Thermal Metamaterial: Fundamental, Application, and Outlook

On-chip omnidirectional electromagnetic-thermal cloak

Simultaneously guiding electromagnetic waves and heat flow at any incidence angle to smoothly bypass some electromagnetic/thermal sensitive elements is a key factor to ensure efficient communication and thermal protection for an on-chip system. In this study, an omnidirectional on-chip electromagnetic-thermal cloak is proposed. Firstly, a holey metallic plate with periodic array of subwavelength apertures is designed by optical surface transformation to realize an omnidirectional electromagnetic cloaking module for on-chip electromagnetic signal. Secondly, a two-layer ring-shaped engineered thermal structure is designed by solving Laplace equation to realize an omnidirectional thermal cloaking module for in-chip heat flow. Finally, these two cloaking modules are combined to achieve cloaking effect for both the electromagnetic waves and thermal fields simultaneously, thus protecting the build-in electromagnetic/thermal sensitive elements without disturbing the external fields. The proposed electromagnetic-thermal cloak may have potential advantage in dealing with omnidirectional electromagnetic compatibility/shielding and multi-directional thermal management/dissipation of an on-chip system.

Machine Learning Aided Design and Optimization of Thermal Metamaterials

Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.

Orthopedic meta-implants

Meta-biomaterials, engineered materials with distinctive combinations of mechanical, physical, and biological properties stemming from their micro-architecture, have emerged as a promising domain within biomedical engineering. Correspondingly, meta-implants, which serve as the device counterparts of meta-biomaterials, offer exceptional functionalities, holding great potential for addressing complex skeletal diseases. This paper presents a comprehensive overview of the various types of meta-implants, including hybrid, shape-morphing, metallic clay, and deployable meta-implants, highlighting their unprecedented properties and recent achievement in the field. This paper also delves into the potential future developments of meta-implants, addressing the exploration of multi-functionalities in meta-biomaterials and their applications in diverse biomedical fields.

General deep learning framework for emissivity engineering

Wavelength-selective thermal emitters (WS-TEs) have been frequently designed to achieve desired target emissivity spectra, as a typical emissivity engineering, for broad applications such as thermal camouflage, radiative cooling, and gas sensing, etc. However, previous designs require prior knowledge of materials or structures for different applications and the designed WS-TEs usually vary from applications to applications in terms of materials and structures, thus lacking of a general design framework for emissivity engineering across different applications. Moreover, previous designs fail to tackle the simultaneous design of both materials and structures, as they either fix materials to design structures or fix structures to select suitable materials. Herein, we employ the deep Q-learning network algorithm, a reinforcement learning method based on deep learning framework, to design multilayer WS-TEs. To demonstrate the general validity, three WS-TEs are designed for various applications, including thermal camouflage, radiative cooling and gas sensing, which are then fabricated and measured. The merits of the deep Q-learning algorithm include that it can (1) offer a general design framework for WS-TEs beyond one-dimensional multilayer structures; (2) autonomously select suitable materials from a self-built material library and (3) autonomously optimize structural parameters for the target emissivity spectra. The present framework is demonstrated to be feasible and efficient in designing WS-TEs across different applications, and the design parameters are highly scalable in materials, structures, dimensions, and the target functions, offering a general framework for emissivity engineering and paving the way for efficient design of nonlinear optimization problems beyond thermal metamaterials.

Adaptive multi-temperature control for transport and storage containers enabled by phase-change materials

The transportation of essential items, such as food and vaccines, often requires adaptive multi-temperature control to maintain high safety and efficiency. While existing methods utilizing phase change materials have shown promise, challenges related to heat transfer and materials' physicochemical properties remain. In this study, we present an adaptive multi-temperature control system using liquid-solid phase transitions to achieve highly effective thermal management using a pair of heat and cold sources. By leveraging the properties of stearic acid and distilled water, we fabricated a multi-temperature maintenance container and demonstrated temperature variations of only 0.14-2.05% over a two-hour period, underscoring the efficacy of our approach. Our findings offer a practical solution to address critical challenges in reliable transportation of goods, with potential implications for various fields in physical, engineering, and life sciences.

