Otimização do processo de monitorização de elétrodos seletivos de ião

• Autor(a) principal: Costa, Daniel João Cerqueira da
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.22/14899
• topic: Automation; Java; Research; Spring; Data; Raspberry Pi; GPIO; Cloud; Serverless; PostgreSQL; S3; BioMark

During the Ion-selective electrode research process, there are a lot of manual calibrations needed in order to achieve better and more effective results. The main goal of most of the researchers in these areas is about the material behaviours and reactions, but there is no way to test them without building the electrode and testing against various possibilities and mediums. Due to this, the research process usually cannot be a continuous process, since there are some interruptions for the manual process to validate all the previous developments. The human based calibration process is fundamental for many conclusions, but, most of the calibrations logic can be implemented in a machine that automates the process, collects the data, and generates the necessary output. The main goal of this MSc thesis is to optimize the calibration process building a calibration box that is fully configurable, and can be replicated, replacing the researcher manual steps during the membrane testing. Herein, it is possible to find the selected hardware and software architecture used, and also the business logic implemented to achieve the automation of these process. The architecture and implementation were designed to be able to work with a digital potentiometer (Crison GLP 21 pH Potentiometer) and a digital precise fluid pump (Legato 100). The solution contains four main components: • External Devices (Digital pH Meter, Digital Peristaltic Pump); • Server side web services and web application; • Cloud Based Deployment (Serverless, Storage, Database); • Hardware automation box; The implementation of this thesis uses the following technologies: • Code: Java, Spring Boot, Spring Data; • Data: JSON, Excel XML OpenFormat, PostgresSQL; • Hardware: RaspberryPi, Relay Boards, LCD Display, Button, LED; • I/O: Ethernet, USB to RS232, RCA, BNC; • Cloud: Heroku Serverless; Heroku PostgresSQL; Amazon S3 Storage; All the membranes used were made by BioMark researchers.