Heat dissipation is one of the most serious problems in modern integrated electronics with the continuously decreasing devices size. Large portion of the consumed power is inevitably dissipated in the form of waste heat which not only restricts the device energy-efficiency performance itself, but also leads to severe environment problems and energy crisis. Thermoelectric Seebeck effect is a green energy-recycling method, while thermoelectric Peltier effect can be employed for heat management by actively cooling overheated devices, where passive cooling by heat conduction is not sufficiently enough. However, the technological applications of thermoelectricity are limited so far by their very low conversion efficiencies and lack of deep understanding of thermoelectricity in microscopic levels. Probing and managing the thermoelectricity is therefore fundamentally important particularly in nanoscale. In this short review, we will first briefly introduce the microscopic techniques for studying nanoscale thermoelectricity, focusing mainly on scanning thermal microscopy (SThM). SThM is a powerful tool for mapping the lattice heat with nanometer spatial resolution and hence detecting the nanoscale thermal transport and dissipation processes. Then we will review recent experiments utilizing these techniques to investigate thermoelectricity in various nanomaterial systems including both (two-material) heterojunctions and (single-material) homojunctions with tailored Seebeck coefficients, and also spin Seebeck and Peltier effects in magnetic materials. Next, we will provide a perspective on the promising applications of our recently developed Scanning Noise Microscope (SNoiM) for directly probing the non-equilibrium transporting hot charges (instead of lattice heat) in thermoelectric devices. SNoiM together with SThM are expected to be able to provide more complete and comprehensive understanding to the microscopic mechanisms in thermoelectrics. Finally, we make a conclusion and outlook on the future development of microscopic studies in thermoelectrics.
Micro/nano-porous polymeric material is considered a unique industrial material due to its extremely low thermal conductivity, low density, and high surface area. Therefore, it is necessary to establish an accurate thermal conductivity prediction model suiting their applicable conditions and provide a theoretical basis for expanding their applications. In this work, the development of the calculation model of equivalent thermal conductivity of micro/nano-porous polymeric materials in recent years is summarized. Firstly, it reviews the process of establishing the overall equivalent thermal conductivity calculation model for micro/nanoporous polymers. Then, the predicted calculation models of thermal conductivity are introduced separately according to the conductive and radiative thermal conductivity models. In addition, the thermal conduction part is divided into the gaseous thermal conductivity model, solid thermal conductivity model and gas–solid coupling model. Finally, it is concluded that, compared with other porous materials, there are few studies on heat transfer of micro/ nanoporous polymers, especially on the particular heat transfer mechanisms such as scale effects at the micro/nanoscale. In particular, the following aspects of porous polymers still need to be further studied: micro scaled thermal radiation, heat transfer characteristics of particular morphologies at the nanoscales, heat transfer mechanism and impact factors of micro/nanoporous polymers. Such studies would provide a more accurate prediction of thermal conductivity and a broader application in energy conversion and storage systems.
The second law of thermodynamics dictates that heat simultaneously flows from the hot to cold bath on average. To go beyond this picture, a range of works in the past decade show that, other than the average dynamical heat flux determined by instantaneous thermal bias, a non-trivial flux contribution of intrinsic geometric origin is generally present in temporally driven systems. This additional heat flux provides a free lunch for the pumped heat and could even drive heat against the bias. We review here the emergence and development of this so called “geometric heat pump”, originating from the topological geometric phase effect, and cover various quantum and classical transport systems with different internal dynamics. The generalization from the adiabatic to the non-adiabatic regime and the application of control theory are also discussed. Then, we briefly discuss the symmetry restriction on the heat pump effect, such as duality, supersymmetry and time-reversal symmetry. Finally, we examine open problems concerning the geometric heat pump process and elucidate their prospective significance in devising thermal machines with high performance.
In recent years, lithium ion (Li-ion) batteries have served as significant power sources in portable electronic devices and electric vehicles because of their high energy density and rate capability. There are growing concerns towards the safety of Li-ion batteries, in which thermal conductivities of anodes, cathodes, electrolytes and separator play key roles for determining the thermal energy transport in Li-ion battery. In this review, we summarize the state-of-the-art studies on the thermal conductivities of commonly used anodes, cathodes, electrolytes and separator in Li-ion batteries, including both theoretical and experimental reports. First, the thermal conductivities of anodes and cathodes are discussed, and the effects of delithiation degree and temperature of materials are also discussed. Then, we review the thermal conductivities of commonly used electrolytes, especially on solid electrolytes. Finally, the basic concept of interfacial thermal conductance and simulation methods are presented, as well as the interfacial thermal conductance between separator and cathodes. This perspective review would provide atomic perspective knowledge to understand thermal transport in Li-ion battery, which will be beneficial to the thermal management and temperature control in electrochemical energy storage devices.
