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    Impact of c- and m-sapphire plane orientations on the structural and electrical properties of β-Ga2O3 thin films grown by metal-organic chemical vapor deposition
    (Institute of Physics, 2024-12-13)
    This work presents a comprehensive investigation into the structural and electrical properties of Ga2O3 thin films grown via metal-organic chemical vapor deposition on both c- and m-plane sapphire substrates. Structural characterization revealed the β-Ga2O3 phase formation in both substrate orientations, with strong epitaxial ( 2 ¯ 01 ) preferential growth on c-plane substrates and polycrystalline films on m-plane substrates. Results show that Ga2O3/m-sapphire exhibits the lower electrical resistivity than its counterpart grown on c-sapphire. Activation energies of acceptor levels were estimated at ~1.4 eV and ~0.7 eV , for Ga2O3 films grown on c- and m-plane, respectively. This result shows that growing Ga2O3 on m-plane sapphire is beneficial to reach a weakly compensated sample. Cathodoluminescence analysis suggests that the additional low activation energy of ~0.18 eV observed in Ga2O3 grown with the highest oxygen flow on m-plane sapphire can be associated to thermally-induced migration of self-trapped hole states.
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    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.