Authors: Dhimas Dwinandha
Source: Institutional Repositories DataBase (IRDB) (article, 2023-09-01)
Field: Chemistry — Inorganic Chemistry

Elucidating transformation products and its formation mechanism during advanced oxidation processes of organic compounds by in silico and experimental approaches Advanced oxidation process (AOP) treatments, especially OH-based process, have been proven as an effective method to eliminate organics in the water while also being easy to implement at a low cost.However, recent studies suggested AOP treatments may not remove the organics completely-the organics are merely transformed into more toxic transformation products (TP).Thus, assessment of TPs from AOPs treatment of organic compounds are significantly needed, particularly assessment of transformation mechanism and resulting products.In this thesis, in silico and experimental methods were employed to give information about: (i) reactivity of organic compounds in OH-based process; (ii) transformation mechanism from parent compounds to their TPs and its key reaction pathways.In the first part of the research, a theoretical investigation using quantum chemical calculations was conducted to understand the OH-mediated oxidation mechanism of phenol.Different levels of theory were compared, and the most accurate predictions were obtained using the M06-2X level of theory.The study successfully predicted the reaction sites and rate constants for the formation of transformation products hydroquinone and catechol and demonstrates the potential of quantum chemical calculations for investigating the formation mechanisms of TPs in AOPs for organic compound removal in water environments.The second part of the research utilized isotopically labelled FT-ICR-MS, combined with machine learning techniques and paired mass distance (PMD) network analysis, to explore the reactivity and transformation of natural organic matter (NOM) during UV irradiation.The reactivity of different molecular formulas and structural categories was examined, with CHOS-formulas exhibiting the highest reactivity.The interpretable machine learning algorithm identified key features governing the reactivity, such as molecular weight, DBE-O, NOSC, and the presence of heteroatoms (N and S).Similarly, the last part investigates lignin reactivity and transformations as a model compound for natural organic matter during UV photolysis using FT-ICR-MS analysis, interpretable machine learning, and PMD analyses.The study reveals aliphatic hydroxyl groups as the most reactive functional groups in the initial stage of UV exposure, while phenolic hydroxyl groups were the most reactive in the later stage.Furthermore, hydroxylation and ketonization/quinone production were found as the primary transformation processes during the initial irradiation, while dealkylation and deformylation reactions were seen subsequently, indicating aromatic ring oxidation and cleavage.The findings contribute to understanding natural organic matter degradation in photo-oxidation systems.Overall, the outcomes of this study have significant implications for water treatment strategies, enabling the design of more efficient Advanced Oxidation Processes (AOPs) and providing insights into the dynamics of organic compound degradation in water systems.Furthermore, the findings contribute to the assessment of environmental transformation products, assisting in the mitigation of potential risks associated with the production of toxic substances in water.exposure ...... In silico ; Mechanism (biology) ; Transformation (genetics) ; Process (computing) ; Mechanism of action Synthesis and characterization of novel inorganic/organometallic compounds ; Inorganic and Organometallic Chemistry ; Mesoporous Materials and Catalysis