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Polysiloxane is one of the most important polymeric materials in technological use. Polydimethylsiloxane displays glass-like mechanical properties at low temperature. Incorporation of phenyl siloxane, via copolymerization for example, improves not...

Thermal issues associated with lithium-ion batteries (LIBs) can dramatically affect their life cycle and overall performance. However, the effective heat transfer is deeply restrained by the high thermal resistance across the cathode (lithium cobalt oxide, LCO)-separator (polyethylene, PE) interface. This work presents a new approach to tailoring the interfacial thermal resistance, namely, unstructured self-assembled lamella (USAL). Compared to the popular self-assembled monolayers, although the USAL gives a redundant interface and amorphous molecule patterns, it can also provide many benefits, including easy assembly, more thermal bridges, and ready pressurization. Three small organic molecules (SOMs) were assembled into an LCO-PE interface, providing unique functional groups, -NH

Thermal issues have become increasingly important for the performance and lifetime of highly miniaturized and integrated devices. However, the high thermal resistance across the polymer/semiconductor interface greatly weakens the fast heat dissipation. In this study, applying the self-assembled monolayer (SAM) technique, organic molecules are employed as heat regulators to mediate interfacial thermal conductance (ITC) between semiconductors (silicon or Si) and polymers (polystyrene or PS). Silane-based SAM molecules with unique functional groups, such as -NH

Phononic metamaterials are widely used to attenuate wave propagation. However, designing the structure of phononic metamaterial remains a challenge. In this work, we proposed a transfer learning-based design framework to accelerate the design of phononic metamaterials with wide bandgaps. First, we establish a transfer learning model with convolutional layers. This model leverages the knowledge learned from the structure-elasticity dataset to predict the structure-phononic property relationship. We demonstrate that the transfer learning model achieves good prediction accuracy with limited training data. We also discuss the feasibility of using the structure-elasticity model to benefit the design optimization of phononic metamaterials. Then we propose a transfer learning-based design framework for the topology optimization of cellular metamaterial for optimal phononic properties (bandgap width). Parametric optimization is conducted to find the optimal structure features that lead to the widest bandgap. The structure features are represented by an embedding layer shared by the structure-elasticity and the structure-phononic property models. Next, the corresponding elastic stiffness constants are obtained via the structure-elasticity model. Then topology optimization is employed to generate the metamaterial structural images corresponding to the target elastic stiffness constant values. The effectiveness of the proposed design framework is validated by comparing the performances of design candidates with existing designs.

Polymeric materials have a broad range of mechanical and physical properties. They have been widely used in material science, biomedical engineering, chemical engineering, and mechanical engineering. The introduction of active elements into the soft matrix of polymers has enabled much more diversified functionalities of polymeric materials, such as self-healing, electroactive, magnetosensitive, pH-responsive, and many others. To further enable applications of these multifunctional polymers, a mechanistic modeling method is required and of great significance, as it can provide links between materials’ micro/nano-structures and their macroscopic mechanical behaviors. Towards this goal, molecular simulation plays an important role in understanding the deformation and evolution of polymer networks under external loads and stimuli. These molecular insights provide physical guidance in the formulation of mechanistic-based continuum models for multifunctional polymers. In this perspective, we present a molecular simulation-guided and physics-informed modeling framework for polymeric materials. Firstly, the physical theory for polymer chains and their networks is briefly introduced. It serves as the foundation for mechanistic-models of polymers, linking their chemistry, physics, and mechanics together. Secondly, the deformation of the polymer network is used to derive the strain energy density functions. Thus, the corresponding continuum models can capture the intrinsic deformation mechanisms of polymer networks. We then highlight several representative examples across multiphysics coupling problems to describe in detail for this proposed framework. Last but not least, we discuss potential challenges and opportunities in the modeling of multifunctional polymers for future research directions.

Machine learning (ML) has emerged as one of the most powerful tools transforming all areas of science and engineering. The nature of molecular dynamics (MD) simulations, complex and time-consuming calculations, makes them particularly suitable for ML research. This review article focuses on recent advancements in developing efficient and accurate coarse-grained (CG) models using various ML methods, in terms of regulating the coarse-graining process, constructing adequate descriptors/features, generating representative training data sets, and optimization of the loss function. Two classes of the CG models are introduced: bottom-up and top-down CG methods. To illustrate these methods and demonstrate the open methodological questions, we survey several important principles in constructing CG models and how these are incorporated into ML methods and improved with specific learning techniques. Finally, we discuss some key aspects of developing machine-learned CG models with high accuracy and efficiency. Besides, we describe how these aspects are tackled in state-of-the-art methods and which remain to be addressed in the near future. We expect that these machine-learned CG models can address thermodynamic consistent, transferable, and representative issues in classical CG models.

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-βVAE) 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-βVAE 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-βVAE model updating-based design framework. In each iteration, the optimal designs found property-driven optimization is used to update the training dataset. The GM-βVAE 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.

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 study, we proposed a deep feature learning-based framework to design cellular metamaterial structures considering two design objectives: bandgap width and stiffness. A Gaussian mixture variational autoencoder (GM-VAE) is employed to extract structural features and a Gaussian Process (GP) model is employed to enable property-driven structure optimization. By comparing the GM-VAE and a regular variational autoencoder (VAE), we demonstrate that (i) GM-VAE has the advantage of learning capability, and (ii) GM-VAE discovers a more diversified design set (in terms of the distribution in the performance space) in the unsupervised learning-based generative design. Two supervised learning strategies, building independent single-response GP models for each output and building an all-in-one multi-response GP model for all outputs, are employed and compared to establish the relationship between the latent features and the properties of interest. Multi-objective design optimization is conducted to obtain the Pareto frontier with respect to bandgap width and stiffness. The effectiveness of the proposed design framework is validated by comparing the performances of newly discovered designs with existing designs. The caveats to designing phonic bandgap metamaterials are summarized.

