The release of chemicals and microorganisms from various sources, such as industry, agriculture, animal farming, wastewater treatment plants, and flooding, into water systems have caused water pollution in several parts of our world, endangering aquatic ecosystems and individual health. World Health Organization (WHO) has introduced strict standards for the maximum concentration limits for nutrients and chemicals in drinking water, surface water, and groundwater. It is crucial to have rapid, sensitive, and reliable analytical detection systems to monitor the pollution level regularly and meet the standard limit. Electrochemical biosensors are advantageous analytical devices or tools that convert a bio-signal by biorecognition elements into a significant electrical response. Thanks to the micro/nano fabrication techniques, electrochemical biosensors for sensitive, continuous, and real-time detection have attracted increasing attention among researchers and users worldwide. These devices take advantage of easy operation, portability, and rapid response. They can also be miniaturized, have a long-life span and a quick response time, and possess high sensitivity and selectivity and can be considered as portable biosensing assays. They are of special importance due to their great advantages such as affordability, simplicity, portability, and ability to detect at on-site. This review paper is concerned with the basic concepts of electrochemical biosensors and their applications in various water quality monitoring, such as inorganic chemicals, nutrients, microorganisms’ pollution, and organic pollutants, especially for developing real-time/online detection systems. The basic concepts of electrochemical biosensors, different surface modification techniques, bio-recognition elements (BRE), detection methods, and specific real-time water quality monitoring applications are reviewed thoroughly in this article.

Poly(3,4-ethylenedioxythiophene) (PEDOT) films were prepared by electro-oxidation on Au microelectrodes in an aqueous solution. Electrolyte solutions and polymerization parameters were optimized prior to overoxidation. The effect of overoxidation time has been optimized by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), which results in the film overoxidized for 45 s at 1.35 V presenting a strong adsorption. The other one-step overoxidation film prepared by direct CV ranging from −0.6 V to 1.35 V was polymerized for comparison. Scanning electron microscope (SEM) analysis and Fourier transform infrared (FTIR) spectroscopy were used for monitoring morphological changes and the evolution of functional groups. Both of them indicate increased abundant oxygen functional groups and roughness, yet the products exhibit dendritic morphology and piles of spherical protrusions, respectively. Moreover, double-step overoxidized film showed better electrochemical performance toward lead ion sensing. These characterizations highlight some novel properties that may be beneficial for specific sensing applications.

The various structures and working conditions make gearbox fault recognition (GFR) more challenging. This paper presents a new penalty domain selection machine (PDSM) enabled transfer learning for GFR study. The domain selection rules are designed using the band-selective independent component analysis (BS-ICA) to obtain the relation between different sensor locations and fault components for signal separation. Meanwhile, the initial penalty factors are calculated to speed up the PDSM process. For PDSM, the domain/signal penalty factors are added to the objective error function of original domain selection machine (DSM) to adapt varying working conditions and different sensor locations simultaneously. To solve the mixed optimization problem involved in PDSM, the Karush Kuhn Tucker (KKT) conditions are utilized to transform it to a two-layer single problem. Experiments using drivetrain dynamics simulator (DDS) prove that PDSM has higher diagnostic accuracy than other domain adaptation models. Meanwhile, it indicates faster convergence and stronger clustering capability than DSM.