacitors will be further expanded and matured.

Cd pollution in marine bays is one of the critical environmental issues, and understanding the stages of transporting process of Cd in marine bays is essential to pollution control. This paper analyzed the stages of Cd's transporting process in waters in Jiaozhou Bay during 1979–1983. Results showed that the transporting process in waters in Jiaozhou Bay included seven different stages of 1) the sedimentation of Cd content was beginning, 2) the sedimentation of Cd content was increasing, 3) the sedimentation of Cd content was increasing greatly, 4) the sedimentation of Cd content was beginning to decrease, 5) the sedimentation of Cd content was decreasing stably, 6) the sedimentation of Cd content was beginning to stop, and 7) the sedimentation of Cd content was beginning to fully stop. In according to the different stages of Cd's transporting process in waters, the changing trends of Cd contents in surface and bottom waters and their relationships could be defined and predicted.