You are welcome to meet Dr. Christian Ebere Enyoh, MCSN, MCSJ, MYESS, MPAMPN, MIYCN

Dr. Christian Ebere ENYOH is a Research fellow on development of novel materials from Microplastics and Nanoplastics at Graduate School of Science and Engineering, Saitama University, Japan under the Japanese Society for the Promotion of Science (JSPS).

I graduated in September 2024 with a PhD in Environmental Science and Engineering, focused on plastics and waste research upcyling using a bottom-up approach. I’m currently a postdoctoral research fellow at Saitama University, Japan.

UPCOMING AND RECENT ACTIVITIES

RECEIVING MY DOCTORAL DEGREE AT SAITAMA UNIVERSITY.

MY Ph.D. Research was titled: Advanced Characterization, Machine Learning and Optimization of Waste Polyethylene Terephthalate Microplastics for the Removal of Emerging Pollutants from Wastewater in Single and Multicomponent Systems

(単一/多成分系の排水における廃PETマイクロプラスチック汚染物質除去の先端的な特性評価､機械学習と最適化) 

AuthorAID Environmental Biology, Chemistry and Toxicology Journal Club

on Febuary 23rd, 2024. The presentation is available on youtube. Please click here

keynote address at the 3rd Andean Congress of Engineering, Construction, Technology in Innovation (CAICTI).

Feburary 6-8, 2024 at Universidad Nacional De Chimborazo, Ecuador

PURCHASE ON AMAZON

SADNESS IS NOT A BAD THING. 

Environmental Health International Seminar 2023, to be held at the Health Polytechnic of Yogyakarta, Indonesia. August 27th, 2023

26-October, 2022 at Yogyakarta, Indonesia

CURRENT PROJECTS

2.     Use of Artificial Intelligence Techniques (Machine and Deep Learning Technique) in Environmental-Chemical Research

COMPLETED PROJECTS

Evaluation of nanoplastics toxicity to the human placenta in systems

Following the discovery of plastics in the human placenta, this study evaluated the toxicity of ten different nanoplastics (NPs) in the human placenta. Since the placenta performs metabolic and excretion functions by the enzymatic system, the NPs ...

Automated Classification of Undegraded and Aged Polyethylene Terephthalate Microplastics from ATR-FTIR Spectroscopy using Machine Learning Algorithms - Journal of Polymers and the EnvironmentAutomated analysis of microplastics is essential due to the labor-intensive, time-consuming, and error-prone nature of manual methods. Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy offers valuable molecular information about microplastic composition. However, efficient data analysis tools are required to effectively differentiate between various types of microplastics due to the large volume of spectral data generated by ATR-FTIR. In this study, we propose a machine learning (ML) approach utilizing ATR-FTIR spectroscopy data for accurate and efficient classification of undegraded and aged polyethylene terephthalate (PET) microplastics (MPs). We evaluate seven ML algorithms, including Random Forest (RF), Gradient Boosting (GB), Decision Tree (DT), k-Nearest Neighbors (k-NN), Logistic Regression (LR), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), to assess their performance. The models were optimized using fivefold cross-validation and evaluated using multiple metrics such as confusion matrix, accuracy, precision, recall (sensitivity), and F1-score. The experimental results demonstrate exceptional performance by RF, GB, DT, and k-NN models, achieving an accuracy of 99% in correctly classifying undegraded and aged PET MPs. The proposed approach capitalizes on the potential of ATR-FTIR spectra to discern distinct chemical signatures of undegraded and aged PET particles, enabling precise and reliable classification. Furthermore, the method offers the benefit of automating the classification process, streamlining the analysis of environmental samples. It also presents the advantage of providing an effective means for method standardization, facilitating more automated and optimized extraction of information from spectral data. The method’s versatility and potential for large-scale application make it a valuable contribution to the field of MP environmental research.

RECENT ACHEIVEMENT 

https://doi.org/10.1016/j.jhazmat.2023.132103 

https://doi.org/10.3390/environments10070130 

https://doi.org/10.1016/j.envpol.2023.122134 

https://doi.org/10.1016/j.jwpe.2023.103909

https://doi.org/10.1016/j.ces.2023.118917

CONNECT WITH ME