We
present a coarse-grained (CG) molecular dynamics (MD) simulation
study of polymer nanocomposites
(PNCs) containing nanorods with homogeneous and patchy surface chemistry/functionalization,
modeled with isotropic and directional nanorod–nanorod attraction,
respectively. We show how the PNC morphology is impacted by the nanorod
design (i.e., aspect ratio, homogeneous or patchy surface chemistry/functionalization)
for nanorods with a diameter equal to the Kuhn length of the polymer
in the matrix. For PNCs with 10 vol % nanorods that have an aspect
ratio ≤5, we observe percolated morphology with directional
nanorod–nanorod attraction and phase-separated (i.e., nanorod
aggregation) morphology with isotropic nanorod–nanorod attraction.
In contrast, for nanorods with higher aspect ratios, both types of
attractions result in aggregated nanorods morphology due to the dominance
of entropic driving forces that cause long nanorods to form orientationally
ordered aggregates. For most PNCs with isotropic or directional nanorod–nanorod
attractions, the average matrix polymer conformation is not perturbed
by the inclusion of up to 20 vol % nanorods. The polymer chains in
contact with nanorods (i.e., interfacial chains) are on average extended
and statistically different from the conformations the matrix chains
adopt in the pure melt state (with no nanorods); in contrast, the
polymer chains far from nanorods (i.e., bulk chains) adopt the same
conformations as the matrix chains adopt in the pure melt state. We
also study the effect of other parameters, such as attraction strength,
nanorod volume fraction, and matrix chain length, for PNCs with isotropic
or directional nanorod–nanorod attractions. Collectively, our
results provide valuable design rules to achieve specific PNC morphologies
(i.e., dispersed, aggregated, percolated, and orientationally aligned
nanorods) for various potential applications.
The adsorptions of Cefocelis hydrochloride in aqueous solution on macroporous resin (HP-20) were studied. On the basis of static experiments with HP-20 resin, the adsorption kinetics, isotherms, and thermodynamics of Cefocelis hydrochloride in aqueous solution on macroporous resin (HP-20) were investigated. Adsorption equilibrium data were correlated with the Langmuir and Freundlich equations. Adsorption data of kinetic were modeled using the Crank equation and fitting for the diffusion coefficient (D e ), and the value of the liquid film mass transfer coefficient (k f ) was estimated from Carberry equation. Dynamic adsorption was performed on HP-20 resin packed in a glass column to obtain optimal parameters. The initial concentration, bed height, and residence time were considered to determine the dynamic test for the breakthrough curve. The values of thermodynamic parameters including the isosteric adsorption enthalpy (ΔH), free energy (ΔG), entropy (ΔS), and adsorption activation energy (E a ) demonstrated the process of adsorption was spontaneous and exothermic. The adsorption process was controlled by a physical mechanism.
Using
molecular dynamics simulations, we elucidate the effect of
nanorod roughness on nanorod aggregation, dispersion, and percolation
in polymer nanocomposites (PNCs). By choosing coarse-grained models
that enable systematic variation of the nanorod roughness and by selecting
purely repulsive pairwise interactions for nanorods and polymer chains,
we show how nanorod roughness affects the entropic driving forces
for various PNC morphologies. At this entropically driven limit, we
find that increasing nanorod roughness hinders nanorod aggregation
and promotes nanorod percolation in the polymer melt. As nanorod roughness
increases, the nanorod volume fraction needed to induce nanorod aggregation
also increases. Increasing nanorod roughness increases the configurational
entropy of the polymer chains and lowers the entropically induced
depletion attraction between nanorods.
In the field of materials science, microscopy is the first and often only accessible method for structural characterization. There is a growing interest in the development of machine learning methods...
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