Enhanced Heterogeneous Diffusion of Nanoparticles in Semiflexible Networks

Abstract

The transport of nanoparticles in semiflexible networks, which form diverse principal structural components throughout living systems, is important in biology and biomedical applications. By combining large-scale molecular simulations as well as theoretical analysis, we demonstrate here that nanoparticles in polymer networks with semiflexible strands possess enhanced heterogeneous diffusion characterized by more evident hopping dynamics. Particularly, the hopping energy barrier approximates to linear dependence on confinement parameters in the regime of moderate rigidity, in contrast to the quadratic dependence of both its soft and hard counterparts. This nonmonotonic feature can be attributed to the competition between the conformation entropy and the bending energy regulated by the chain rigidity, captured by developing an analytical model of a hopping energy barrier. Moreover, these theoretical results agree reasonably well with previous experiments. The findings bear significance in unraveling the fundamental physics of substance transport confined in network-topological environments and would provide an explanation for the dynamics diversity of nanoparticles within various networks, biological or synthetic.

Sticky Rouse Time Features the Self-Adhesion of Supramolecular Polymer Networks

“…Ω­( r , t ) is a binary function such that Ω­( r , t ) = 1 if a r-bond is established, while Ω­( r , t ) = 0 if the r-bond is broken. The state of Ω­( r , t ) is updated every time step Δ t through a Metropolis MC algorithm based on the Bell model as follows ,− where ξ is an equally distributed random number between 0 and 1 generated in every time step. E B and E UB are the energy barriers (in unit of k B T ) for binding and unbinding, respectively.…”

Dynamics of Nanoparticles in Polydisperse Polymer Networks: from Free Diffusion to Hopping

“…39−49 In some of the earliest simulation studies, this network was simply modeled as an array of fixed obstacles, 39 which is clearly a far cry from a physically realistic description. Other authors have included connectivity and flexibility in the network model, but most of them considered only regular structures, in which the cross-links are placed at the vertices of a regular lattice and connected either by chain segments 40,48,49 or directly by springs. [41][42][43]45 In the latter case, because there is no actual strand connecting the cross-links, strand dynamics and entanglement effects are not accounted for.…”

“…When a nanoparticle (NP) is embedded in a polymer network, its dynamics can slow down dramatically. , Understanding what factors govern this slowing down is of primary importance in many fields, such as material science ( e.g., with application to thin films − and polymer-based sensors ,, ), biophysics, − and medicine, in particular for applications in drug delivery. ,, Although in recent years the diffusion of NPs in polymer solutions and melts has been the subject of numerous theoretical − and simulation studies, − only few investigations have dealt with the problem of NP diffusion in permanently cross-linked networks, despite its importance in many applications. − In some of the earliest simulation studies, this network was simply modeled as an array of fixed obstacles, which is clearly a far cry from a physically realistic description. Other authors have included connectivity and flexibility in the network model, but most of them considered only regular structures, in which the cross-links are placed at the vertices of a regular lattice and connected either by chain segments ,, or directly by springs. − , In the latter case, because there is no actual strand connecting the cross-links, strand dynamics and entanglement effects are not accounted for. Moreover, real-life networks, such as hydrogels, vulcanized rubbers, or networks produced by electron irradiation, are often disordered and polydisperse, with a continuous distribution of strand lengths, properties that lead to an additional complexity in the dynamics of the NPs.…”

WaveNet-Based Deep Neural Networks for the Characterization of Anomalous Diffusion (WADNet)

“…A random walker undergoes anomalous diffusion when its mean squared displacement (MSD) deviates from a linear temporal evolution [1,2]. Close attention is paid to such diffusion process in a variety of scientific fields, such as physics [3][4][5][6], chemistry [7], biology [8][9][10][11][12], economics [13][14][15], and social science [16]. The raw information of anomalous diffusion dynamics can be recorded as trajectories of random walkers via techniques like single particle tracking [17][18][19][20][21].…”