Random multilayer (RML) structures, or aperiodic superlattices, can localize coherent phonons and therefore exhibit drastically reduced lattice thermal conductivity compared to their superlattice counterparts. The optimization of RML structures is essential for obtaining ultralow thermal conductivity, which is critical for various applications such as thermoelectrics and thermal barrier coatings. A higher degree of disorder in RMLs will lead to stronger phonon localization and, correspondingly, a lower lattice thermal conductivity. In this work, we identified several essential parameters for quantifying the disorder in layer thicknesses of RMLs. We were able to correlate these disorder parameters with thermal conductivity, as confirmed by classical molecular dynamics simulations of conceptual Lennard-Jones RMLs. Moreover, we have shown that these parameters are effective as features for physics-based machine learning models to predict the lattice thermal conductivity of RMLs with improved accuracy and efficiency.

Randomizing the layer thickness of superlattices (SL) can lead to localization of coherent phonons and thereby reduces the lattice thermal conductivity κ l. In this work, we propose strategies that can suppress incoherent phonon transport in the above random multilayer (RML) structure to further reduce κ l. Molecular dynamics simulations are conducted to investigate phonon heat conduction in SLs and RMLs with lattice imperfections. We found that interfacial species mixing enhances thermal transport across single interfaces and few-period SLs through the phonon “bridge” mechanism, while it substantially reduces the κ l of many-period SLs by breaking the phonon coherence. This is a clear manifestation of the transition from incoherent-phonon-dominated to coherent-phonon-dominated heat conduction in SLs when the number of interface increases. In contrast, interfacial species mixing always increases the κ l of RMLs owing to the dominance of incoherent phonons. Moreover, we found that doping a binary RML with impurities can reduce κ l significantly, especially when the impurity atom has an atomic mass lower or higher than both of the two base elements. This work reveals the critical effect of lattice imperfections on thermal transport in SLs and RMLs, and provides a unique strategy to hierachically suppress coherent and incoherent phonon transport concurrently.

This article reviews the recent progress towards achieving carbon-based thermoelectric materials. A wide range of experimental and computational studies on carbon allotropes and composites is covered in this review paper. Specifically, we discuss the strategies for engineering graphene, graphene nanoribbon, graphene nanomesh, graphene nanowiggle, carbon nanotube (CNT), fullerene, graphyne, and carbon quantum dot for better thermoelectric performance. Moreover, we discuss the most recent advances in CNT/graphene-polymer composites and the related challenges and solutions. We also highlight the important charge and heat transfer mechanisms in carbon-based materials and state-of-the-art strategies for enhancing thermoelectric performance. Finally, we provide an outlook towards the future of carbon-based thermoelectrics.

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