This call seeks to fund engineering research in Kenyan universities or research institutions that leverage advances in AI and exploit both the computational and LoRaWAN infrastructure installed by KENET. In addition, this call seeks to support early-career PhD faculty and/or researchers in advancing emerging areas of Artificial Intelligence through work that combines technical depth with demonstrable, real-world implementation.
The Computational Modeling and Materials Science is a Special Interest Group supported by KENET to enhance research capacity development in the use of computational modeling in science and technology. Research work is expected to involve university faculty and graduate students in Chemistry, Physics and Environmental Sciences but strongly welcomes those from other basic and applied sciences.
In 2022, Dr. Ian Kaniu a Senior Lecturer at University of Nairobi was awarded the Research and Innovation Grant by KENET in the Special Interest Group (SIG) for Computational Modelling and Materials Science (CMMS). The purpose of the grant was to lead the efforts in developing an on-site detection method for chlorothalonil in fresh vegetable /farm produce.
The increasing use of pesticides in modern agriculture has raised serious concerns about food safety, environmental sustainability, and public health. Chlorothalonil, a widely used pesticide to control fungal diseases in crops, has been linked to health risks, including cancer. Dr. Kaniu's research aims to provide an innovative, cost-effective, and non-destructive solution for detecting pesticide residues using unique spectral fingerprints and machine learning models for rapid pesticide screening.
Pesticide residues in food pose significant health risks, particularly in developing countries where regulatory enforcement may be weak. In Kenya, for example, recent studies have linked prolonged exposure to chlorothalonil with increased risks of cancer, endocrine disruption, and organ toxicity. Despite these concerns, conventional detection methods remain largely laboratory-based, time-consuming, and expensive. The lack of accessible screening tools has resulted in poor regulatory compliance and undetected exposure to harmful pesticide levels.
