A data-driven scheme for modeling electrical impedance in biosensors is presented by a subspace method working with the singular value decomposition of structured voltage and current data. Contrary to the classical electrical impedance spectroscopy (EIS) methods, our scheme uses simple instrumentation, works in time-domain, provides fast results, and does not require semi-empirical assumptions to retrieve structured models from data. We show how data-driven models exhibit a close relationship with lumped-element circuits, encoding dielectric and conductive properties detected by the sensor in the range from 10 kHz up to 10 MHz. Performance results are shown for calibration networks and two case studies: (i) a buffer solution, and (ii) a biological cell suspension. Finally, the viability of the scheme is discussed when compared with the classical EIS method.
In this paper, we present an attractive EGFET-based pH sensor that integrates a n-channel metal-oxide-semiconductor field-effect transistor (MOSFET) as the transducer and low-cost electrochemical screen-printed electrodes (SPE) as the sensitive element. Our sensor is based on a metallic silver/silver-chloride (Ag/AgCl) reference electrode and an indium-tin-oxide (ITO) sensitive electrode, operating in the pH range of 2 to 9. Results show that the proposed sensor can measure pH with acceptable sensitivity and resolution.
The development of sensitive and affordable testing devices for infectious diseases is essential to preserve public health, especially in pandemic scenarios. In this work, we have developed an attractive analytical method to monitor products of genetic amplification, particularly the loop-mediated isothermal amplification reaction (RT-LAMP). The method is based on electrochemical impedance measurements and the distribution of relaxation times model, to provide the so-called time-constant-domain spectroscopy (TCDS). The proposed method is tested for the SARS-CoV-2 genome, since it has been of worldwide interest due to the COVID-19 pandemic. Particularly, once the method is calibrated, its performance is demonstrated using real wastewater samples. Moreover, we propose a simple classification algorithm based on TCDS data to discriminate among positive and negative samples. Results show how a TCDS-based method provides an alternative mechanism for label-free and automated assays, exhibiting robustness and specificity for genetic detection.
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