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ABSTRACTIn polymer physics, the concept of entanglement refers to the topological constraints between long polymer chains that are closely packed together. Both theory and experimentation suggest that entanglement has a significant influence on the mechanical properties of polymers. This indicates its promise for materials design across various applications. However, understanding the relationship between entanglement and mechanical properties is complex, especially due to challenges related to length scale constraints and the difficulties of direct experimental observation. This research delves into how the polymer network structure changes when deformed. We specifically examine the relationship between entanglement, crosslinked networks, and their roles in stretching both entangled and unentangled polymer systems. For unentangled polymers, our findings underscore the pivotal role of crosslinking bond strength in determining the system’s overall strength and resistance to deformation. As for entangled polymers, entanglement plays a pivotal role in load bearing during the initial stretching stage, preserving the integrity of the polymer network. As the stretching continues and entanglement diminishes, the responsibility for bearing the load increasingly shifts to the crosslinking network, signifying a critical change in the system’s behavior. We noted a linear correlation between the increase in entanglement and the rise in tensile stress during the initial stretching stage. Conversely, the destruction of the network correlates with a decrease in tensile stress in the later stage. The findings provide vital insights into the complex dynamics between entanglement and crosslinking in the stretching processes of polymer networks, offering valuable guidance for future manipulation and design of polymer materials to achieve desired mechanical properties.

Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction (ϕ) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer–particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For ϕ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer–particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials.

Phononic bandgap metamaterials, which consist of periodic cellular structures, are capable of absorbing energy within a certain frequency range. Designing metamaterials that trap waves across a wide wave frequency range is still a challenging task. In this paper, we present a deep feature learning-based design framework for both unsupervised generative design and supervised learning-based exploitative optimization. The Gaussian mixture beta variational autoencoder (GM-ßVAE) is used to extract latent features as design variables. Gaussian process (GP) regression models are trained to predict the relationship between latent features and properties for property-driven optimization. The optimal structural designs are reconstructed by mapping the optimized latent feature values to the original image space. Compared with the regular variational autoencoder (VAE), we demonstrate that GM-ßVAE has a better learning capability and is able to generate a more diversified design set in unsupervised generative design. Furthermore, we propose an iterative GM-ßVAE model updating-based design framework. In each iteration, the optimal designs found property-driven optimization is used to update the training dataset. The GM-ßVAE model is re-trained with the updated dataset for the optimization search in the next iteration. The effectiveness of the iterative design framework is demonstrated by comparing the proposed designs with the designs found by the traditional single-loop design method and the topologically optimized designs reported in literatures. The caveats to designing phonic bandgap metamaterials are summarized. [ABSTRACT FROM AUTHOR]

A passive MEMS DC/AC current sensor with high measurement accuracy, accomplished by the methodology combining both the "stress-equilibrium" solution and the "position-free" solution, was proposed for measuring electricity consumption of household equipment and Information and Communication Technology devices. For the "stress-equilibrium" solution, slots between two adjacent piezoelectric plates are implemented to minimize the distribution difference of the uneven stress close to the fixed end. The measurement error caused by the uneven stress distribution is decreased from 9% to 4% for ten-piezoelectric-plates and from 8% to 0.5% for three-piezoelectric-plates, respectively. For the "position-free" consideration, an array comprised of four piezoelectric cantilevers is proposed to eliminate the positional error resulted by the uneven magnetic field distribution generated by the test object of electric currents. And the solution is proofed to be an effective method to eliminate the positional error by theoretical and simulation analysis. In light of the above preliminary results, the passive MEMS DC/AC current sensor is believed to be useful to achieve high measurement accuracy via integrating the "stress-equilibrium" and the "position-free" designs. The newly proposed current sensor with high measurement accuracy is applicable to two-wire appliance cord without using any cord separator like that used in Hall-effect based sensor. [ABSTRACT FROM AUTHOR]

A position and orientation correction scheme for current sensing based on magnetic piezoelectric cantilevers is proposed in this letter, offering an insight for the improvement of measurement accuracy. In this scheme, an array of magnetic piezoelectric cantilevers, converting mechanical vibration generated by current induced magnetic force applied on magnets at the ends of cantilevers into output voltages, is utilized. A system of nonlinear equations is established in terms of amplitude of objective current, position, and orientation errors. They are derived by solving the system of equations treated as variables, instead of parameters. As a result, the measurement error caused by position and orientation errors decreased substantially. Experiments of passive current sensing for two-wire appliances prove the validity of the proposed position and orientation correction scheme. The application of the proposed scheme can be further extended to sensors based on the magnetic field sensing mechanism, for achieving higher measurement accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a method for passive sensing a two-wire DC current without using any cord separator. In this method, a piezoelectric thin-film cantilever with a micro-magnet on its end is positioned near a power cord. The DC current in the power cord induces a magnetic field, which generates a magnetic force acting on the micro-magnet. Consequently, the piezoelectric cantilever is bent and induces an output voltage that is sensitive to small variations of DC currents. A prototype device has been fabricated and experimentally studied. It was found that the initial peak amplitude of the piezoelectric output voltage increases linearly with the DC current value upon "ON-OFF" switching. Compared to the Hall-effect based sensing, this method has the advantages of no need of driving voltage or cord separators even for two-wire appliance cords. Apart from the conventional consumer electronics, this method may have a wide application foreground in the new emerging fields, such as energy vehicles, lithium ion battery, or smart power grid. [ABSTRACT FROM AUTHOR]