Abstract:Nanoparticle (NP) probes were used
to characterize the local structure
of N-isopropylacrylamide (NIPAAM), a thermoresponsive
hydrogel, using single particle tracking (SPT). Swelling ratio, and
thus gel network confinement, was varied by tuning polymer and cross-linker
concentrations. Based on the swelling ratio, the volume phase transition
(VPTT) was determined to be near 32 °C. In general, NPs were
found to be localized by two barriers. A primary localization region
of approximately 100 nm was attributed to … Show more
“…Average mesh size, ξ, is calculated for each polymer network and the dynamics of a tracer particle is compared as a function of ξ/σ tr . Although several factors influence the dynamics of a tracer particle in polymer networks, such as interactions between a tracer particle and polymer segments [8,19,33], aggregation of polymer segments of the network [30], and network heterogeneity [19,31,32], we solely focus on the size-dependent obstructive effect of small meshes by investigating the diffusion of an inert, nonsticky tracer particle in homogeneous polymer networks. Similar with those in polymer solutions and melts [24,34,35], the dynamics of a tracer particle in polymer networks can be distinguished by the relative mesh size to that of a tracer particle, ξ/σ tr [36].…”
Section: Introductionmentioning
confidence: 99%
“…The measurement of tracer diffusion provides important pieces of microscopic information on the structural and dynamic properties of underlying polymeric materials, including polymer films [ 5 , 6 , 7 , 8 , 9 , 10 ], and biological cells [ 11 , 12 , 13 , 14 , 15 ], and on the penetration and mobility of analytes in polymer gels employed in a broad range of applications for drug delivery and sensors [ 9 , 16 , 17 ]. Among polymer networks, stimuli-responsive hydrogels serve as an interesting platform for the study of tracer diffusion [ 7 , 8 , 9 , 18 , 19 ], because the internal structure of the polymer networks can be altered significantly upon changes in environmental conditions, such as temperature, pH, ionic strength, and solvent quality [ 20 , 21 , 22 ]. However, the dynamics of molecules and nanoparticles in hydrogels is not well understood at the nanoscopic and mesoscopic scales, particularly with respect to the effect of change in the internal structure of the polymer network on molecular transport.…”
Section: Introductionmentioning
confidence: 99%
“…The porosity of polymer networks is a key parameter characterizing the internal structure of polymer networks and it is measured by a structural quantity, called the average mesh size, [ 23 ]. Accordingly, previous studies on tracer diffusion in polymer networks have focused on understanding the effect of different network mesh sizes [ 7 , 8 , 9 , 19 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. For instance, recent experiments on tracer diffusion in hydrogels investigated the effect of changes in by varying the degree of crosslinking and by volume phase transition [ 7 , 8 , 9 , 19 , 26 , 31 ].…”
We report Brownian dynamics simulations of tracer diffusion in regularly crosslinked polymer networks in order to elucidate the transport of a tracer particle in polymer networks. The average mesh size of homogeneous polymer networks is varied by assuming different degrees of crosslinking or swelling, and the size of a tracer particle is comparable to the average mesh size. Simulation results show subdiffusion of a tracer particle at intermediate time scales and normal diffusion at long times. In particular, the duration of subdiffusion is significantly prolonged as the average mesh size decreases with increasing degree of crosslinking, for which long-time diffusion occurs via the hopping processes of a tracer particle after undergoing rattling motions within a cage of the network mesh for an extended period of time. On the other hand, the cage dynamics and hopping process are less pronounced as the mesh size decreases with increasing polymer volume fractions. The interpretation is provided in terms of fluctuations in network mesh size: at higher polymer volume fractions, the network fluctuations are large enough to allow for collective, structural changes of network meshes, so that a tracer particle can escape from the cage, whereas, at lower volume fractions, the fluctuations are so small that a tracer particle remains trapped within the cage for a significant period of time before making infrequent jumps out of the cage. This work suggests that fluctuation in mesh size, as well as average mesh size itself, plays an important role in determining the dynamics of molecules and nanoparticles that are embedded in tightly meshed polymer networks.
