Previous research summary

Summary of my research achievement and results is provided below:

From 2016, my research focused also involve environmental monitoring, specifically inorganic and organic pollutants, as well as microplastics, in various environmental samples. These studies aimed to provide insights into the environmental quality and potential risks to human health in specific regions of Nigeria. One significant study conducted during this period was the characterization and health risk assessment of microplastics and potentially toxic elements (PTEs) in garri, a popular staple food in West Africa. This research was pioneering, as it was the first study to evaluate microplastics in garri, shedding light on the presence of microplastics in food and their potential impact on human health. Another important study focused on the human health risk assessment of heavy metals in floodbasin soils in Owerri, Southeastern Nigeria providing valuable data for environmental management and public health protection. Additionally, my research provided the first report on the abundance, distribution, and composition of macrodebris and microplastics pollution, highlighting the growing environmental concern of plastic pollution in Nigeria. Lastly, as part of my undergraduate research project, I conducted a study on the physicochemical properties of palm oil and soil from Ihube Community, Okigwe, Imo State, Nigeria which information into the impact of human activities on the local environment.

My master's degree research in 2019-2021 focused on phytoremediation of simulated pesticides polluted water using Canna indica L., based on hydroponic technology. This research involved several key studies: I employed an Artificial Neural Network and Response Surface Design to model the competitive biosorption of Pentachlorophenol and 2,4,6-Trichlorophenol to Canna indica L. in Aquaponia. This approach allowed for a more comprehensive understanding of the biosorption process, aiding in the development of efficient remediation strategies. I also investigated the competitive biosorption and phytotoxicity of chlorophenols in aqueous solution to Canna indica L., providing insights into the plant's tolerance to these pollutants and its potential as a phytoremediation agent. Another important aspect of my research was the removal of Pentachlorophenol (PCP) and 2,4,6-Trichlorophenol (TCP) by the plant., where I conducted kinetics, isotherm, and thermodynamic studies to assess the efficiency of the plant in removing this pollutant. 

In recent my research (2021-2023), my research has focused on evaluating and developing predictive machine learning models for the degradation and toxicity of micro and nanoplastics to human health and the soil-plant system. This work encompassed several key studies: I evaluated the thermal degradation pathways of synthesized polyethylene terephthalate microplastics using kinetics and machine learning algorithms with non-isoconversional TGA data. This analysis provided insights into the thermal stability of PET MPs, aiding in material selection and processing strategies for more sustainable PET-based materials. Another study examined the impact of nanoplastics and PFAS exposure on immune functions by inhibiting Secretory Immunoglobulin A (SIgA) in human breast milk. Using molecular simulation and fractional factorial designs, this study assessed the toxicity of NPs and PFAS on breast milk, providing crucial information on their influence on infant immunity for the first time. Additionally, I evaluated the toxicity of nanoplastics to the human placenta, highlighting the potential risks to the developing fetus. This study demonstrated for the first time how nanoplastics could alter the metabolic processes of placental enzymes, jeopardizing placental function and fetal development. Other studies included virtual chemical analysis and machine learning-based prediction of polyethylene terephthalate nanoplastics toxicity on aquatic organisms, degradation of PET microplastics by mineral acids, and examination of the binding affinity of plastic monomers to a novel polyester hydrolase target using machine learning. Furthermore, I conducted toxicity evaluations of microplastics to aquatic organisms through molecular simulations and fractional factorial designs, providing valuable insights into the environmental impact of microplastics. Additionally, I conducted the first research on the effect of macro- and microplastics in soil on quantitative phytochemicals in different parts of juvenile lime trees, highlighting the impact of plastics on plant health and development. 

During my Ph.D. research (2021-2024), I focused on the reconversion of plastic waste into a cost-efficient material for the removal of emerging pollutants in water. This work involved several key studies: I optimized the removal of Ciprofloxacin and phenol from aqueous solutions using polyethylene terephthalate microplastics. Through a central composite design and machine learning, I achieved high removal efficiency for both pollutants simultaneously. Additionally, I developed and characterized polyethylene terephthalate microplastics for the removal of contaminant phenol and Ciprofloxacin from simulated wastewater. This study provided a detailed mechanism of the removal processes using both experimental and theoretical approaches, including quantum modeling, molecular dynamics, and grand canonical Monte Carlo simulations. Another important aspect of my research was the sorption of Per- and Polyfluoroalkyl Substances (PFAS) using Polyethylene (PE) microplastics as an adsorbent. Through Grand Canonical Monte Carlo and Molecular Dynamics studies, I investigated the effectiveness of PE microplastics in adsorbing PFAS, providing insights into potential applications for water treatment. Also, during my Ph.D. research (2023-2024), I recently developed a novel Artificial Intelligence/Machine Learning model for the automated classification of undegraded and aged Polyethylene Terephthalate (PET) Microplastics from Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) Spectroscopy. This research addresses the need for systems that can differentiate between microplastics in different states. Our approach utilizes unsupervised machine learning algorithms to automate the classification process, reducing the need for manual data labeling. This makes the method cost-effective and adaptable to various types of microplastic samples.

In summary, my overall research direction is expending toward material design for environmental engineering applications. I believe that I can make a useful contribution to the global society by facilitating the development of low-cost environmental engineering technology through the design of nanoscale materials which are critical components of many emerging environmental remediating systems.