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1.
Phys Rev E ; 101(2-1): 022104, 2020 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-32168581

RESUMO

We revisit the problem of excluded volume deposition of rigid rods of length k unit cells over square lattices. Two new features are introduced: (a) two new short-distance complementary order parameters, called Π and Σ, are defined, calculated, and discussed to deal with the phases present as coverage increases; (b) the interpretation is now done beginning at the high-coverage ordered phase which allows us to interpret the low-coverage nematic phase as an ergodicity breakdown present only when k≥7. In addition the data analysis invokes both mutability (dynamical information theory method) and Shannon entropy (static distribution analysis) to further characterize the phases of the system. Moreover, mutability and Shannon entropy are compared, and we report the advantages and disadvantages they present for their use in this problem.

2.
J Mater Chem B ; 2(7): 766-774, 2014 Feb 21.
Artigo em Inglês | MEDLINE | ID: mdl-32261308

RESUMO

A series of bio-compatible metal-organic frameworks (MOFs) have been studied as potential carriers for drug delivery applications. Grand canonical Monte Carlo (GCMC) simulations were performed to study the adsorption of the model drug ibuprofen. Simulations were first validated with available experimental data for ibuprofen adsorption and release in MIL-53, MIL-100 and MIL-101. In the second stage, the study was extended to three additional MOFs with interesting properties in terms of bio-compatibility and porosity: CDMOF-1, based on edible precursors; MOF-74 containing a highly biocompatible metal (Mg); and BioMOF-100, a mesoporous MOF with extremely high pore volume. By comparing with experimental data, we show how GCMC simulation is able to predict the macroscopic performance of new porous MOFs in drug delivery applications, providing useful molecular-level insights and giving thermodynamic and structural details of the process. Adsorption isotherms, snapshots, energy of adsorption and radial distribution functions were used to analyse the drug delivery process.

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