Arylsulfonamide chalcones as alternatives for fuel additives: antioxidant activity and machine learning protocol studies
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2023
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Biodiesel is a promising fuel with the potential to reduce some negative aspects of fossil fuels, such as the emission of pollutants and greenhouse gases (GHGs), the scarcity of natural resources and market instability. To amplify its low durability and stability the use of technologies based on molecular compounds that reduce the oxidation rate of biodiesel and preserve its physical–chemical properties is very common and necessary throughout the world. To reduce oxidative stability problems of diesel–biodiesel blends, arylsulfonamide chalcones were evaluated as potential additives for B20 blends. In this study, comprehensive structural, computational, and experimental analyses were undertaken to understand the antioxidant potential of these compounds as possible additives. The supramolecular arrangements were stabilized by weak molecular interactions (C–H⋯O and C–H⋯π), which are related to antioxidant and antibacterial action, and groups can act as electron-donating substituents. The energy range of 593.1–570.2 kJ mol−1 in the frontier molecular orbitals indicates high structural stability, due particularly to sulfonamide groups which enable electrophilic attack. Furthermore, the Fukui function aligned with kinetics parameters, obtained using machine learning protocols, provided information to clarify and expand the comprehension of chalcone antioxidant features mediated by free-radical capture. The heat of combustion indicated good energy availability (6530.5–7306.5 kcal kg−1), close to those of conventional fuels. In addition, the oxidative stability of the diesel–biodiesel blend (B20) remained at around 27 hours, after 140 days of storage, which is better than for some commercial additives. We hope that this comprehensive study will support the understanding of chalcone-based compounds as alternatives to fuel additives.
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DUARTE, Vitor S. et al. Arylsulfonamide chalcones as alternatives for fuel additives: antioxidant activity and machine learning protocol studies. New Journal of Chemistry, London, v. 47, n. 21, p. 10003-10015, 2023. DOI: 10.1039/D3NJ00255A. Disponível em: https://pubs.rsc.org/en/content/articlelanding/2023/NJ/D3NJ00255A. Acesso em: 17 ago. 2023.