Nanomaterial Texture-Based Machine Learning of Ciprofloxacin Adsorption on Nanoporous Carbon
Uko Maran
Drug substances in water bodies and groundwater have become a significant threat to the surrounding environment. Therefore our study focused on the ability of the nanoporous carbon materials to remove ciprofloxacin from aqueous solutions under specific experimental conditions and on the development of the mathematical model that would allow describing the molecular interactions of the adsorption process and calculating the adsorption capacity of the material. One can read about ciprofloxacin as a drug substance and its effect on the environment and living organisms in a newly published scientific article. Based on the adsorption measurements of the 87 carbon materials, it was found that, depending on the porosity and pore size distribution, adsorption capacity values varied between 55 and 495 mg g−1. For a more detailed analysis of the effects of different carbon textures and pores characteristics, a machine learning Quantitative nano-Structure–Property Relationship (QnSPR) was developed to describe and predict the ability of a nanoporous carbon material to remove ciprofloxacin from aqueous solutions. The adsorption capacity of potential nanoporous carbon-based adsorbents for the removal of ciprofloxacin was shown to be sufficiently accurately described by a three-parameter multi-linear QnSPR equation. This description was achieved only with parameters describing the texture of the carbon material such as specific surface area and pore size fractions of 1.1–1.2 nm and 3.3–3.4 nm.
Article: https://doi.org/10.3390/ijms252111696
FAIR data and model: http://dx.doi.org/10.15152/QDB.265
