Bridging Dissolved Organic Matter Reactivity to Ozonation Catalysts for Cu@Al₂O₃ from the Molecular Level by Machine...

Area of Science:

• Environmental Chemistry
• Advanced Oxidation Processes
• Catalysis

Background:

• Catalytic ozonation is crucial for treating refractory organic wastewater.
• Dissolved organic matter (DOM) variability complicates reaction mechanisms.
• Designing wastewater-specific catalysts remains a significant challenge.

Purpose of the Study:

• To develop a data-driven strategy for linking wastewater characteristics to catalyst design.
• To optimize catalytic ozonation for refractory organic wastewater treatment.
• To understand the molecular-level mechanisms of pollutant degradation.

Main Methods:

• Integrated principal component analysis and correlation analysis.
• Utilized Cu@Al2O3 catalyst for catalytic ozonation of three refractory wastewaters.
• Employed Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) and random forest modeling for molecular analysis.
• Statistical modeling identified UV absorbance at 254 nm (UV254) as a key wastewater characterization surrogate.

Main Results:

• Total organic carbon removal efficiency varied significantly (19.1%-58.6%) across different wastewaters.
• FT-ICR-MS revealed molecular heterogeneity; random forest model achieved 67.3%-80.4% accuracy in classifying molecules.
• Removed molecules were predominantly aromatic, heteroatom-rich, and high molecular weight (>400 Da).
• Oxygen vacancy concentration strongly correlated with CHOS compound removal (r = 0.998) but hindered fluorescence region V degradation (r < -0.997).

Conclusions:

• A data-driven strategy effectively bridges molecular DOM profiling and catalyst descriptors.
• This approach guides the rational design of ozonation catalysts for targeted wastewater treatment.
• UV254 serves as a robust surrogate for wastewater characterization in optimizing catalytic ozonation.