A machine learning–based microorganism classification and a novel synthesis of stabilized tetragonal and cubic ZrO₂ nanoparticles: A future carrier for antimicrobial drug delivery
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A supervised machine learning model has been used to classify eight representative microorganisms—Amoeba, Euglena, Hydra, Paramecium, rod-shaped bacteria, spherical bacteria, spiral bacteria, and yeast—with 98% accuracy and high F1-scores (0.98–0.99). After successful classification of the microorganisms, a nontoxic nanosystem of stabilized tetragonal and cubic ZrO₂ have been developed that can be used as potent antimicrobial agent and a biocompatible nanocarrier for antimicrobial drug delivery. To develop such potent nanosystem, a fully transparent colorless Al 3+ modified ZrO(OH) 2 ⋅xH 2 O gel has been developed by a chemical method of Al-metal surface hydrolysis in aqueous solution of zirconium oxychloride at room temperature. After that, Al 2 O 3 (Al 2 O 3 content ≤ 5 mol %) stabilized ZrO 2 nanoparticles have been synthesized in a metastable t-ZrO 2 phase by a reconstructive thermal decomposition of this amorphous hydroxyl transparent gel in air at temperature 500 °C. The t-phase of ZrO 2 changes to c-phase with the increase in Al 2 O 3 content (Al 2 O 3 content ≥ 10 mol %). The phase formation in Al 2 O 3 stabilized metastable t- or c-ZrO 2 nanoparticles has been revealed by X-ray diffraction study of the specimen before and after annealing at temperature 500 °C. No independent Al 2 O 3 recrystallizes in a perfect Al 2 O 3 : ZrO 2 solid solution. This present work may show a new perspective in both the classification of the microorganisms and development of a new biocompatible nanosystem that can be used as antimicrobial agent and in antimicrobial drug delivery. Future work will focus on detailed antimicrobial and drug targeting studies of this nanosystem.