“…Average mesh size, ξ, is calculated for each polymer network and the dynamics of a tracer particle is compared as a function of ξ/σ tr . Although several factors influence the dynamics of a tracer particle in polymer networks, such as interactions between a tracer particle and polymer segments [8,19,33], aggregation of polymer segments of the network [30], and network heterogeneity [19,31,32], we solely focus on the size-dependent obstructive effect of small meshes by investigating the diffusion of an inert, nonsticky tracer particle in homogeneous polymer networks. Similar with those in polymer solutions and melts [24,34,35], the dynamics of a tracer particle in polymer networks can be distinguished by the relative mesh size to that of a tracer particle, ξ/σ tr [36].…”
Section: Introductionmentioning
confidence: 99%
“…The measurement of tracer diffusion provides important pieces of microscopic information on the structural and dynamic properties of underlying polymeric materials, including polymer films [ 5 , 6 , 7 , 8 , 9 , 10 ], and biological cells [ 11 , 12 , 13 , 14 , 15 ], and on the penetration and mobility of analytes in polymer gels employed in a broad range of applications for drug delivery and sensors [ 9 , 16 , 17 ]. Among polymer networks, stimuli-responsive hydrogels serve as an interesting platform for the study of tracer diffusion [ 7 , 8 , 9 , 18 , 19 ], because the internal structure of the polymer networks can be altered significantly upon changes in environmental conditions, such as temperature, pH, ionic strength, and solvent quality [ 20 , 21 , 22 ]. However, the dynamics of molecules and nanoparticles in hydrogels is not well understood at the nanoscopic and mesoscopic scales, particularly with respect to the effect of change in the internal structure of the polymer network on molecular transport.…”
Section: Introductionmentioning
confidence: 99%
“…The porosity of polymer networks is a key parameter characterizing the internal structure of polymer networks and it is measured by a structural quantity, called the average mesh size, [ 23 ]. Accordingly, previous studies on tracer diffusion in polymer networks have focused on understanding the effect of different network mesh sizes [ 7 , 8 , 9 , 19 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. For instance, recent experiments on tracer diffusion in hydrogels investigated the effect of changes in by varying the degree of crosslinking and by volume phase transition [ 7 , 8 , 9 , 19 , 26 , 31 ].…”
We report Brownian dynamics simulations of tracer diffusion in regularly crosslinked polymer networks in order to elucidate the transport of a tracer particle in polymer networks. The average mesh size of homogeneous polymer networks is varied by assuming different degrees of crosslinking or swelling, and the size of a tracer particle is comparable to the average mesh size. Simulation results show subdiffusion of a tracer particle at intermediate time scales and normal diffusion at long times. In particular, the duration of subdiffusion is significantly prolonged as the average mesh size decreases with increasing degree of crosslinking, for which long-time diffusion occurs via the hopping processes of a tracer particle after undergoing rattling motions within a cage of the network mesh for an extended period of time. On the other hand, the cage dynamics and hopping process are less pronounced as the mesh size decreases with increasing polymer volume fractions. The interpretation is provided in terms of fluctuations in network mesh size: at higher polymer volume fractions, the network fluctuations are large enough to allow for collective, structural changes of network meshes, so that a tracer particle can escape from the cage, whereas, at lower volume fractions, the fluctuations are so small that a tracer particle remains trapped within the cage for a significant period of time before making infrequent jumps out of the cage. This work suggests that fluctuation in mesh size, as well as average mesh size itself, plays an important role in determining the dynamics of molecules and nanoparticles that are embedded in tightly meshed polymer networks.
“…To the best of our knowledge, fundamental research on ionic polymer nanocomposites to understand and investigate the ionic interactions, nanoparticle mobility on the interphasial region, and nanoparticle dispersion state of the nanocomposite have not been performed so far. Exceptions are the coarse grained model by Hong et al [79,80] for nanoparticle ionic liquids, where nanoparticles diffuse like in a polymer solution [81,82,83] while chains diffuse faster than nanoparticles, as well as the studies in ionomer nanocomposites [59,84], polymer charged solutions [85,86] and polymer gels [87,88].…”
We investigate the effect of various spherical nanoparticles in a polymer matrix on dispersion, chain dimensions and entanglements for ionic nanocomposites at dilute and high nanoparticle loading by means of molecular dynamics simulations. The nanoparticle dispersion can be achieved in oligomer matrices due to the presence of electrostatic interactions. We show that the overall configuration of ionic oligomer chains, as characterized by their radii of gyration, can be perturbed at dilute nanoparticle loading by the presence of charged nanoparticles. In addition, the nanoparticle’s diffusivity is reduced due to the electrostatic interactions, in comparison to conventional nanocomposites where the electrostatic interaction is absent. The charged nanoparticles are found to move by a hopping mechanism.
“…Few studies have been reported on the diffusion of NPs entrapped in thermoresponsive hydrogels and there are no effective models for predicting the mobility of NPs in a polymeric network with thermally induced phase transitions. Because of a lack of effective mathematical or physical models, many experimental efforts have been made to determine the diffusion of NPs using single particle tracking (SPT) [37][38][39] or measuring the uptake and release of drugs into thermo-sensitive hydrogels [40,41]. However, in these works, experimental characterization can be laborious and expensive, or only a few factors were considered.…”
Thermoresponsive hydrogels have been studied intensively for creating smart drug carriers and controlled drug delivery. Understanding the drug release kinetics and corresponding transport mechanisms of nanoparticles (NPs) in a thermoresponsive hydrogel network is the key to the successful design of a smart drug delivery system. To investigate the anomalous NP diffusion in smart hydrogels with tunable network characteristics, we construct an energy-conserving dissipative particle dynamics model of rigid NPs entrapped in a hydrogel network in an aqueous solution, where the hydrogel network is formed by cross-linked semiflexible polymers of thermoresponsive poly(N-isopropylacrylamide) (PNIPAM). By varying the environmental temperature crossing the lower critical solution temperature of PNIPAM we can significantly change the hydrogel network characteristics. We systematically investigate how the matrix porosity of the hydrogel and the nanoparticle size affect the NPs' diffusion process and NPs' release kinetics at different temperatures. Quantitative results on the mean-squared displacement and the van Hove displacement distributions of NPs show that all NPs entrapped in the smart hydrogels undergo subdiffusion at both low and high temperatures. For a coil state and as the system temperature approaches the critical temperature of the coil-to-globule phase transition, both the subdiffusive exponent and the diffusion coefficient of NPs increase due to the increased kinetic energy and the decreased confinement on NPs, while the transport of NPs in the hydrogels can be also enhanced by decreasing the matrix porosity of the polymer network and NPs' size. However, when the solution temperature is increased above the critical temperature, the hydrogel network collapses following the coil-to-globule transition, with the NPs tightly trapped in some local regions inside the hydrogels. Consequently, the NP diffusion coefficient can be reduced by two orders of magnitude, or the diffusion processes can even be completely stopped. These findings provide new insights for designing controlled drug release from stimuli-responsive hydrogels, including autonomously switch on/off drug release to respond to the changes of the local environment.
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