Nano Track: sistema inteligente de gerenciamento de dados de síntese de nanoestruturas

• Ano de defesa: 2013
• Autor(a) principal: Andre Sousa Figueiredo Silva
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: LIMS; Ambiente de simulação; Nanociência; Programação Genética; Rede Neural Articial; Nanotecnologia; Nanoinformática; Workow; Computação; Inteligência computacional
• Link de acesso: http://hdl.handle.net/1843/ESBF-97HF63

The interest in Nanoscience and Nanotechnology has increased due to the possibility of developing new materials with improved and innovative properties. According to the scientists, Nanotechnology will be able to change the nature of almost everything that has been done by humans, generating an impact in areas such as medicine, engineering, telecommunications, energy, computing, etc. However, Nanotechnology lacks computational tools that support the researcher in his research activities. Usually, the tasks involved in the development of nanostructures are not simple to be executed. Several technological and economic factors contribute to this complexity. For the manufacture of any compound, the knowledge about the material that is being built is essential. For nanosized compounds there is no difference and one should have knowledge about the nanosized compound. However, obtaining the required data is difficult due to the precariousness with which information of the experiments are stored and also due to restriction about the data from other groups. Because of this, the knowledge discovery by the researcher is restricted, and this is something that hampers the development of innovative nanomaterials. The LIMS (Laboratory Information Management Systems) are systems developed to facilitate the storage and management of data from laboratories and it is already being widely used in other areas. Based on the Flux platform, we developed the NanoTrack tool, that is a LIMS for Nanotechnology support. In addition to the management, the tool provides a simulation environment that supports researchers with knowledge discovery. This environment consists of plugins that are incorporated into the tool. These plugins are composed by inference and optimization algorithms, based on Artificial Neural Networks and Genetic Algorithms, respectively. This work presents three developed case studies of the Nanotechnology laboratories: Quantum Dots, Semiconductor Laboratory and Carbon Nanotubes. The results obtained demonstrate the tool power in the data management and its computer algorithms capacity for discovering important parameters for the synthesis of nanostructures and nanodevices.