Negative thermal expansion (NTE) of materials is an intriguing phenomenon challenging the concept of traditional lattice dynamics and of importance for a variety of applications. Progresses in this field develop markedly and update continuously our knowledge on the NTE behavior of materials. In this article, we review the most recent understandings on the underlying mechanisms (anharmonic phonon vibration, magnetovolume effect, ferroelectrorestriction and charge transfer) of thermal shrinkage and the development of NTE materials under each mechanism from both the theoretical and experimental aspects. Besides the low frequency optical phonons which are usually accepted as the origins of NTE in framework structures, NTE driven by acoustic phonons and the interplay between anisotropic elasticity and phonons are stressed. Based on the data documented, some problems affecting applications of NTE materials are discussed and strategies for discovering and design novel framework structured NET materials are also presented.
Nonlinear heat transfer can be exploited to reveal novel transport phenomena and thus enhance people’s ability to manipulate heat flux at will. However, there has not been a mature discipline called nonlinear thermotics like its counterpart in optics or acoustics to make a systematic summary of relevant researches. In the current review, we focus on recent progress in an important part of nonlinear heat transfer, i.e., tailoring nonlinear thermal devices and metamaterials under the Fourier law, especially with temperature-dependent thermal conductivities. We will present the basic designing techniques including solving the equation directly and the transformation theory. Tuning nonlinearity coming from multi-physical effects, and how to calculate effective properties of nonlinear conductive composites using the effective medium theory are also included. Based on these theories, researchers have successfully designed various functional materials and devices such as the thermal diodes, thermal transistors, thermal memory elements, energy-free thermostats, and intelligent thermal materials, and some of them have also been realized in experiments. Further, these phenomenological works can provide a feasible route for the development of nonlinear thermotics.
With the rapidly increasing integration density and power density in nanoscale electronic devices, the thermal management concerning heat generation and energy harvesting becomes quite crucial. Since phonon is the major heat carrier in semiconductors, thermal transport due to phonons in mesoscopic systems has attracted much attention. In quantum transport studies, the nonequilibrium Green’s function (NEGF) method is a versatile and powerful tool that has been developed for several decades. In this review, we will discuss theoretical investigations of thermal transport using the NEGF approach from two aspects. For the aspect of phonon transport, the phonon NEGF method is briefly introduced and its applications on thermal transport in mesoscopic systems including one-dimensional atomic chains, multi-terminal systems, and transient phonon transport are discussed. For the aspect of thermoelectric transport, the caloritronic effects in which the charge, spin, and valley degrees of freedom are manipulated by the temperature gradient are discussed. The time-dependent thermoelectric behavior is also presented in the transient regime within the partitioned scheme based on the NEGF method.
Traditional simulation methods have made prominent progress in aiding experiments for understanding thermal transport properties of materials, and in predicting thermal conductivity of novel materials. However, huge challenges are also encountered when exploring complex material systems, such as formidable computational costs. As a rising computational method, machine learning has a lot to offer in this regard, not only in speeding up the searching and optimization process, but also in providing novel perspectives. In this work, we review the state-of-the-art studies on material’s thermal properties based on machine learning technique. First, the basic principles of machine learning method are introduced. We then review applications of machine learning technique in the prediction and optimization of material’s thermal properties, including thermal conductivity and interfacial thermal resistance. Finally, an outlook is provided for the future studies.
We retrospect three abstract models for heat engines which include a classic abstract model in textbook of thermal physics, a primary abstract model for finite-time heat engines, and a refined abstract model for finite-time heat engines. The detailed models of heat engines in literature of finite-time thermodynamics may be mapped into the refined abstract model. The future developments based on the refined abstract model are also surveyed.
Brownian particles suspended in disordered crowded environments often exhibit non-Gaussian normal diffusion (NGND), whereby their displacements grow with mean square proportional to the observation time and non-Gaussian statistics. Their distributions appear to decay almost exponentially according to “universal” laws largely insensitive to the observation time. This effect is generically attributed to slow environmental fluctuations, which perturb the local configuration of the suspension medium. To investigate the microscopic mechanisms responsible for the NGND phenomenon, we study Brownian diffusion in low dimensional systems, like the free diffusion of ellipsoidal and active particles, the diffusion of colloidal particles in fluctuating corrugated channels and Brownian motion in arrays of planar convective rolls. NGND appears to be a transient effect related to the time modulation of the instantaneous particle’s diffusivity, which can occur even under equilibrium conditions. Consequently, we propose to generalize the definition of NGND to include transient displacement distributions which vary continuously with the observation time. To this purpose, we provide a heuristic one-parameter function, which fits all time-dependent transient displacement distributions corresponding to the same diffusion constant. Moreover, we reveal the existence of low dimensional systems where the NGND distributions are not leptokurtic (fat exponential tails), as often reported in the literature, but platykurtic (thin sub-Gaussian tails), i.e., with negative excess kurtosis. The actual nature of the NGND transients is related to the specific microscopic dynamics of the diffusing particle.