Substituted naphthalene reaction rates with peroxy-acid treatment: prediction of reactivity using PEST

ABSTRACT

Persistent organic contaminants in the environment pose an environmental risk due to widespread occurrence and toxic properties. Advanced oxidation processes (AOPs) are treatment methods that have been used to successfully degrade organic contaminants in water, soil, sediments and sludge. Reaction rate constants (k) for peroxy acid treatment of 10 substituted naphthalene compounds were determined. The treatment method utilized acetic acid, hydrogen peroxide and a sulphuric acid catalyst to degrade the polyaromatic structures found in the compounds. Molecular structures of the selected compounds were derived at the B3LYP/6-31G* level of theory. Property-encoded surface translator (PEST) descriptors were calculated from B3LYP/6-31G* optimized structures and were found to have significant levels of correlation with k. Models using minimum local ionization potential (PIP.MIN) and a histogram [bin] of the gradient of the K electronic kinetic energy normal to the isosurface (DKN) were evaluated and found to agree within 10% of experimentally derived values of k in most instances. Results show that a combination of PEST descriptors could be used to predict reactivity by the peroxy-acid process. The PEST technology could prove to be a valuable asset for effective remediation design by predicting reaction outcomes for substituted naphthalene compounds and possibly other hydrophobic organic compounds (HOCs).

KEYWORDS:

• AOP
• naphthalenes
• local IP
• remediation
• PEST

Supplemental Material

Supplementary material for this article can be accessed at: https://doi.org/10.1080/1062936X.2019.1579755 .

Acknowledgments

The authors gratefully thank the US NSF Career Award to MCN (BES-0093191) for the GC-FID used in this investigation. The authors would also like to acknowledge RPI’s internal support to JMS and ELA, and Mr Krein and Ms Morkowchuk for helpful discussions, PEST software instruction and help with molecular modelling questions. We would also like to thank the author of the PEST program, Dr Matt Sundling, and the Center of Biotechnology and Interdisciplinary Studies (CBIS) at RPI.

Disclosure statement

No potential conflict of interest was reported by the authors.