Tunable smart mid infrared thermal control emitter based on phase change material VO2 thin film

This work proposes a colored intelligent thermal control emitter characterized by adjustable structural color development with a reflection spectrum in the visible light range and intelligent thermal control with temperature switching in the mid infrared wavelength band. In the beginning, we investigate an intelligent thermal control emitter based on a multilayer membrane formed by the phase change material vanadium dioxide, metal material Ag, and dielectric materials Ge and ZnS. By incorporating the thermochromic material vanadium dioxide, the structure exhibits a high emissivity of εH = 0.85 in the range of 3-13 μm wavelength when in the high temperature metallic state, enabling efficient heat radiation. In the low temperature dielectric state, the structure only has a low emission capacity of εL = 0.07, reducing energy loss. Our results demonstrate that the structure achieves excellent emission regulation ability (Δε = 0.78) through the thermal radiation modulation from high to low temperature, maintaining effective thermal control. Furthermore, the thermal control emitter exhibits selective emission of peak wavelength due to destructive interference and shows a certain independence on polarization and incidence angle. Additionally, the integration of structural color adjustment capability enhances the visual aesthetics of the human experience. The proposed colored intelligent thermal control structure has significant potential for development in aesthetic items such as colored architecture and energy-efficient materials. 

Structural color tunable intelligent mid-infrared thermal control emitter

Diffusion driven by temperature or concentration gradients is a fundamental mechanism of energy and mass transport, which inherently differs from wave propagation in both physical foundations and application prospects. Compared with conventional schemes, metamaterials provide an unprecedented potential for governing diffusion processes, based on emerging theories like the transformation and the scattering cancellation theory, which enormously expanded the original concepts and suggest innovative metamaterial-based devices. We hereby use the term"diffusionics"to generalize these remarkable achievements in various energy (e.g., heat) and mass (e.g., particles and plasmas) diffusion systems. For clarity, we categorize the numerous studies appeared during the last decade by diffusion field (i.e., heat, particles, and plasmas) and discuss them from three different perspectives: the theoretical perspective, to detail how the transformation principle is applied to each diffusion field; the application perspective, to introduce various intriguing metamaterial-based devices, such as cloaks and radiative coolers; and the physics perspective, to connect with concepts of recent concern, such as non-Hermitian topology, nonreciprocal transport, and spatiotemporal modulation. We also discuss the possibility of controlling diffusion processes beyond metamaterials. Finally, we point out several future directions for diffusion metamaterial research, including the integration with artificial intelligence and topology concepts.

Controlling mass and energy diffusion with metamaterials

Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next-generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure-property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data-driven design. This review provides a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. Existing research is organized into data-driven modules, encompassing data acquisition, machine learning-based unit cell design, and data-driven multiscale optimization. The approaches are further categorized within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions and opportunities. 

Data‐Driven Design for Metamaterials and Multiscale Systems: A Review

Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next-generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure-property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data-driven design. In this review, we provide a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. We organize existing research into data-driven modules, encompassing data acquisition, machine learning-based unit cell design, and data-driven multiscale optimization. We further categorize the approaches within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions and opportunities.

pdf/data-driven-design-for-metamaterials-and-multiscale-systems-3pe3p1l7.pdf

Data-Driven Design for Metamaterials and Multiscale Systems: A Review

/pdf/deep-learning-based-design-of-thermal-metadevices-2gsfk7qi.pdf

Deep learning based design of thermal metadevices

Selective thermal emission and infrared camouflage based on layered media

An improved four-parameter conjugated bond-based peridynamic method for fiber-reinforced composites

The traditional trial-and-error method is expensive and time-consuming to determine the needling process parameters satisfying the stiffness requirements of the 3D needled composites. A new design method for these unknown needling process parameters is proposed using a convolutional neural network (CNN) surrogate model, in which a series of stress distribution images of representative volume cell (RVC) for 3D needled composites under different loads are implemented. These stress distribution images reserve both material stiffness and needling process information. The CNN surrogate model can be efficiently established to obtain the relationship between the stress distribution images and the needling process parameters. The accuracy of the training samples and the test samples in the CNN surrogate model reaches 93.85% and 87.50% respectively. An artificial RVC can be directly constructed corresponding to the stiffness properties. The unknown needling process parameters can be determined using the CNN surrogate model and the stress distribution images of the artificial RVC. The result shows the stiffness properties of 3D needled composites with the needling process parameters obtained by the CNN surrogate model are in good agreement with the experimental results, where the maximum relative error is 7.66%. This method provides a new way to design the needling process parameters satisfying the specific stiffness requirements of the 3D needled composites. 

Determination of needling process satisfying stiffness requirements of
 3D
 needled composites

Micromechanical modeling for fiber-reinforced composites based on element-based peridynamics

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Papers

Deep learning based design of thermal metadevices

Selective thermal emission and infrared camouflage based on layered media

An improved four-parameter conjugated bond-based peridynamic method for fiber-reinforced composites

Determination of needling process satisfying stiffness requirements of 3D needled composites

Micromechanical modeling for fiber-reinforced composites based on element-based peridynamics