Task I. Visible Light-Induced Photoelectrochemical and UV-activated Degradation of BTA in CMP Wastewater (PI: Leem and Yoo): Azole compounds, including benzotriazole (BTA), are widely used in industrial and consumer applications due to their chemical stability and versatile functional properties. However, their persistence during conventional chemical–mechanical planarization (CMP) wastewater treatment presents a significant environmental concern. The aromatic heterocyclic structure and nitrogen substitutions of BTA impart strong resistance to microbial degradation and oxidative breakdown, resulting in its continuous release into aquatic environments. Numerous studies have reported the presence of BTA in CMP wastewater effluents, raising concerns regarding its environmental persistence and potential ecotoxicological impacts. To address this challenge, the PIs initially investigated the degradation of the hazardous organic compound BTA using a photoelectrochemical (PEC) process, a technology extensively studied for applications such as water splitting, hydrogen evolution, and alcohol oxidation. A WO₃//BiVO₄ electrode was employed as the photoanode for PEC degradation of BTA under visible-light irradiation. The process was further enhanced by incorporating chlorine radical sources (NaCl and HClO₄). Under one-sun illumination and an applied bias of 1.0 V (vs. Ag/AgCl) for 12 hours, approximately 90% of BTA was successfully degraded in the presence of chlorine radicals at room temperature. These results demonstrate that PEC-based oxidation is a promising strategy for the effective degradation of persistent organic contaminants in wastewater.
In addition, to elucidate the degradation mechanism and reactive species involved, the PIs conducted scavenger experiments to identify the reactive oxygen species (ROS) responsible for BTA degradation using UV-activated persulfate processes. The results showed that azole compounds were effectively degraded under UV-activated persulfate conditions, whereas treatment with UV irradiation or oxidants alone resulted in limited removal. Notably, BTA exhibited rapid degradation when peroxymonosulfate was combined with UV irradiation, achieving over 97% removal within 30 minutes. Overall, these findings highlight the superior efficiency and mechanistic advantages of UV-activated persulfate processes and PEC-based oxidation for the advanced treatment of azole-contaminated wastewater.
Task II. Development of Sustainable Corrosion Inhibitors for Cu CMP (PI: Seo): Project activities shifted beyond primary electrochemical evaluation toward computational interpretation and early-stage predictive analysis of corrosion inhibition mechanisms relevant to Cu CMP. Using the corrosion data generated previously as a foundation, density functional theory (DFT) studies were undertaken for a representative portion of the organic inhibitor library to quantify electronic properties linked to copper surface interactions. These calculations are helping to elucidate adsorption tendencies and clarify how functional groups containing heteroatoms influence inhibition behavior at the molecular level.
At the same time, a comprehensive set of structural and physicochemical molecular descriptors was constructed using RDKit, enabling integration of experimental results with theoretical features. This expanded dataset is currently being applied to train and assess several machine-learning models designed to estimate inhibition effectiveness. Initial model comparisons suggest that non-linear, ensemble-based methods-particularly tree-based algorithms such as XGBoost-provide more reliable predictions than linear regression approaches.
Continued evaluation of model outputs indicates that variables related to nitrogen and sulfur content, molecular aromaticity, and selected electronic descriptors exert a strong influence on inhibition performance, whereas molecules dominated by oxygen-containing groups tend to be associated with enhanced corrosion. These observations align with experimental trends and remain under active investigation. Collectively, the efforts during this reporting period emphasized building the computational and data-analytic infrastructure necessary for further model optimization and mechanistic insight in subsequent project phases. In addition, two review articles were published, a research manuscript was submitted by Murali, and student researchers presented posters at the CAMP CMP conference earlier this year.