Quantitative structure–property relationship of the photoelectrochemical oxidation of phenolic pollutants at modified nanoporous titanium oxide using supervised machine learning

2020 ◽  
Vol 22 (16) ◽  
pp. 8878-8888 ◽  
Author(s):  
Jesse S. Dondapati ◽  
Aicheng Chen
Keyword(s):  
Machine Learning ◽  
Titanium Oxide ◽  
Structural Properties ◽  
Supervised Machine Learning ◽  
Quantitative Structure ◽  
Structure Property ◽  
Phenolic Pollutants ◽  
Photoelectrochemical Oxidation ◽  
Structure Property Relationship ◽  
Relationship Of

The effects of intrinsic structural properties on the photoelectrochemical oxidation of phenolic pollutants at nanoporous TiO2 are systemically studied.

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