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In this article, the electrochemical monitoring of phenol (Ph) in water by using a nanostructured MnO2-modified screen-printed carbon electrode (MnO2-SPCE) is reported. MnO2 nanoparticles were synthesized by the redox-precipitation method. Morphological, textural, and microstructural characteristics were investigated by scanning electron microscopy (SEM), X-ray diffraction, Raman, Brunauer–Emmett–Teller (BET) surface area, and pore volume analyses. Electrochemical properties of the modified MnO2-SPCE electrode have been investigated by cyclic voltammetry and linear sweep voltammetry. The detection of Ph in water by the developed sensor was exploited using square wave voltammetry and chronoamperometry. The sensor showed good characteristics, such as simple fabrication, low cost, good sensitivity, stability, reproducibility, and selectivity, against inorganic analytes and other phenolic compounds. The reported results demonstrated the great potential of nanostructured MnO2 in developing high performances electrochemical sensors to be utilized in the field of environmental pollution monitoring. [ABSTRACT FROM AUTHOR]

Here, this article reports about the design of a soft sensor (SS) able to monitor the hazardous gases in industrial plants. The SS is designed to estimate the gas concentrations by means of the measurements coming from an array of sensors, avoiding at the same time the humidity and temperature influence on array outputs. The SS has been designed with a data-driven approach, using a set of experimental data acquired in a laboratory. The design methodologies of two different SSs are compared, with the aim of obtaining both the good performance and a low computational complexity. As a first approach, a principal component analysis (PCA) has been performed to exploit the high correlation among some of the measures coming from the sensor array. A classical multilayer perceptron neural network is then trained to estimate the relationships between the PCA outputs and the gas concentrations. As a second approach, a deep belief network (DBN) has been considered. The data here reported show a good accuracy in the evaluation of several gas concentrations, even in the presence of noised measurements, allowing an efficient risk warning. Even if both the methods gave a similar performance, a lower number of model parameters and a lower noise sensitivity are obtained when using DBNs. [ABSTRACT FROM AUTHOR]

Neurodegenerative diseases such as Alzheimer, Parkinson, motor neuron, and Chorea affect millions of people today. Their effect on the central nervous system causes the loss of brain functions as well as motor disturbances and sometimes cognitive deficits. In such a scenario, the monitoring and evaluation of early symptoms are mandatory for the improvement of the patient’s quality of life. Here, the authors describe the development, the laboratory calibration, and the “in-field validation” under the medical supervision of a movement tremors recorder for subjects affected by neurodegenerative diseases. The developed device is based on an array of four accelerometers connected to an embedded development board. This system is able to monitor tremor/movement, accidental falls, and, moreover, it can track the Alzheimer subjects’ geographical position. A remote supervisor can collect data from the system through Bluetooth, Wi-Fi, or GSM connections. A data compression algorithm was developed directly on board in order to increase the efficiency of data transmission and reduce power consumptions. [ABSTRACT FROM AUTHOR]