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Item type:Publication, The interaction of metallic ions onto activated carbon surface using computational chemistry software(SAGE Publishing, 2020-07-01)Activated carbon was prepared from the seeds of aguaje palm ( Mauritia flexuosa L.f.) by a chemical activation with phosphoric acid. This activated carbon was used for adsorbing metal ions: Pb(II), Cd(II), and Cr(III). To understand the mechanism of adsorption of these heavy metals (Cr, Cd, and Pb), the activated carbon surface was oxidized with nitric acid (1 M) increasing the oxygenated surface groups showing an increasing in their adsorption capacities of these metals. The oxidized activated carbon slightly increased the maximum adsorption capacity to 5–7%. The order of adsorption for unoxidized and oxidized activated carbons was Pb> Cd> Cr. This experimental information was corroborated by molecular modeling program Hyperchem 8 based adsorption mainly on two factors: the electron density and orbitals—highest occupied molecular orbital and lowest unoccupied molecular orbital.Activated carbons were characterized by adsorption/desorption of N 2 , obtaining an increase of microporous surface area for oxidized activated carbon. An increase of surface acidity and a reduction of isoelectric points were observed in oxidized activated carbon. According to these results, the adsorption of metal ions is favored in contact with an oxidized activated carbon, which has more amount of phenolic and carboxylic functional groups. Similarly, decreasing the isoelectric point indicates that the surface has a higher negative charge. The surface information was corroborated by Hyperchem, which indicates that the surface of the oxidized activated carbon has a higher electron density, indicating a larger amount of electrons on its surface, which means the surface of oxidized activated carbon charges negatively and thereby attracts metal ions. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, SnO2-TiO2 and SnO2-MoO3 based composite gas sensors to develop an e-nose for Peruvian Pisco varieties differentiation(IOP Publishing Ltd, 2022-01-01)There is great interest in the development of a simple system that could identify adulteration or counterfeiting of Peruvian Pisco (a grape-based alcoholic drink). In this study, sensors based on SnO2-TiO2 and SnO2-MoO3 composites with different weight composition ratios were synthesized and characterized. These sensors were tested with aqueous solutions of EtOH/MeOH and Pisco samples of Italia and Quebranta varieties in order to explore their capacity to identify variations in these beverages. The response profile of the most sensitive sensors showed an enhanced response to alcoholic samples with greater content of ethanol up to a concentration of 45%v/v, while the increased content of methanol in the range of 0.1 to 0.3% v/v diminished the intensity of the sensor response. Differences in the composition of methanol and ethanol in the Pisco varieties studied (Italia and Quebranta) were correlated to the capacity of the composite-based sensors to differentiate them with greater performance. Sensors based on SnO2-TiO2-1/2 composites showed greater reproducibility in their response profile over time in comparison to SnO2-TiO2-1/1 and SnO2-MoO3 composites. The PCA showed that composite sensors were able to differentiate Pisco samples according to the varieties, Italia and Quebranta. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Application of Machine Learning Algorithms to Classify Peruvian Pisco Varieties Using an Electronic Nose(Multidisciplinary Digital Publishing Institute (MDPI), 2023-07-01)Pisco is an alcoholic beverage obtained from grape juice distillation. Considered the flagship drink of Peru, it is produced following strict and specific quality standards. In this work, sensing results for volatile compounds in pisco, obtained with an electronic nose, were analyzed through the application of machine learning algorithms for the differentiation of pisco varieties. This differentiation aids in verifying beverage quality, considering the parameters established in its Designation of Origin”. For signal processing, neural networks, multiclass support vector machines and random forest machine learning algorithms were implemented in MATLAB. In addition, data augmentation was performed using a proposed procedure based on interpolation–extrapolation. All algorithms trained with augmented data showed an increase in performance and more reliable predictions compared to those trained with raw data. From the comparison of these results, it was found that the best performance was achieved with neural networks.