Pressure-Induced Enhancement of Thermoelectric Performance in Rubrene

Insight into the effect of side chains on thermal transport of organic semiconductors

Understanding the correlation mechanism of side chains on thermal transport of organic semiconductors is crucial for functionalized organic electronics. In this study, phenyl and alkyl side chains, two representatives of side chain engineering, are chosen to modify dinaphtho-[2,3-b:2',3'-f]thieno[3,2-b]thiophene (DNTT) to synthesize Ph-DNTT and C10-DNTT. The thermal conductivities of the three organic semiconductors exhibit obvious anisotropy, and the corresponding relationships are along-chain > inter-chain > cross-chain. The phenyl side chains enhance the thermal conductivity in the along-chain direction and degrade it in the inter-chain direction, while the alkyl side chains hinder thermal transport. In the cross-chain direction, side chains have a slight effect on thermal transport. The structure orientation consistency between the phenyl side chains and the main chains in Ph-DNTT leads to phonon coupling in the along-chain direction, which improves phonon transport. In the inter-chain direction, the combined effect of the phonon group velocity and phonon participation ratio causes the thermal conductivity degeneracy of Ph-DNTT. For C10-DNTT, the vibrational mismatch between the alkyl side chains and the main chains results in the degradation of thermal transport in the along-chain and inter-chain directions. In the cross-chain direction, the nonbonding interaction dominates the energy transfer in the three organic semiconductors, which induces inferior phonon transport properties and weak effects of side chains.

A Critical Review of Machine Learning Techniques on Thermoelectric Materials

Thermoelectric (TE) materials can directly convert heat to electricity and vice versa and have broad application potential for solid-state power generation and refrigeration. Over the past few decades, efforts have been made to develop new TE materials with high performance. However, traditional experiments and simulations are expensive and time-consuming, limiting the development of new materials. Machine learning (ML) has been increasingly applied to study TE materials in recent years. This paper reviews the recent progress in ML-based TE material research. The application of ML in predicting and optimizing the properties of TE materials, including electrical and thermal transport properties and optimization of functional materials with targeted TE properties, is reviewed. Finally, future research directions are discussed.

Strain-induced thermoelectricity in pentacene

The present work discusses a non-synthetic strategy to achieve a favorable thermoelectric response in pentacene via strain. It is found that a uni-axial strain is capable of inducing spatial anisotropy in the molecule. As a result, the transmission spectrum becomes highly asymmetric under a particular strained scenario, which is the primary requirement to get a favorable thermoelectric response. Different thermoelectric quantities are computed for the strain-induced pentacene using Green's function formalism following the Landauer-Büttiker prescription. Various scenarios are considered to make the present work more realistic, such as the effects of substrate, coupling strength between the molecule and electrodes, dangling bonds, etc. Such a scheme to enhance the thermoelectric performance in pentacene is technologically intriguing and completely new to the best of our knowledge.

The Zintl phases compound AEIn2As2 (AE=Ca, Sr, Ba): topological phase transition under pressure

AEIn2As2 (AE=Ca, Sr, Ba), as a new crucial nonmagnetic thermoelectric candidate, is desired to be understood in terms of its potential physical properties and controversial structural phases in both experimental...