Enhancement of anodic TiO² nanotube arrays (TNTAs) production for photocatalytic CO² reduction / Md Arif Hossen

Faculty of Civil Engineering Technology

Thesis (Doctor of Philosophy in Civil Engineering) -- Universiti Malaysia Pahang Al Sultan Abdullah – 2024

Includes bibliographical references

Energy scarcity and atmospheric pollution are significant global challenges. Addressing the rising CO2 emissions is critical. Photocatalytic reduction of CO2, especially using TiO2 nanotubes (TNTAs) photocatalysts, offers a promising solution. However, the practical application of TiO2 is hindered by its wide band gap and fast recombination rate, necessitating modifications. The modification of TiO2 by morphological alteration using electrochemical anodization technique is one of the most cost-effective and widely used approach. This method allows to produce TNTAs of suitable morphology by adjusting anodization parameters. To achieve desired TNTAs morphology for improving CO2 photoreduction, it is crucial to optimize the anodizing conditions. However, conventional laboratory scale anodization reactor can produce TNTAs with very limited amounts. This study focuses on enhancing the production of TNTAs for efficient CO2 photoreduction by optimizing the anodization parameters and scaling up the anodization reactor. The study employed response surface methodology (RSM) to optimize the anodization parameters, achieving highly ordered TNTAs morphology. At optimized anodizing conditions, highly ordered nanotube morphology with precise control over diameter and length was achieved by two-step anodization. A scaled-up anodization reactor produced 250 times more TNTAs compared to the conventional reactor. This scale-up reactor can easily be applied to produce TNTAs on a commercial scale with necessary adjustments. The photocatalytic performance of the as-produced TNTAs, free-standing TNTAs, and modified-TNTAs was evaluated, showing superior CO2 reduction performance (especially those produced by two-step anodization and doped with g-C3N4). To predict the CO2 photoconversion rate, eight popular ML algorithms of regression, tree, kernel and neural network-based models were applied in this study. The five crucial photocatalytic parameters including catalyst exposed surface area, CO2 concentration, light intensity, irradiation time and feed pressure were chosen as input parameters. Compared to other ML models, ANN model showed outstanding rating of performance evaluation metrics, including R2, MSE, RMSE, and MRE at values of 0.993, 0.752, 0.867 and 0.882, respectively. These results demonstrated the robust and effective predictive capabilities of the ANN model. Sensitivity analysis identified catalyst exposed surface area is the most influential input parameter to enhance gas-phase CO2 photoconversion. These findings contribute to the design of more efficient photocatalytic systems for solar-based energy production and greenhouse gas reduction.