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Advanced Oxidation of PET-Derived Monomers Using Excimer Radiation and Hydrogen Peroxide: Kinetic and Operational Insights
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Excimer UV lamps combined with hydrogen peroxide achieved complete degradation of PET-derived monomers BHET and TPA in 30 and 80 minutes respectively, reducing chemical oxygen demand by up to 46%. This mercury-free advanced oxidation approach offers a promising pathway for treating wastewater contaminated with plastic-breakdown chemicals.
Growing environmental concern over plastic pollution has increased the need to address the persistence of PET-derived monomers, such as bis(2-hydroxyethyl) terephthalate (BHET) and terephthalic acid (TPA). This work examines the use of excimer radiation lamps combined with hydrogen peroxide (H2O2) to enhance advanced oxidation processes (AOPs) for their degradation. This approach stands out for its high selectivity, absence of mercury, and lower production of toxic byproducts. Experimental tests assessed how different operational factors affect pollutant degradation, such as the initial pollutant concentration (50–200 mg/L), the reaction volume (125–500 mL), and the H2O2:monomer mass ratio (0:1–6:1 for BHET and 0:1–4:1 for TPA). For BHET, the best results occurred with a 5:1 mass ratio, while TPA degraded optimally with a 3:1 ratio, with a 250 mL reaction volume and a 100 mg/L initial concentration for both compounds. Under these conditions, total degradation of the initial monomers was achieved in around 30 and 80 min for BHET and TPA, respectively, and at the end of the reaction, COD decreased by 46% and 32% relative to their initial values. In both cases, hydrogen peroxide was crucial since UV radiation alone led to much lower degradation efficiency. These results emphasize the need to optimize operational conditions for greater efficiency and establish a starting point for future use of excimer technology in the treatment of wastewater contaminated with PET and its derivatives. Additionally, the degradation data closely matched a pseudo-first-order kinetic model (R2 ≈ 1), confirming its reliability for predictive analysis, which is of high importance for the simulation and optimization of the process.
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