🤖 Plasma-Based Pollutant Degradation via DFT & ML

 ⚡ Non-thermal plasma (NTP) has emerged as a powerful and eco-friendly technology for degrading persistent organic contaminants in water and air. Unlike thermal processes, NTP generates highly reactive species such as •OH, O₃, and reactive nitrogen species at ambient conditions, enabling rapid pollutant breakdown without secondary pollution . Experimental studies show that plasma treatment efficiently disrupts complex molecular structures, making it ideal for treating pharmaceuticals, dyes, and industrial pollutants.


pH plays a critical role in controlling degradation pathways during plasma treatment. Under acidic, neutral, or alkaline conditions, the formation and dominance of reactive species vary, directly influencing reaction kinetics and intermediate formation . Density Functional Theory (DFT) analysis helps reveal how protonation states and molecular orbital distributions change with pH, explaining why certain bonds become more vulnerable to plasma-induced attack ⚛️.

🤖 Machine learning (ML) further enhances understanding by correlating experimental parameters, DFT descriptors, and degradation efficiencies . ML models can predict optimal pH conditions, energy input, and reaction outcomes, reducing experimental time and cost . Together, experiments, theoretical insights, and data-driven modeling form a robust framework for designing next-generation plasma-based advanced oxidation processes for sustainable environmental remediation 🌍✨.

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