🧪📊 Decoding pH Effects in Plasma-Driven Organic Degradation

Degradation of Organic Contaminants by Non-Thermal Plasma ⚡🧪 is emerging as a powerful and eco-friendly strategy for water and wastewater treatment. Non-thermal plasma generates highly reactive species such as •OH radicals, O₃, and excited electrons that can rapidly break down persistent organic pollutants. Unlike conventional methods, this approach operates under ambient conditions and minimizes secondary pollution, making it attractive for sustainable environmental remediation 🌍💧.


 

A key factor influencing plasma-driven degradation is pH, which controls the formation, lifetime, and reactivity of plasma-generated species 🔬⚖️. Experimental observations reveal that acidic, neutral, and alkaline conditions lead to distinct degradation pathways and efficiencies. By integrating density functional theory (DFT) calculations, researchers can explain how pH alters molecular orbitals, bond dissociation energies, and reaction sites of contaminants, offering atom-level insight into plasma–pollutant interactions 🧠⚛️.

To further enhance understanding, machine learning (ML) models are applied to correlate experimental parameters, theoretical descriptors, and degradation performance 🤖📊. ML helps predict optimal pH conditions, identify dominant reaction mechanisms, and accelerate process optimization without exhaustive experimentation. Together, non-thermal plasma, DFT analysis, and ML form a smart, data-driven framework for designing next-generation advanced oxidation technologies 🚀♻️.

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