Sistemas nano híbridos termoresponsivos a base de tetronic e laponita e sua otimização através de uma nova abordagem de aprendizado de máquina
The purpose of the study is to describe the phase behavior of hybrid systems formed by copolymers of poly ethylene oxide and poly propylene oxide available in branched stellar blocks, available on the market under the brand name Tetronic®, and synthetic clays sold under the name Laponita®, in differ...
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| Formato: | bachelorThesis |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/50045 |
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| Resumo: | The purpose of the study is to describe the phase behavior of hybrid systems formed by copolymers of poly ethylene oxide and poly propylene oxide available in branched stellar blocks, available on the market under the brand name Tetronic®, and synthetic clays sold under the name Laponita®, in different values of concentrations, pH and temperature. Due to the restriction of information on these systems, a bibliographic review was carried out in the databases: PubMed, ScienceDirect and through the Capes journal portal, addressing three types of Tetronics® (Tetronic® 1304, Tetronic® 904 and Tetronic® 90R4) and three types of Laponita® (Laponita® RD, Laponita® XLG, Laponita® XL21). Literature data revealed that these substances are efficient in incorporating substances for controlled delivery. Next, hybrid systems based on Tetronic 904, Tetronic 90R4, Tetronic 1304 and Laponita® RD were prepared, in concentrations of 1 to 20% of polymer and 1.5 and 3% of clay. The changes in Tetronic® conformation and hydrophilicity were reflected through a temperature ramp, with variation from 25º to 80º, pH variation and in its different concentrations. When we manifested these variations, the hybrid systems felt different physical states, realizing that the insertion of Laponita® had an impact on the sol-gel transition. Finally, the data obtained with the Tetronic® 1304 and Laponita® RD systems were applied in a new machine learning (ML). These systems were inserted in support vector machines (SVM) and in the multilayer perceptron (MLP). Plants derived from this work will be very useful to optimize the performance of copolymers in different pharmaceutical applications and in drug delivery systems. |
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