(193bd) Design of Side Chains in P3HT-like Molecules for Maximizing Ionic Conductivity

Semi-conducting polymers, such as Poly(3-hexylthiophene) (aka P3HT) have been extensively studied for applications in areas such as organic photovoltaics, biosensors, and electrochromic devices. Likewise, ionically conducting polymers, such as Polyethylene oxide/glycol (aka PEO/PEG), have been extensively studied for applications as solid electrolytes for Li⁺-ion batteries. While these types of polymers have applications on their own, the combination of electronic and ionic conduction in the same material offers exciting opportunities for materials design when one considers the ability to leverage two types of conduction for designing energy storage devices with high capacity, and high charge-discharge rates, as well as bioelectronics such as organic electrochemical transistors. Additionally, electronic conduction is enhanced by the presence of nearby ionically charged groups. Previous studies have looked at the P3HT-b-PEO linear copolymer, which separates the ionic, and electronic conduction, but here we seek to keep a P3HT-like molecular template, where the traditionally alkyl side chain is replaced with different chemistries ranging between the limits of a fully alkyl chain (like in P3HT) and a fully pegylated side chain. We then evaluate the ionic mobility of each chemistry for the two limiting structural states of a crystalline phase and an amorphous phase. We find that the optimal chemistry of the side chain for ion conduction depends on whether the system is in the crystalline or amorphous phase, as percolation between ion solvation sites plays an important role in the amorphous phase while the crystalline phase masks this effect due to the regularity in its structure. We propose a relationship between the ionic mobility, the salt concentration, and the energy required to dissociate the salt by which the ionic conductivity can be estimated for these yet to be synthesized chemistries, providing guidance to our experimental collaborators. This framework is being employed with an expanded chemical composition space and the impact of different chemical groups on the ionic conductivity will be studied using a neural network to rapidly sample the entire phase space.