Quantitative structure-property relationship of the photoelectrochemical oxidation of phenolic pollutants at modified nanoporous titanium oxide using supervised machine learning
| Autor: | Jesse S. Dondapati, Aicheng Chen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Quantitative structure–activity relationship
Materials science Photoelectrochemical oxidation Chemical substance Loo General Physics and Astronomy 02 engineering and technology 010402 general chemistry Machine learning computer.software_genre 01 natural sciences chemistry.chemical_compound Rhodamine B Physical and Theoretical Chemistry business.industry Nanoporous 021001 nanoscience & nanotechnology 0104 chemical sciences Titanium oxide chemistry 13. Climate action Artificial intelligence 0210 nano-technology business Science technology and society computer |
| Zdroj: | Physical chemistry chemical physics : PCCP. 22(16) |
| ISSN: | 1463-9084 |
| Popis: | Here we report on an advanced photoelectrochemical (PEC) oxidation of 22 phenolic pollutants based on modified nanoporous TiO2, which was directly grown on a titanium substrate electrochemically. Their degradation rate constants were experimentally determined and their physicochemical properties were computaionally calculated. The quantitative structure–property relationship (QSPR) was elucidated by employing multiple linear regression (MLR) method. A supervised machine learning approach was employed to build QSPR models. The high predictive abilities of the QSPR model were validated via leave-one-out (LOO) method and a strict regimen of statistical validation tests. The significant descriptors identified in the QSPR Model for the phenolic compounds were also assessed using a typical dye pollutant Rhodamine B, further confirming the high effectiveness and predictability of the optimized model. Our study has shown that the integrated effect of the structural, hydrophobic and topological properties along with electronic property should be considered in order to design an efficient PEC catalytic approach for environmental applications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|