Critical Review of Electro-kinetic Remediation of Contaminated Soils and Sediments: Mechanisms, Performances and Technologies

Remediation of contaminated soil and sediment is important for improving the eco-environmental quality. Electro-kinetic remediation (EKR) is an environmentally friendly technology to migrate and remove pollutants from the soil and sediment matrix. This paper analyses the mechanism and performance of EKR of heavy metals, organic pollutants, and compound pollutants. Moreover, the effect of optimizing individual EKR through soil and sediment pre-treatment (adding acid/oxidant/co-solvent/surfactant, stirring, heating, etc.), electrode optimization (exchange electrode, anode approximation, electrode matrix, etc.), and applying multi-technology combination (electro-kinetic permeable reaction barrier/Fenton/ion, exchange membrane/ultrasonic/electrolyte enhancement, etc.) was evaluated. Factors including incomplete separation of pollutants, variation in physico-chemical properties and microstructure of soil/sediment, and difficulties in in situ practice have restrained the field application of EKR. To solve the above technical challenge, an integrated EKR technology based on pollutant in situ separation, followed by separated contaminant treatment, and subsequent valuable elements recovery is proposed.

On the DC Offset Current Generated during Biphasic Stimulation: Experimental Study

This paper deals with the DC offset currents generated by a platinum electrode matrix during biphasic stimulation. A fully automated test bench evaluates the nanoampere range DC offset currents in a realistic and comprehensive scenario by using platinum electrodes in a saline solution as a load for the stimulator. Measurements are performed on different stimulation patterns for single or dual hexagonal stimulation sites operating simultaneously and alternately. The effectiveness of the return electrode presence in reducing the DC offset current is considered. Experimental results show how for a defined nominal injected charge, the generated DC offset currents differ depending on the stimulation patterns, frequency, current amplitude, and pulse width of a biphasic signal.

Improved Capacitive Proximity Detection for Conductive Objects through Target Profile Estimation

The accuracy of a capacitive proximity sensor is affected by various factors, including the geometry and composition of the nearby object. The quantitative regression models that are used to seek out the relationship between the measured capacitances and distances to objects are highly dependent on the geometrical properties of the objects. Consequently, the application of capacitive proximity sensors has been mainly limited to detection of objects rather than estimation of distances to them. This paper presents a capacitive proximity sensing system for the detection of metallic objects with improved accuracy based on target profile estimation. The presented approach alleviates large errors in distance estimation by implementing a classifier to recognize the surface profiles before using a suitable regression model to estimate the distance. The sensing system features an electrode matrix that is configured to sweep a series of inner-connection patterns and produce features for profile classification. The performance of the sensing modalities is experimentally assessed with an industrial robot. Two-term exponential regression models provide a high degree of fittings for an object whose shape is known. Recognizing the shape of the object improved the regression models and reduced the close-distance measurement error by a factor of five compared to methods that did not take the geometry into account. The breakthroughs made through this work will make capacitive sensing a viable low-cost alternative to existing technologies for proximity detection in robotics and other fields.