Smart Plasma Degradation: pH, DFT & ML 🤖🧠⚡


 Non-thermal plasma (NTP) has emerged as a powerful advanced oxidation technology for degrading persistent organic contaminants ⚡. By generating highly reactive species such as •OH, O₃, and reactive nitrogen species, NTP enables rapid pollutant breakdown under ambient conditions. Experimental studies reveal that solution pH plays a crucial role in controlling reaction pathways and degradation  efficiency 🧪📊.


 

At different pH levels, the dominant reactive species and molecular interactions vary significantly . Density Functional Theory (DFT) analysis helps unravel how pH influences bond cleavage, electron transfer, and intermediate stability during plasma-driven reactions. These insights explain why acidic, neutral, or alkaline environments lead to distinct degradation mechanisms and by-product profiles 🧠.

To further optimize performance, machine learning models are integrated with experimental and DFT data . ML algorithms can predict degradation efficiency, identify key controlling parameters, and guide process optimization across pH ranges. This combined experimental–theoretical–data-driven approach accelerates the design of efficient plasma-based water treatment strategies for sustainable environmental remediation .

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