Molecular modeling study of antibacterial molecules on nylon 6,6 surfaces

Verner, Adam; Tokarský, Jonáš

doi:10.1016/j.matpr.2019.12.221

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Smart algorithm as implemented in MS with 5•10 5 steps was used. Convergence thresholds for the maximum energy and maximum force changes were 1•10 −4 kcal/mol and 5•10 −3 kcal/mol/Å, respectively [10]. For the Coulombic and the van der Waals forces, atom based summation method as implemented in MS was used.…”

Section: Modeling Conditionsmentioning

confidence: 99%

Molecular Modeling of Interactions Between Catalytic Nanoparticles and Polymer Carriers

Verner

Tokarský

2020

NANOCON Conference Proeedings

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Nowadays, the scientific and technological field is at a very high level, which is related, among other things, to the large production of various products, and this unfortunately brings a high degree of environmental threat. For example, highly hazardous gases might be released into the environment accidentally, therefore a solution to these dangerous situations needs to be found. A research is conducted into cerium(IV) oxide, platinum, and palladium nanoparticles (NPs), which have been found to have hazardous gases degrading properties. This work complements this field by studying the interactions between NPs and their potential polymer nanofiber carriers, polyamide 6 (PA) and β-polyvinylidene fluoride (PVDF). In order to determine the suitability of these carriers, molecular modeling (geometry optimization using COMPASS forcefield) was involved. Interaction energies (Eint) between the most occupied (hkl) planes of NPs ((100), ( 110), (111), and (311)) and different (hkl) planes of PA and PVDF surfaces ((001), ( 100) and ( 010), ( 001), (00-1), respectively) were determined. Although all types of NPs showed attractive interactions with all types of surfaces, preferred orientations are clearly identifiable. Attention was focused on the possible influence of the NPs interlayer distance dhkl on the resulting Eint.

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Section: Modeling Conditionsmentioning

confidence: 99%