Percolative Charge Transport In Binary Nanocrystal Solids

TL;DR

This study models electron transport in binary nanocrystal solids, revealing a mobility minimum at the percolation threshold influenced by temperature, electron density, and other factors, using a hierarchical simulation approach.

Contribution

It introduces the Hierarchical Nanoparticle Transport Simulator (HINTS) to analyze charge transport and explains the mobility minimum with a renormalized trap model considering various physical parameters.

Findings

Mobility exhibits a deep minimum at the percolation threshold.

The minimum shifts with changes in electron density and temperature.

Transport behavior is explained by a renormalized trap model.

Abstract

We simulated electron transport across a binary nanocrystal solid (BNS) of PbSe NCs with diameters of 6.5nm and 5.1nm. We used our Hierarchical Nanoparticle Transport Simulator HINTS to model the transport in these BNSs. The mobility exhibits a minimum at a Large-NC-fraction f_LNC=0.25. The mobility minimum is deep at T=80K and partially smoothed at T=300K. We explain this minimum as follows. As the LNC fraction f_LNC starts growing from zero, the few LNCs act as traps for the electrons traversing the BNS because their relevant energy level is lower. Therefore, increasing the f_LNC concentration of these traps decreases the mobility. As increasing f_LNC reaches the percolation threshold f_LNC=f_p, the LNCs form sample-spanning networks that enable electrons to traverse the entire BNS via these percolating LNC networks. Transport through the growing percolating LNC networks drives the rapid growth of the mobility as f_LNC grows past f_p. Therefore, the electron mobility exhibits a pronounced minimum as a function of f_LNC, centered at f_LNC=f_p. The position of the mobility minimum shifts to larger LNC fractions as the electron density increases. We have studied the trends of this mobility minimum with temperature, electron density, charging energy, ligand length, and disorder. We account for the trends by a "renormalized trap model", in which capturing an electron renormalizes a deep LNC trap into a shallow trap or a kinetic obstacle, depending on the charging energy. We verified this physical picture by constructing and analyzing heat maps of the mobile electrons in the BNS.