In healthcare, new diagnostic tools that help in the diagnosis, prognosis, and monitoring of diseases rapidly and accurately are in high demand. For in-situ measurement of disease or infection biomarkers, point-of-care devices provide a dramatic speed advantage over conventional techniques, thus aiding clinicians in decision-making. During the last decade, paper-based analytical devices, combining paper substrates and electrochemical detection components, have emerged as important point-of-need diagnostic tools. This review highlights significant works on this topic over the last five years, from 2015 to 2019. The most relevant articles published in 2018 and 2019 are examined in detail, focusing on device fabrication techniques and materials applied to the production of paper fluidic and electrochemical cell architectures as well as on the final device assembly. Two main approaches were identified, that are, on one hand, those ones where the fabrication of the electrochemical cell is done on the paper substrate, where the fluidic structures are also defined, and, on the other hand, the fabrication of those ones where the electrochemical cell and liquid-driving paper component are defined on different substrates and then heterogeneously assembled. The main limitations of the current technologies are outlined and an outlook on the current technology status and future prospects is given.
In this work, an electronic tongue (ET) system based on an array of potentiometric ion-selective electrodes (ISEs) for the discrimination of different commercial beer types is presented. The array was formed by 21 ISEs combining both cationic and anionic sensors with others with generic response. For this purpose beer samples were analyzed with the ET without any pretreatment rather than the smooth agitation of the samples with a magnetic stirrer in order to reduce the foaming of samples, which could interfere into the measurements. Then, the obtained responses were evaluated using two different pattern recognition methods, principal component analysis (PCA), which allowed identifying some initial patterns, and linear discriminant analysis (LDA) in order to achieve the correct recognition of sample varieties (81.9% accuracy). In the case of LDA, a stepwise inclusion method for variable selection based on Mahalanobis distance criteria was used to select the most discriminating variables. In this respect, the results showed that the use of supervised pattern recognition methods such as LDA is a good alternative for the resolution of complex identification situations. In addition, in order to show an ET quantitative application, beer alcohol content was predicted from the array data employing an artificial neural network model (root mean square error for testing subset was 0.131 abv).
Monitoring chemical contamination in water is a must to guarantee the supply to the society of this more and more scarce prized asset. The European Union as well as other bodies have released reports and directives defining lists of substances whose detection in waters should be prioritized and posing limits to the maximum allowable concentrations that drinking water must have. The scientific community has been actively working on the development of analytical tools that could be applied in the detection of hazardous chemical species in waters. Here, an overview of electrochemical devices with the potential of being implemented to the monitoring of the forty five pollutants include in the list of priority substances set in the 2013 EU directive that could be grouped into heavy metals, pesticides, hydrocarbons, halogenated hydrocarbons and alkyl phenols, is given, aiming at showing their benefits and limitations in this scenario.
This work reports on the fabrication and comparative analytical assessment of electrochemical sensors applied to the rapid analysis of chemical oxygen demand (COD) in urban waste waters. These devices incorporate a carbon nanotube-polystyrene composite, containing different inorganic electrocatalysts, namely, Ni, NiCu alloy, CoO, and CuO/AgO nanoparticles. The sensor responses were initially evaluated using glucose as standard analyte and then by analyzing a set of real samples from urban wastewater treatment plants. The estimated COD values in the samples were compared with those provided by an accredited laboratory using the standard dichromate method. The sensor prepared with the CuO/AgO-based nanocomposite showed the best analytical performance. The recorded COD values of both the sensor and the standard method were overlapped, considering the 95% confidence intervals. In order to show the feasible application of this approach for the detection of COD online and in continuous mode, the CuO/AgO-based nanocomposite sensor was integrated in a compact flow system and applied to the detection of wastewater samples, showing again a good agreement with the values provided by the dichromate method.
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