Researchers Alejandro Toral-Lopez, Damiano Marian, and Gianluca Fiori from the University of Pisa have developed a new simulation tool that could help better understand and predict electrical conductivity in a range of materials important to the energy industry. Their work, published in the journal Physical Review B, focuses on a phenomenon called hopping transport, which is crucial for understanding how electricity moves through disordered materials like organic semiconductors, perovskites, and certain alloys. These materials are of significant interest for applications such as solar cells, light-emitting diodes, and other electronic devices.
Hopping transport occurs when electrons jump or “hop” between localized states within a material, rather than moving freely as they do in more ordered materials like silicon. Two main regimes of hopping transport have been identified: Variable Range Hopping (VRH) and Nearest Neighbor Hopping (NNH). VRH allows electrons to hop over longer distances to find more energetically favorable states, while NNH restricts electrons to hopping only to the nearest neighboring states. The transition between these regimes as temperature changes has been observed experimentally but has not been fully understood or modeled effectively until now.
The researchers developed a Monte Carlo Random Resistor Network-based simulator that can accurately capture both VRH and NNH regimes. This tool allows them to study how material properties, such as the localization length and the spatial and energetic distribution of sites, influence which transport regime dominates. The simulator was validated against experimental data and successfully reproduced the transition between VRH and NNH, as well as accurately capturing 1D, 2D, and 3D VRH behavior.
For the energy industry, this research provides a powerful tool for studying and interpreting transport mechanisms in disordered materials. By better understanding how electricity moves through these materials, researchers can optimize their properties for specific applications, such as improving the efficiency of solar cells or the performance of organic light-emitting diodes. This work offers both a theoretical framework for interpreting experiments and a practical tool for designing and developing new materials for energy applications.
Source: Physical Review B
This article is based on research available at arXiv.