Design & Prototype for a Microplastics Data Logging System

This projects main aim is to design and build an IoT system for microplastics detection and characterization that addresses the existing limitations for in-situ monitoring. To expand our product offering with a new hardware, we need insights into how we can redesign our existing popular microplastics analytics platform, MPConnect , to incorporate state of the art data logging capabilities with a smart sensor. AbbaTek aims to enhance its impact on environmental sustainability and advance its mission of innovative solutions for a healthier planet.

Smart Microplastics Sensor Development

The project focuses on developing a quantum powered sensor to monitor microplastic particles beginning in aquatic environments. These environments are often challenging to study due to their complexity and the time-consuming nature of traditional methods. The goal is to create a sensor that can perform both chemical and physical characterization of microplastic particles. This will be achieved through a field-based, flow-through system or a fully-submersible in-situ sensor. The ultimate goal is to help standardize in-situ analysis, providing greater control in multi-sample studies. Additionally, the sensor will export data in standardized formats to the MPConnect analytics platform, enhancing accessibility and insights for decision-making and life cycle assessments. An intuitive user interface will be developed to simplify the sampling process, making it user-friendly and efficient.

Sustainable Polymers Enabled by Simulation Research and Emerging Data Analytics

A transformative new wave of AI innovation is reshaping how we solve complex physical challenges. AI simulation using Large Quantitative Models (LQMs) enables researchers to model intricate real-world systems at the molecular and atomic levels, creating digital twins that can simulate chemical reactions and material properties with unprecedented precision. We would like to collaborate with engineers to conduct our simulations. This will involve several different steps: Familiarizing themselves with our research project and objectives. Familiarizing themselves with our LQMs simulation and modelling. Running simulations and comparing results to experimental data. Tuning simulation parameters to better match experimental conditions. Reporting the results of simulations. Bonus steps in the process would also include: Researching other simulations and models to compare our simulation against.