Project Description
Task I. Visible Induced Photoelectrochemical Degradation of BTA in CMP Wastewater (PI: Leem and Yoo): Significant progress was made during this period in the photoelectrochemical evaluation of degradation of benzotriazole, a common pollutant found in wastewater generated from CMP processes. A bismuth vanadate-coated tungsten(VI) oxide (WO₃//BiVO₄) electrode was successfully fabricated via a hydrothermal method and subsequently characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and UV-Vis spectroscopy as shown in Figure 1. The WO₃//BiVO₄ electrode was employed in a photoelectrochemical reaction mediated by chlorine radical (sources; NaCl and HClO4) under 1 sun illumination and an applied bias of 1 V(vs Ag/AgCl) for 12 hours. Under these conditions and in the presence of chlorine radical sources, approximately 90% of benzotriazole was successfully degraded. This degradation was achieved under milder conditions compared to conventional methods, highlighting the environmental friendliness and cost-effectiveness of the approach. To elucidate the degradation pathway, control experiments were performed both in the presence and absence of chlorine radical sources. Notably, significant degradation was observed only when chlorine radical sources was present, indicating that the photoelectrochemical activation of WO₃@BiVO₄ electrode in the presence of chlorine radical sources effectively generates reactive species chlorine radical. This mechanism not only facilitates the efficient degradation of benzotriazole but also demonstrates potential applicability to its various derivatives. This research was presented by Seongsu Park, Ph.D student, at the 3rd Healthy Water Solutions Conference held in Syracuse on May 22nd.
Task II. Development of Sustainable Corrosion Inhibitors for Cu CMP (PI: Seo): During the July–December period, the research progressed from initial experimental screening toward deeper mechanistic analysis and predictive modeling of corrosion inhibition behavior in Cu CMP environments. Building on the electrochemical dataset established in the previous period, density functional theory (DFT) calculations were initiated and carried out for a broad subset of the screened organic inhibitors to evaluate key electronic descriptors associated with inhibitor–Cu surface interactions. These calculations are providing molecular-level insight into adsorption behavior and the role of heteroatom-containing functional groups.
In parallel, molecular descriptors capturing structural and physicochemical features were generated using RDKit to complement the experimental and DFT data. This combined dataset is being used to develop and refine multiple machine-learning models aimed at predicting corrosion inhibition power. Preliminary results indicate that nonlinear models, particularly tree-based approaches such as XGBoost, show promising predictive capability compared to linear regression methods.
Ongoing analysis of model feature importance suggests that heteroatom content (especially nitrogen and sulfur), aromaticity, and selected electronic descriptors play key roles in inhibition performance, while oxygen-only functional groups often correlate with corrosion promotion. These trends are consistent with experimental observations and are currently being further validated. Overall, the work during this period focused on establishing the computational and data-driven framework that will support continued model refinement and mechanistic interpretation in the next phase of the project. Two review papers were published, a research paper was submitted by Murali, and our students presented their posters at the CAMP CMP conference earlier this year.