This study presents an innovative technique for the in situ analysis of aquatic biochemical elements detected through wet chemical processes. A new compact in situ phosphate analyzer based on sequential injection analysis, liquid waveguide capillary flow cell and spectrophotometry was developed, and a safe and modular electronics-chemical separation mechanical structure was designed. The sequential injection system of this analyzer was optimized, and the major functions of this analyzer were studied and estimated. With a 10 cm liquid waveguide capillary flow cell and a 6.3 min time cost of detection, the analyzer reaches a detection limit of 1.4 μg·L−1 (≈14.7 nM, [PO43−]) and a consumption of 23 μL at most for each reagent. This analyzer was operated in situ and online during two scientific research cruises in the Pearl River Estuary and northern South China Sea. The advantages of this analyzer include its simple versatile manifold, full automation, low chemical consumption and electronics-chemical separate safe structure. Long-term in situ performance of this analyzer will be validated in the future.

Featuring excellent computational rates and highly parallel computing, human brain-based neuromorphic devices have attracted the attention of more and more researchers. There have been numerous reports investigating the use of transistors to simulate synaptic functions; however, the majority of the dielectric and channel layers in these devices are layer-stacked structures, which are not conducive to the modulation of the active layers. Moreover, optimizing the paired pulse facilitation index is a critical factor in enhancing the short-term memory of synaptic devices and constructing high-precision synaptic systems, but it has received inadequate attention. In this study, we present a low-cost electrolyte-gated synaptic transistor with three-dimensional (3D) interfacial contacts, in which the channel layer is SnO2 nanofibers, and the 3D interface reduces the power consumption to 9.6 fJ. This study has simulated some important synaptic behaviors; importantly, the PPF value is as high as 223%, which is related to the slow kinetics of sodium ions. In addition, the Ebbinghaus forgetting curve and its application to image memory are also simulated. These findings provide valuable insight for the future development of complex neuromorphic systems.

This study is targeted at the key state parameters of vehicle stability controllers, the controlled vehicle model, and the nonlinearity and uncertainty of external disturbance. An adaptive double-layer unscented Kalman filter (ADUKF) is used to compute the sideslip angle, and a vehicle stability control algorithm adaptive fuzzy radial basis function neural network sliding mode control (AFRBF-SMC) is proposed. Since the sideslip angle cannot be directly determined, a 7-degrees-of-freedom (DOF) nonlinear vehicle dynamic model is established and combined with ADUKF to estimate the sideslip angle. After that, a vehicle stability sliding mode controller is designed and used to trace the ideal values of the vehicle stability parameters. To handle the severe system vibration due to the large robustness coefficient in the sliding mode controller, we use a fuzzy radial basis function neural network (FRBFNN) algorithm to approximate the uncertain disturbance of the system. Then the adaptive rate of the system is solved using the Lyapunov algorithm, and the systemic stability and convergence of this algorithm are validated. Finally, the controlling algorithm is verified through joint simulation on MATLAB/Simulink-Carsim. ADUKF can estimate the sideslip angle with high precision. The AFRBF-SMC vehicle stability controller performs well with high precision and low vibration and can ensure the driving stability of vehicles.