Mechanical Janus Structures by Soft–Hard Material Integration (Adv. Mater. 6/2023)

Zhang, Haozhe; Yang, Weizhu; Liu, Qingchang; Gao, Yuan; Yue, Zhufeng; Xu, Baoxing

doi:10.1002/adma.202370037

Cited by 6 publications

(5 citation statements)

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“…The experimental setup shown in Figure a is then built up to characterize the fabricated metadevices and the details are in the Experimental Section. To confirm the obtained results from the experiment, the simulated 2D digital numbers are performed based on the Fresnel–Kirchhoff diffraction integration, [ 47 ] the calculation details can be found in Section S6 (Supporting Information).…”

Section: Design and Methodsmentioning

confidence: 99%

Creating Wavelength‐Selective Polarization Digital Numbers

Intaravanne

Ansari

Ahmed

et al. 2023

Advanced Optical Materials

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Polarization and wavelength are two important properties of light for understanding optics. Engineered polarization and wavelength profiles have received considerable interest due to their unusual optical features and more degrees of freedom. However, to simultaneously control polarization and wavelengths, conventional methods suffer from big pixel size, complicated fabrication process, and limited levels in phase control. The unprecedented capability of metasurfaces in the light control has shown much promise to tackle these challenges. Polarization digital numbers with ten different wavelengths are proposed and experimentally realized. A geometric metasurface is used to simultaneously realize wavelength multiplexing, phase multiplexing, and polarization rotation, creating wavelength‐selective polarization digital numbers. A deep learning approach is used to increase the identification accuracy of the digital numbers. The approach can simultaneously control wavelength and polarization, providing more design flexibility. This work may find applications in many fields such as virtual reality, image steganography, and anti‐counterfeiting.

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Section: Design and Methodsmentioning

confidence: 99%

Creating Wavelength‐Selective Polarization Digital Numbers

Intaravanne

Ansari

Ahmed

et al. 2023

Advanced Optical Materials

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“…Due to the excellent deformability of the conductive network to hinder crack propagation, its local reconstruction by self‐adaptive deformation could create the new conductive pathways. Consequently, the hierarchical conductor, which can also be called the Janus film due to its distinct components, microstructure, and functionalities on its two sides according to the broad definition of the term “Janus”, [ 51,52 ] exhibits both exceptional mechanical properties and conductivity at large strain. In addition, the in situ scanning electron microscopy (SEM) and finite element (FE) simulation are carried out to elucidate the relevant mechanisms as well.…”

Section: Introductionmentioning

confidence: 99%

Soft‐Hard Janus Nanoparticles Triggered Hierarchical Conductors with Large Stretchability, High Sensitivity, and Superior Mechanical Properties

He,

Yang,

Liu

et al. 2024

Advanced Materials

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There is a long‐standing conflict between the large stretchability and high sensitivity for strain sensors, a strategy of decoupling the mechanical/electrical module by constructing the hierarchical conductor has been developed in this study. The hierarchical conductor, consisting of a mechanically stretchable layer, a conductive network layer, and a strongly bonded interface, can be produced in a simple one‐step process with the aid of soft‐hard Janus nanoparticles (JNPs). The introduction of JNPs in the stretchable layer can evenly distribute stress and dissipate energy due to forming the rigid‐flexible homogeneous networks. Specifically, JNPs can drive graphene nanosheets (GNS) to fold or curl, creating the unique JNPs‐GNS building block that can further construct the conductive network. Due to its excellent deformability to hinder crack propagation, the flexible conductive network could be stretched continuously and the local conductive pathways could be reconstructed. Consequently, the hierarchical conductor could detect both subtle strain of 0−2% and large strain of up to 370%, with a gauge factor from 66.37 to 971.70, demonstrating outstanding stretchability and sensitivity. And it also owns large tensile strength (5.28 MPa) and high deformation stability. This hierarchical design will give graphene‐based sensors a major boost in emerging applications.This article is protected by copyright. All rights reserved

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“…In addition, Zafar et al [ 19 ] found that soft composites with a soft solid as the matrix and incompressible liquid inclusions as reinforcements can be designed to respond to external stimuli and offer the prospect of providing a targeted combination of stiffness and toughness. Inspired by the seamless integration of soft and hard materials in biological structures, Zhang et al [ 20 ] presented a mechanical Janus structure composed of soft and hard materials with a dramatic difference in mechanical properties at an additively manufacturable macroscale. Yang et al [ 21 ] proposed a hierarchical lattice metamaterial with modified face‐centered cubic cell configuration.…”

Section: Introductionmentioning

confidence: 99%

The Nonlinear Mechanical Constitutive Model and Deep Learning Method to Inverse Design Dual‐Feature‐Integrated Lattice Metamaterial

Deng,

Huang,

2024

Adv Eng Mater

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Bio‐inspired lattice structure of regularly fibrous organization is kind of structural materials with practical value in flexible bio‐integrated electronics. The recently proposed dual‐feature integrated lattice structure can accurately customize the nonlinear mechanical curve of biological issues. However, it is still lacking the constitutive models to inverse design the desirable mechanical properties. In this work, a nonlinear mechanical constitutive model for the dual‐feature integrated metamaterial is established by introducing the equilibrium equation and deformation coordination conditions. The experimental and numerical results show that the proposed constitutive model could predict accurately the stress‐strain curves of dual‐feature integrated lattice structure. In addition, the Machine Learning ‐ Whale Optimization Algorithm method (ML‐WOA) is used to inverse design the dual‐feature integrated lattice structure, which can quickly find the target mechanical responses (chicken skin and human skin) in a board design space. The dual‐feature integrated mechanical metamaterial has a higher structural design options in comparison to the pure horseshoe and chiral metamaterial. The finding of this work contributes to the designs of lattice structures with flexibility and stretchable electronics.This article is protected by copyright. All rights reserved.

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Mechanical Janus Structures by Soft–Hard Material Integration (Adv. Mater. 6/2023)

Cited by 6 publications

References 0 publications

Creating Wavelength‐Selective Polarization Digital Numbers

Creating Wavelength‐Selective Polarization Digital Numbers

Soft‐Hard Janus Nanoparticles Triggered Hierarchical Conductors with Large Stretchability, High Sensitivity, and Superior Mechanical Properties

The Nonlinear Mechanical Constitutive Model and Deep Learning Method to Inverse Design Dual‐Feature‐Integrated Lattice Metamaterial

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