Computational optimization of bioadsorbents for the removal of pharmaceuticals from water
authors Pereira, JM; Calisto, V; Santos, SM
nationality International
journal JOURNAL OF MOLECULAR LIQUIDS
author keywords Adsorption; Water treatment; Molecular dynamics; Molecular modelling; Monte-Carlo simulations; Virtual porous carbons
keywords PAPER-MILL SLUDGE; ACTIVATED CARBONS; WASTE-WATER; ADSORPTION; ADSORBENTS; EFFLUENT; SURFACE; WOOD; ANTIBIOTICS; PYROLYSIS
abstract Pharmaceutically active ingredients are among the most persistent wastewater contaminants, resisting to wastewater treatment plants (WWTP) conventional processes, and some of them are proved to pose serious threats to organisms and the environment. In this context, adsorption by activated carbons (AC) is one of the most promising methodologies for the removal of pharmaceuticals from water due to its versatility and high removal efficiency. However, ACs are expensive and therefore not widely applied in WWTP. Primary sludge from paper mills has been previously appointed as a potential inexpensive and renewable source of carbon for AC production by pyrolysis. Computational chemistry may help shed some light into the molecular mechanisms underlying the adsorption of organic pollutants onto ACs. In this context, CarbGen, an online tool for Virtual Porous Carbon (VPC) models creation, was developed and made available for public use. A quantitatively validated model based on both physical and chemical characteristics of an experimentally produced AC is proposed. The produced model is in agreement with obtained experimental data in terms of elemental composition, functional group content and surface area. Grand Canonical Monte Carlo (GCMC) studies were performed on various VPC models with different levels of oxygen content, revealing the importance of electrostatic mechanisms in adsorption, with different degrees depending on the pharmaceutical molecular characteristics. The results further reinforce the importance of functional groups in future VPC models for correct molecular modelling. (C) 2019 Elsevier B.V. All rights reserved.
publisher ELSEVIER SCIENCE BV
issn 0167-7322
year published 2019
volume 279
beginning page 669
ending page 676
digital object identifier (doi) 10.1016/j.molliq.2019.01.167
web of science category Chemistry, Physical; Physics, Atomic, Molecular & Chemical
subject category Chemistry; Physics
unique article identifier WOS:000462101800070
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journal impact factor 4.513
5 year journal impact factor 3.929
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