The synthesis of undoped cobalt oxide and cobalt oxide with 4%nickel doping is studied in samples obtained through hydrothermal technique. After the final heat treatment at 300°C, X-ray diffraction analysis indicated the formation of cobalt spinel oxide with a complete replacement of nickel in the lattice. An average crystallite size of ~42 nm and ~31nm was found, as well as a particle size of ~ 20 nm and ~30 nm for Co3O4 and Co3-xNixO4, respectively. Structural parameters were established through Rietveld refinement with a good correspondence between the simulated and the experimental pattern with values of x2 = 1.25 and GOF = 0.90 for Co3O4. The energy band gap was found through UV-Vis spectroscopy in two different regions: Eg1 and Eg2 values vary between 1.54 and 2.04eV for samples doped with nickel. The SEM results indicated the formation of nanostructures with semi-cubic shape and irregular rods. Parameters such as crystal size, particle size, surface area, as well as morphology of the final product depend on doping.

Electronic tongue-type sensor arrays are devices used to determine the quality of substances and seek to imitate the main components of the human sense of taste. For this purpose, an electronic tongue-based system makes use of sensors, data acquisition systems, and a pattern recognition system. Particularly, in the latter, machine learning techniques are useful in data analysis and have been used to solve classification and regression problems. However, one of the problems in the use of this kind of device is associated with the development of reliable pattern recognition algorithms and robust data analysis. In this sense, this work introduces a taste recognition methodology, which is composed of several steps including unfolding data, data normalization, principal component analysis for compressing the data, and classification through different machine learning models. The proposed methodology is tested using data from an electronic tongue with 13 different liquid substances; this electronic tongue uses multifrequency large amplitude pulse signal voltammetry. Results show that the methodology is able to perform the classification accurately and the best results are obtained when it includes the use of K-nearest neighbor machine in terms of accuracy compared with other kinds of machine learning approaches. Besides, the comparison to evaluate the methodology is made with different classification performance measures that show the behavior of the process in a single number.

Cobalt oxide (Co₃O₄) nanoparticles were successfully prepared by sol–gel and hydrothermal methods for antibacterial and photocatalytic applications with the addition of 1%, 4% nickel (Ni), and reduced graphene oxide (rGO). The structural and morphological properties of the nanoparticles were obtained by XRD, TEM and FESEM techniques. Cobalt oxide showed typical crystallographic planes to cubic phase and particles with inferior diameter to 30 nm. The Ni-Co₃O₄ + rGO nanocrystals exhibit a band gap value of 2.0 eV. The bactericidal tests for S. aureus and E. coli revealed that the insertion rGO synthesized by the sol–gel method promoted the antimicrobial activity for both microorganisms. Afterward, the photocatalytic assay for the atrazine contaminant showed significant responses to pesticide removal attributed to the simultaneous adsorption and degradation process. In addition, the sol–gel process found a better response to Ni-Co₃O₄ in the presence of rGO, indicating a nanocomposite superior synergism.