Hydrogels and polydimethylsiloxane (PDMS) are complementary to each other, since the hydrophobic PDMS provides a more stable and rigid substrate, while the water-rich hydrogel possesses remarkable hydrophilicity, biocompatibility, and similarity to biological tissues. Herein a transparent and stretchable covalently bonded PDMS-hydrogel bilayer (PHB) structure is prepared via in situ free radical copolymerization of acrylamide and allylamine-exfoliated-ZrP (AA-e-ZrP) on a functionalized PDMS surface. The AA-e-ZrP serves as cross-linking nano-patches in the polymer gel network. The covalently bonded structure is constructed through the addition reaction of vinyl groups of PDMS surface and monomers, obtaining a strong interfacial adhesion between the PDMS and the hydrogel. A mechanical-responsive wrinkle surface, which exhibs transparency change mechanochromism, is created via introducing a cross-linked polyvinyl alcohol film atop the PHB structure. A finite element model is implemented to simulate the wrinkle formation process. The implication of the present finding for the interfacial design of the PHB and PDMS-hydrogel-PVA trilayer (PHPT) structures is discussed.

This paper, the first of two companion papers, mainly reports theoretical considerations on working principle and structural design of a passive MEMS dc current sensor. The proposed sensor is capable of measuring dc current flowed in a two-wire appliance cord without using any cord separator like that used in Hall-effect based sensor. The crucial component of the proposed sensor is a cantilever made up of a piezoelectric film and a neodymium micro-magnet integrated on the end. The theoretical considerations are emphasized on the effect of partitioning piezoelectric thin film on the total output voltage, the magnetic force acted on the micro-magnet in a magnetic field generated by the dc current, and the cantilever stiffness subject to the sensing principle. A prototype of the proposed MEMS dc current sensor is thus micro-fabricated, with ten PZT partition plates and a 1-mm3 micro-magnet, in accordance with the theoretical consideration of the design principle. The prototype device provides a physical support for further experimental verifications to be discussed in Part II.

This paper, the second part of two companion papers, reports experimental methodology and verification results based on the prototype device of the proposed passive MEMS dc current sensor in Part I. The relationship between output voltages and crucial factors, such as the relative position of the sensor to the two-wire appliance cord, different connection modes of the PZT partition plates, as well as the applied dc current has been characterized. The experimental methodology mainly includes those of how to position the sensor device, how to determine the measurement range for sensitivity characterization, and how to electrically connect the PZT partition plates in different modes. Sensitivity characteristics were studied in ac current from 8 to 400 mA and in dc current from 0.5 to 3 A, respectively. Two typical connection modes, i.e., in series and parallel, were investigated with ten PZT partition plates. In the case of dc current, the waveforms of the output voltage in the individual PZT plate were studied in detail. First, the initial peak voltages corresponding to ON–OFF switching of dc current can be clearly characterized with a higher amplitude (relatively lower frequency) for turning ON and a lower one (relatively higher frequency) for turning OFF. Second, the measured peak voltages are smaller than the analytical ones, but showing similar increasing tendency with the increasing of the dc current. In light of the above-mentioned experimental verifications, the proposed passive MEMS dc current sensor was proved to be applicable to direct measurements of the two-wire appliance cords without using cord separator. The proposed sensor devices are, therefore, more convenient than Hall-effect-based sensors in electricity end-use measuring and monitoring of dc power supply.

We have developed a highly efficient and portable fluid–structure interaction (FSI) simulation package, so-called OpenFSI. Within this package, the structure dynamics is accounted by a lattice model (LM) implemented in the framework of Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), demonstrating the same accuracy as finite element analysis. The fluid flow is resolved by Palabos, which adopts the Lattice Boltzmann method (LBM) to efficiently solve the Boltzmann equation that can recover the Navier–Stokes equation in mesoscale. Additionally, the immersed boundary method (IBM) is employed to couple LM and LBM together, therefore endowing the flexibility to choose alternative solid and fluid solvers. The whole simulation is fulfilled within the framework of Palabos, and the LAMMPS framework is called in Palabos as an external library and coupled through IBM. To demonstrate the capability and accuracy of the proposed package, the validations for the LM are first performed by conducting the deflections of two-dimensional (2D) and three-dimensional (3D) beams in LAMMPS, and comparing the results with those in finite element analysis. Followed are the classical benchmarks of flow passing 2D flexible beam behind a cylinder and 3D flow passing a fixed cylinder. In the results, the free-falling of spheres and flapping of a deformable plate in cross-flow are investigated. Furthermore, the possibility to study complex FSI phenomena is demonstrated by the cases of spheres passing a dam and swimming of microswimmers. Lastly, the efficiency of this simulation package is explored by examining an extremely large system with thousands of red blood cells in blood flow. The OpenFSI package is found to have excellent linear scalability up to 8192 processors, due to the particle-based LM and LBM for structure and fluid flow respectively, as well as advanced cyberinfrastructure of LAMMPS package. Therefore, OpenFSI presents an alternative option to efficiently solve large scale FSI problems, hence to facilitate the unveiling of underlying physical mechanisms.

In order to decrease the measurement error of piezoelectric-cantilever current sensor caused by position and orientation errors, a position and orientation correction scheme including an array of piezoelectric cantilever and a system of nonlinear equations is presented in this paper. By solving the system with the values of piezoelectric outputs, current to be measured can be derived. Results of experiments of passive sensing are presented, proving the validity of the proposed correction scheme. Theoretical analysis of active sensing gives insights to future research.

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 achiev...

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 theor...

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.