<strong>Computational model of adsorption for paralytic shellfish poisoning toxins (PSTs) on graphene surface.</strong>

Abstract

Paralytic Shellfish Toxins (PSTs) are polar analytes, most of them with positive charges resulting in a charge-induced dipole at the graphene surface when they approach to it. Graphene is a novel material with great potentials to be used as sorbent due to its ultrahigh surface area. Herein, we perform the simulation about the retention mechanism of PSTs on the graphene through Merck Molecular Force Field (MMFF94) minimizations. The overall retention on graphene is a combination of two mechanisms:-Adsorption: The strength of analyte interactions with graphene is largely dependent on the molecular area in contact with the graphene surface, and also on the type and positioning of the functional groups in relation to the graphene surface at the points of contact.-Charge induced interactions of a polar analyte with the polarizable surface of graphene: when the polar group with apermanent dipole approaches the surface, an induced dipole is formed, increasing the attraction between the analyte and graphene surface.Computational results were compared with those obtained after elution using a HPLCHypercarb column: they showed a good correlation pattern where it was seen that the theoretical model exhibited the potential of graphene as an excellent sorbent material for saxitoxin and analogues.Hypercarb model, elution order: (shorter retention time) dcSTX < NEO < STX < GTX5 < GTX3 < C2 < GTX2 < C1 (longer retention time).Merck Molecular Force Field (MMFF94) model, interaction energy values order: (minor complex energy) dcSTX < STX < NEO < GTX5 < GTX3 < GTX2 < C2 < C1 (mayor complex energy)

Computational Model of Adsorption for Hydroxybenzoate Saxitoxin Derivatives (GCs) on Graphene Surface

“…We have previously reported experimental and computational studies on the association of water-soluble saxitoxin and tetrodotoxin analogues with pristine graphene and graphene oxide [15,16]. An applicable retention time prediction model must not only be predictive but also be able to predict analytes in the correct order.…”