Patel, Harsh V, Green, Jazmin, Park, Hyoshin, Luster-Teasley_Pass, Stephanie, and Zhao, Renzun. A Hybrid Response Surface Methodology and Machine Learning Framework for Quantifying Effects of Physicochemical Parameters on PFAS Distribution. Retrieved from https://par.nsf.gov/biblio/10705303. ACS ES&T Water 6.5 Web. doi:10.1021/acsestwater.5c01162.

Patel, Harsh V, Green, Jazmin, Park, Hyoshin, Luster-Teasley_Pass, Stephanie, & Zhao, Renzun. A Hybrid Response Surface Methodology and Machine Learning Framework for Quantifying Effects of Physicochemical Parameters on PFAS Distribution. ACS ES&T Water, 6 (5). Retrieved from https://par.nsf.gov/biblio/10705303. https://doi.org/10.1021/acsestwater.5c01162

Patel, Harsh V, Green, Jazmin, Park, Hyoshin, Luster-Teasley_Pass, Stephanie, and Zhao, Renzun. "A Hybrid Response Surface Methodology and Machine Learning Framework for Quantifying Effects of Physicochemical Parameters on PFAS Distribution". ACS ES&T Water 6 (5). Country unknown/Code not available: ACS. https://doi.org/10.1021/acsestwater.5c01162. https://par.nsf.gov/biblio/10705303.

@article{osti_10705303,
place = {Country unknown/Code not available}, title = {A Hybrid Response Surface Methodology and Machine Learning Framework for Quantifying Effects of Physicochemical Parameters on PFAS Distribution}, url = {https://par.nsf.gov/biblio/10705303}, DOI = {10.1021/acsestwater.5c01162}, abstractNote = {Not Available}, journal = {ACS ES&T Water}, volume = {6}, number = {5}, publisher = {ACS}, author = {Patel, Harsh V and Green, Jazmin and Park, Hyoshin and Luster-Teasley_Pass, Stephanie and Zhao, Renzun}, }