DotAr a microfluidic platform for the automation of molecular reactions (e.g., LAMP) aiming to autonomously detect, quantify, monitor, and prevent airborne pathogenic microorganisms in crops and in real-time

• Grant number: 22/10708-0
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: February 01, 2023
• End date: January 31, 2025
• Field of knowledge: Biological Sciences - Genetics - Molecular Genetics and Genetics of Microorganisms
• Principal Investigator: Samuel Matos da Costa
• Grantee: Samuel Matos da Costa
• Company: Doroth Indústria, Comércio e Serviços de Informação Ltda
• CNAE: Atividades de apoio à agricultura ; Desenvolvimento de programas de computador sob encomenda
• Principal investigators: João Paulo de Campos da Costa
• Associated researchers: Luana Parras Meleiro Garcia ; Rodrigo Henrique Gounella
• Associated research grant: 18/01143-3 - Microfluidics platform to obtain genetic information and predict genetic profile of individual patients for medical doctors and hospitals, based on a fast, integrated DNA treatment, sequencing, and data analysis solution in personalized medicine, AP.PIPE
• Associated research grant(s): 23/01608-4 - Dot-air: optimization of molecular reactions (e.g. LAMP) aiming at the detection, quantification, monitoring, and automatic prevention, and in real time, of pathogenic microorganisms of area dispersion in crops shipped in a micro-fluidic platform, AP.R
• Associated scholarship(s): 23/14098-4 - Integrated design of accessory hardware through a single microcontroller, firmware development, final state machine with radio monitoring communication system, self-diagnosis, and receiving firmware updates., BP.TT; 23/01883-5 - Improvement of protocols focused on DNA extraction in an automated environment and integration with accessory devices, BP.TT

Abstract

Through an innovative microfluidic chip technology, DotAr aims to develop an autonomous device that can be placed directly on farms to identify the presence and quantify airborne pathogenic microorganisms in real-time. Besides, our technology aims to reveal which variant species are present and what frequency over the entire pathogen population they occur, generating information of excellent decision value for farmers who could determine when and which fungicides (or other means of protection) to employ in crops (AU)