Licenciatura em Química (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/26
Siglas das Coleções:
APP - Artigo Publicado em Periódico
TAE - Trabalho Apresentado em Evento
TCC - Trabalho de Conclusão de Curso
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Resultados da Pesquisa
Item Cálculos de química quântica para funções orgânicas(2022-10-05) Silva, Yuri Antonio Fortunato da; Bastos, Cristiano Costa; http://lattes.cnpq.br/6385190604693576; http://lattes.cnpq.br/9340189446066073Organic-physical chemistry is an area that can contribute to the development of new compounds and the understanding of chemical reactions and processes based on quantum chemistry calculations. One of the concepts addressed in this area is the structure-activity relationship of a functional group, an atom or grouping of atoms connected in a specific way in a molecule, providing reactivity and specific chemical and physical properties. Based on this, this work has the general objective of classifying organic functions through intrinsic properties of functional groups with the increase of the carbonic structure. With the aid of molecular computational models, properties were obtained for the following organic functions, in an attempt to classify them: alkanes; alkenes; alkynes; carboxylic acids; aldehydes; ketones; esters; amines and amides. Some of the properties calculated were: Total electronic energy; molar volume; Energy of the HOMO and LUMO orbitals; gap HOMO-LUMO; Kinetic energy of the HOMO and LUMO orbitals; Ionization Energy; Electroaffinity and Electronegativity. The Ionization Energy properties; Electroaffinity and Electronegativity were obtained indirectly from other properties. It was observed that some properties are more satisfactory in the classification of some organic functions. Furthermore, it was possible to establish mathematical relationships that show a pattern for different organic functions: alkanes, carboxylic acids, esters, aldehydes, ketones and amides. However, for alkenes, alkynes and amines another pattern of behavior was noted. Furthermore, the classification of organic functions is more suitable using the energies of the HOMO and LUMO orbitals. In addition, other properties are also good candidates, such as Electronic Energy, Ionization Energy, Electroaffinity and Electronegativity. Finally, alkenes, alkynes and amines had a greater prominence due to having unique behaviors for many calculated properties.Item Obtenção de modelos QSAR na plataforma livre de computação em nuvem OCHEM e uma aplicação para atividade acaricida de monoterpenos(2022-06-08) Silva, Esterfania Laís da; Batista, Hélcio José; http://lattes.cnpq.br/1234630357325796; http://lattes.cnpq.br/0889940460113959The present work focuses in using the resources of the free access cloud computing web platform OCHEM - English Online Chemical Modeling Environment, designed for data storage, development of models and publication of chemical information. As an example of application on the platform, we sought to obtain QSAR models - Quantita-tive Structure-Activity Relation for insecticidal activity on the species of Spider Mite (Tetranychus Urticae), a common pest in family farming in the Northeast region of Bra-zil, from a series of compounds of the monoterpene class: α-pinene, p-cymene, β-pinene, menthol, α-terpineol, geraniol, R-limonene, 1-8-cineole, R-linalool, terpenyl ac-etate, and terpinolene and 4- (S) ter-pineol. To this end, we carried out a literature review on the monoterpenes usage in agriculture pest control, as well as a review of the Quantitative Structure-Activity Relation-ship method, QSAR. For the application of the OCHEM platform to obtain QSAR models for the series of monoterpenes, we per-formed the molecular modeling of the series compounds by methods of Quantum Chemistry, generating the molecular structures in order to feed the OCHEM platform, to calculate the molecular descriptors and obtain of QSAR models. We explored differ-ent sets of descriptors as well as different types of machine learning methods, such as Multiple Linear Regression (MLR), Partial Least Squares (PLS) and Neural Networks (NN - Neural Networks). The models so obtained for the series under study, however, did not have sufficient statistical quality, probably due, firstly, to the small number of compounds in the training set with available experimental data and, also, to a likely strong degree of non-linearity present in the relationship between the chosen de-scriptors and the activity.