The assessment of particle and cell size in electrical microfluidic flow cytometers has become common practice. Nevertheless, in flow cytometers with coplanar electrodes accurate determination of particle size is difficult, owing to the inhomogeneous electric field. Pre-defined signal templates and compensation methods have been introduced to correct for this positional dependence, but are cumbersome when dealing with irregular signal shapes. We introduce a simple and accurate post-processing method without the use of pre-defined signal templates and compensation functions using supervised machine learning. We implemented a multiple linear regression model and show an average reduction of the particle diameter variation by 37% with respect to an earlier processing method based on a feature extraction algorithm and compensation function. Furthermore, we demonstrate its application in flow cytometry by determining the size distribution of a population of small (4.6 ± 0.9 μm) and large (5.9 ± 0.8 μm) yeast cells. The improved performance of this coplanar, two electrode chip enables precise cell size determination in easy to fabricate impedance flow cytometers.

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In this work, the different techniques for non-destructive in situ measurement of chloride ion concentration are presented. Non-destructive (ND) in situ measurement is crucial for reliable and continuous determination of chloride ion concentration in concrete. Over the last 20 years, several studies have been performed on ND measurements. These were mainly focused on the application of electrochemical and electromagnetic techniques. Each technique has its advantages and disadvantages. Depending on the requirement of assets managers and constructors and considering the limitations, these techniques can be well applied. The main concepts and comparative analysis, in view of possibilities and limitations, of these non-destructive techniques are presented in this paper.@en