Degradation of water pollutants

Per- and polyfluoroalkyl substances (PFASs) are synthetic chemicals that are harmful to both the environment and human health. They possess one of the strongest and most stable bonds in organic chemistry, namely, the carbon-fluorine (C–F) bond. The high stability of this bond, which has enabled several technological advancements in the past century, now poses a serious health hazard.

In the last century, the industrial sector has harnessed the intrinsic strength of the C–F bond to incorporate PFASs in a wide variety of consumer and industrial goods. Specifically, PFASs are regularly used in the electronics, automotive, and aviation industries; several consumer goods, including non-stick cookware, clothing, carpets, shampoos, cleaning agents, and adhesives contain these persistent pollutants. The widespread usage of these anthropogenic compounds has contaminated water resources in India, the US, and many other countries. Most importantly, the consumption of this contaminated water leads to the bio-accumulation of these compounds in various organisms, including humans.

The intrinsic strength of the C–F bond in PFASs prevents most organisms from dissociating these compounds through natural means, which facilitates their bio-accumulation and toxicity. Thus, the intrinsic strength of the C–F bond poses a severe threat to many forms of life, making the treatment of these contaminated water sources essential. In our group, we examine these PFAS compounds using novel quantum mechanical techniques and suggest efficient ways to degrade these harmful pollutants.

Relevant Publications


  1. Sharma SRKC Yamijala, Ravindra Shinde, Kota Hanasaki, Zulfikhar A Ali, and Bryan M Wong. Photo-induced degradation of PFASs: Excited-state mechanisms from real-time time-dependent density functional theory. Journal of Hazardous Materials 423, 127026, (2021). Full text

  2. Sharma SRKC Yamijala, Ravindra Shinde, and Bryan M Wong. Real-time degradation dynamics of hydrated per- and polyfluoroalkyl substances (PFASs) in the presence of excess electrons. Physical Chemistry Chemical Physics 22 (13), 6804-6808, (2020). Full text

  3. Akber Raza, Sharmistha Bardhan, Lihua Xu, Sharma SRKC Yamijala, Chao Lian, Hyuna Kwon, Bryan M Wong. A machine learning approach for predicting defluorination of per-and polyfluoroalkyl substances (PFAS) for their efficient treatment and removal. Environmental Science & Technology Letters, 6 (10), 624-629, (2019). Full text