This paper reviews the development of all-solid-state ion-selective electrodes (ASSISEs) for agricultural crop detection. Both nutrient ions and heavy metal ions inside and outside the plant have a significant influence on crop growth. This review begins with the detection principle of ASSISEs. The second section introduces the key characteristics of ASSISE and demonstrates its feasibility in crop detection based on previous research. The third section considers the development of ASSISEs in the detection of corps internally and externally (e.g., crop nutrition, heavy metal pollution, soil salinization, N enrichment, and sensor miniaturization, etc.) and discusses the interference of the test environment. The suggestions and conclusions discussed in this paper may provide the foundation for additional research into ion detection for crops.
The attention of electric vehicle (EV) development is still hot at present. As an important part of EV - power battery, its safety issue still maintains great attention. In recent years, the research on thermal runaway (TR) of lithium-ion batteries (LIBs) has taken a big step forward. The latest research on TR mechanism, inducement and propagation are firstly introduced, and the latest research status of TR protection is partially extended in combination with these principles. Then the influence of the material and design of the battery cell components on TR is introduced, and lastly the safety measures before and after TR are comprehensively reviewed. This paper has a summary effect on past TR research and a referential effect for future TR protection.
Calcium, potassium, nitrogen, magnesium, and phosphorus, the main elements of the nutrient solution, are absorbed by plants and play an important role in plants. By measuring Ca2+, K+, Mg2+, NH4+, NO3−, HPO42−, the Artificial Neural Networks (ANNs) were used in this study to accurately calculate the concentrations of these elements. Firstly, the error sources of the calculating element concentration were analyzed based on the data of six-ion measurement experiments. Subsequently, various optimization algorithms were compared to optimize back propagation (BP) and radial basis function (RBF) ANNs. Finally, the results of mean relative errors (MREs) and recovery values show that ANNs can effectively reduce the measurement error of ion sensors. From the perspective of recovery values, the prediction error of all elements can be controlled within 15%. From the perspective of MRE, except for magnesium and phosphorus elements, the improved model prediction errors of other elements were also less than 10%.
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