Equivalent dynamic thermoviscoelastic modeling of ionic polymers

Ionic polymers have attracted considerable attention due to their interesting sensing and actuating behavior which make them a proper choice for use in a wide range of applications including biomimetic robots and biomedical devices. The complicated electro-chemo-mechanical dynamics of ionic polymer actuators is a drawback for their applications in functional devices. Therefore, establishing a mathematical model which could effectively predict the actuators' dynamic behavior is of great interest. In this paper, a mathematical model, named equivalent dynamic thermoviscoelastic (EDT) model, based on thermal analogy and beam theory is proposed for dynamic analysis of bending-type ionic polymer actuators. Then, the developed model is extended for analyzing the performance of the actuator in finite element software. The finite element analysis of the actuator enables consideration of material and geometric nonlinearities and facilitates modeling of functional devices based on the ionic polymer actuators. The proposed modeling approach is validated using experimental data.

A new insight into growth mechanism and kinetics of mesoporous silica nanoparticles by in situ small angle x-ray scattering

The growth mechanism and kinetics of mesoporous silica nanoparticles (MSNs) were investigated for the first time by using a synchrotron time-resolved small-angle X-ray scattering (SAXS) analysis. The synchrotron SAXS offers unsurpassed time resolution and the ability to detect structural changes of nanometer sized objects, which are beneficial for the understanding of the growth mechanism of small MSNs (∼20 nm). The Porod invariant was used to quantify the conversion of tetraethyl orthosilicate (TEOS) in silica during MSN formation, and the growth kinetics were investigated at different solution pH and temperature through calculating the scattering invariant as a function of reaction time. The growth of MSNs was found to be accelerated at high temperature and high pH, resulting in a higher rate of silica formation. Modeling SAXS data of micelles, where a well-defined electrostatic interaction is assumed, determines the size and shape of hexadecyltrimethylammonium bromide (CTAB) micelles before and after the addition of TEOS. The results suggested that the micelle size increases and the micelle shape changes from ellipsoid to spherical, which might be attributed to the solubilization of TEOS in the hydrophobic core of CTAB micelles. A new "swelling-shrinking" mechanism is proposed. The mechanism provides new insights into understanding MSN growth for the formation of functional mesoporous materials exhibiting controlled morphologies. The SAXS analyses were correlated to the structure of CTAB micelles and chemical reaction of TEOS. This study has provided critical information to an understanding of the growth kinetics and mechanism of MSNs.

The structure of communities in scale-free networks

Scale-free networks are often used to model a wide range of real-world networks, such as social, technological, and biological networks. Understanding the structure of scale-free networks evolves into a big data problem for business, management, and protein function prediction. In the past decade, there has been a surge of interest in exploring the properties of scale-free networks. Two interesting properties have attracted much attention: the assortative mixing and community structure. However, these two properties have been studied separately in either theoretical models or real-world networks. In this paper, we show that the structural features of communities are highly related with the assortative mixing in scale-free networks. According to the value of assortativity coefficient, scale-free networks can be categorized into assortative, disassortative, and neutral networks, respectively. We systematically analyze the community structure in these three types of scale-free networks through six metrics: node embeddedness, link density, hub dominance, community compactness, the distribution of community sizes, and the presence of hierarchical communities. We find that the three types of scale-free networks exhibit significant differences in these six metrics of community structures. First, assortative networks present high embeddedness, meaning that many links lying within communities but few links lying between communities. This leads to the high link density of communities. Second, disassortative networks exhibit great hubs in communities, which results in the high compactness of communities that nodes can reach each other via short paths. Third, in neutral networks, a big portion of links act as community bridges, so they display sparse and less compact communities. In addition, we find that (dis)assortative networks show hierarchical community structure with power-law-distributed community sizes, while neutral networks present no hierarchy. Understanding the structure of communities from the angle of assortative mixing patterns of nodes can provide insights into the network structure and guide us in modeling information propagation in different categories of scale-free networks.