Convective Thermal Metamaterials: Exploring High-Efficiency, Directional, and Wave-Like Heat Transfer

Convective thermal metamaterials are artificial structures where convection dominates in the thermal process. Due to the field coupling between velocity and temperature, convection provides a new knob for controlling heat transfer beyond pure conduction, thus allowing active and robust thermal modulations. With the introduced convective effects, the original parabolic Fourier heat equation for pure conduction can be transformed to hyperbolic. Therefore, the hybrid diffusive system can be interpreted in a wave-like fashion, reviving many wave phenomena in dissipative diffusion. Here, recent advancements in convective thermal metamaterials are reviewed and the state-of-the-art discoveries are classified into the following four aspects, enhancing heat transfer, porous-media-based thermal effects, nonreciprocal heat transfer, and non-Hermitian phenomena. Finally, a prospect is cast on convective thermal metamaterials from two aspects. One is to utilize the convective parameter space to explore topological thermal effects. The other is to further broaden the convective parameter space with spatiotemporal modulation and multi-physical effects.

Preparation and Band Gap Characteristics of Composite Film/Substrate Instability System

Soft materials such as biological tissues are prone to deformation and generate different stable structures under external stimulation. This property is widely used to create tunable patterns, and the tuning of the wrinkling patterns can be applied to the control of elastic waves. In this paper, the wrinkling modes of film/substrate systems with different geometric dimensions and material parameters were studied. It is verified by numerical simulation that the elastic wave band gaps corresponding to the two wrinkling modes can be effectively superposed in one system, and the experimental samples with two wrinkling modes coexisting in one system were prepared by parameter optimization and a moisture-curing process. A vibration test showed that the hybrid system could effectively suppress the propagation of elastic waves. Combined with engineering needs, the wrinkling system under different loading conditions was studied, which provides a design guide for widening and regulating the elastic wave band gap.

Rotational symmetry engineered, polarization and incident angle-insensitive, perfect metamaterial absorber for X and Ku band wireless applications

In this paper, a square enclosed split-maze shaped metamaterial absorber is proposed for X and Ku band wireless applications. Two square metal enclosures were introduced around the split-maze structure to make it rotational symmetric and thus insensitive to cross-polarization. The proposed absorber has shown maximum absorptions at 9.33 GHz, 12.83 GHz, 13.86 GHz, and 15.61 GHz with single negative value of permittivity. The absorber is insensitive to the incident angle of applied EM waves for normal and oblique incidence up to 180 degrees. In addition, it was proved co- & cross-polarization insensitive due to the symmetric structure of the patch. A comprehensive equivalent circuit analysis was done to explain the fundamental EM behaviour of the metamaterial structure, and the circuit outputs coincided with the simulation results. Finally, the metamaterial was measured for both unit cell, and the array after fabrication and simulation results were validated. The proposed MMA is suitable for wireless applications in devices, especially for sensing, EM energy harvesting, EM coupling reduction, and antenna gain enhancement purposes.

Robustly printable freeform thermal metamaterials

Thermal metamaterials have exhibited great potential on manipulating, controlling and processing the flow of heat, and enabled many promising thermal metadevices, including thermal concentrator, rotator, cloak, etc. However, three long-standing challenges remain formidable, i.e., transformation optics-induced anisotropic material parameters, the limited shape adaptability of experimental thermal metadevices, and a priori knowledge of background temperatures and thermal functionalities. Here, we present robustly printable freeform thermal metamaterials to address these long-standing difficulties. This recipe, taking the local thermal conductivity tensors as the input, resorts to topology optimization for the freeform designs of topological functional cells (TFCs), and then directly assembles and prints them. Three freeform thermal metadevices (concentrator, rotator, and cloak) are specifically designed and 3D-printed, and their omnidirectional concentrating, rotating, and cloaking functionalities are demonstrated both numerically and experimentally. Our study paves a powerful and flexible design paradigm toward advanced thermal metamaterials with complex shapes, omnidirectional functionality, background temperature independence, and fast-prototyping capability.