Rhoda Oyeladun Adegoke

Bio: Rhoda Oyeladun Adegoke is an academic researcher from Ladoke Akintola University of Technology. The author has contributed to research in topics: Chemistry & Adsorption. The author has an hindex of 3, co-authored 3 publications receiving 26 citations.

Clean technology for sequestering Rhodamine B dye on modified mango pod using artificial intelligence techniques

Abstract: In this study, an acid-modified mango pod (AMMP) was prepared as adsorbent for removal of Rhodamine dye and the adsorption uptake was also determined using machine learning algorithms namely, Artificial Neural network (ANN) and Adaptive neuro-fuzzy inference systems (ANFIS) respectively. The prepared adsorbent was characterized with SEM, FTIR, EDX, and PXRD techniques. Surface chemistry revealed the presence of O–H stretching of free hydroxyls and alcohols and phenols, C–H stretch of alkanes, C≡C stretch of alkynes, CO stretch of ketones, lactones, and carboxylic anhydrides, –CC- stretching of alkenes, C–N stretch of aliphatic amines and O–H bend of carboxylic acids. Surface morphologies of the AMMP revealed well-developed and open porous surfaces needed for an efficient adsorption of the dye molecule. EDX analysis showed an increase in the carbon contents from 79.94% (raw) to 80.12% (AMMP) by weight and 86.89% (raw) to 89.11% (AMMP) by atom. The crystallinity structure revealed the new and intense peak formations and the presence of ordered (organized) crystalline structures on AMMP. Optimal ANN architectures, activation functions and training algorithms were selected after several stimulations with different network parameters while the ANFIS model was tested with three clustering approaches namely, Grid partitioning, fuzzy c-means, and subtractive clustering to carry out the extensive studies to optimally predict the adsorption efficiency/capacity of Rhodamine dye onto AMMP. The performance of the developed models was evaluated using the following statistical metrics; the optimal ANN model gave RMSE ​= ​10.422, MAD ​= ​3.673, MAPE ​= ​5.409, R2 ​= ​0.977 while with optimal ANFIS model gave a RMSE ​= ​9.246, MAD ​= ​4.938, MAPE ​= ​4.672, R2 ​= ​0.984 ​at the testing phase. The lower value of the statistical parameters indicates better performance in both models. Batch adsorption studies gave qmax of 456.67 ​mg/g, 389.51 ​mg/g, and 410.56 ​mg/g for the experimental data, ANN and ANFIS models respectively thus, suggesting their good correlations. Technoeconomic aspects of the study present that AMMP is approximately 8 times cheaper, translating to saving cost of 225.2 USD/kg when compared with 259.5 USD/kg for commercial activated carbon.

Gas diffusion electrodes (GDEs) for electrochemical reduction of carbon dioxide, carbon monoxide, and dinitrogen to value-added products: a review

Abstract: Electrochemical reduction of gaseous feeds such as CO2, CO, and N2 holds promise for sustainable energy and chemical production. Practical application of this technology is impeded by slow mass transport of the sparingly soluble gases to conventional planar electrodes. Gas diffusion electrodes (GDEs) maintain a high gas concentration in the vicinity of the catalyst and improve mass transport, thereby resulting in current densities higher by orders of magnitude. However, gaseous feeds cause changes to the GDE environment, and specific features are required to efficiently tune the product selectivity and improve reaction stability. Herein, with a comprehensive review of the challenges and advances in GDE development for various electrocatalytic reactions, we intend to complement the body of material-focused reviews. This review outlines GDE fundamentals and highlights key advantages of GDE over conventional electrodes. Through critical discussion about steps in GDE fabrication, and specific shortcomings and remedial strategies for various electrochemical applications, this review discusses connections, unique design criteria, and potential opportunities for gas-fed reactions and desired products. Finally, priorities for future studies are suggested, to support the advancement and scale-up of GDE-based electrochemical technologies.

Factors Limiting Li + Charge Transfer Kinetics in Li-Ion Batteries

Abstract: Understanding the factors limiting Li+ charge transfer kinetics in Li-ion batteries is essential in improving the rate performance, especially at lower temperatures. The Li+ charge transfer process involved in the lithium intercalation of graphite anode includes the step of de-solvation of the solvated Li+ in the liquid electrolyte and the step of transport of Li+ in the preformed solid electrolyte interphase (SEI) on electrodes until the Li+ accepts an electron at the electrode and becomes a Li in the electrode. Whether the de-solvation process or the Li+ transport through the SEI is a limiting step depends on the nature of the interphases at the electrode and electrolyte interfaces. Several examples involving the electrode materials such as graphite, lithium titanate (LTO), lithium iron phosphate (LFP), lithium nickel cobalt aluminum oxide (NCA) and solid Li+ conductor such as lithium lanthanum titanate or Li-Al-Ti-phosphate are reviewed and discussed to clarify the conditions at which either the de-solvation or the transport of Li+ in SEI is dominating and how the electrolyte components affect the activation energy of Li+ charge transfer kinetics. How the electrolyte additives impact the Li+ charge transfer kinetics at both the anode and the cathode has been examined at the same time in 3-electrode full cells. The resulting impact on Li+ charge transfer resistance, Rct, and activation energy, Ea, at both electrodes are reported and discussed.