Selective removal of heavy metals from water is a complex topic. We present a theoretical-computational approach, supported by experimental evidences, to design a functionalized nanomaterial that is able to selectively capture metallic ions from water in a self-assembling process. A theoretical model is used to map an experimental mixture of Ag nanoparticles (AgNPs) and either Co 2+ or Ni 2+ onto an additive highly asymmetric attractive Lennard-Jones binary mixture. Extensive NVT (constant number of particles, volume, and temperature) Monte Carlo simulations are performed to derive a set of parameters that first induce aggregation among the two species in solution and then affect the morphology of the aggregates. The computational predictions are thus compared with the experimental results. The gathered insights can be used as guidelines for the prediction of an optimal design of a new generation of selective nanoparticles to be used for metallic ion adsorption and hence for maximizing the trapping of ions in an aqueous solution.
Prosposito, P., Venditti, I., Corsi, P., Capone, B., Battocchio, C., Meneghini, C., et al. (2019). Designing an Optimal Ion Adsorber at the Nanoscale: The Unusual Nucleation of AgNP/Co 2+ -Ni 2+ Binary Mixtures. JOURNAL OF PHYSICAL CHEMISTRY. C, 123(6), 3855-3860.
Designing an Optimal Ion Adsorber at the Nanoscale: The Unusual Nucleation of AgNP/Co 2+ -Ni 2+ Binary Mixtures
prosposito paolo;venditti iole;mochi federico;
2019-01-01
Abstract
Selective removal of heavy metals from water is a complex topic. We present a theoretical-computational approach, supported by experimental evidences, to design a functionalized nanomaterial that is able to selectively capture metallic ions from water in a self-assembling process. A theoretical model is used to map an experimental mixture of Ag nanoparticles (AgNPs) and either Co 2+ or Ni 2+ onto an additive highly asymmetric attractive Lennard-Jones binary mixture. Extensive NVT (constant number of particles, volume, and temperature) Monte Carlo simulations are performed to derive a set of parameters that first induce aggregation among the two species in solution and then affect the morphology of the aggregates. The computational predictions are thus compared with the experimental results. The gathered insights can be used as guidelines for the prediction of an optimal design of a new generation of selective nanoparticles to be used for metallic ion adsorption and hence for maximizing the trapping of ions in an aqueous solution.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
