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Designer Conjugation-Break Spacer That Boosts Charge Transport in Semiconducting Terpolymers

Yunfeng Qiang, Runze Xie, Bo He, Cheng Liu, Quanfeng Zhou, Yunfei Wang, Xiaodan Gu, Xiu Gong, Yi Liu, Xuncheng Liu

2024Macromolecules17 citationsDOI

Abstract

Conjugated polymers (CPs) are pivotal for high-mobility applications, typically avoiding nonconjugated units due to their perceived negative impact on charge transport. Contrary to this belief, our study introduces a nonconjugated unit, DKPDO, which when employed as a conjugation-break spacer (CBS), significantly enhances charge transport. DKPDO was obtained as a center-modified isoindigo variant featuring strongly electron-withdrawing amide groups that diminish the electron density and foster multiple hydrogen-bonding interactions. This results in enhanced electron deficiency and reinforced coplanarity relative to isoindigo. DKPDO was incorporated into a polymer backbone, comprising isoindigo and bithiophene, to create a series of terpolymers with varied molar ratios, allowing systematic evaluation of their solubilities, interchain interactions, crystallinities, and energy levels. Notably, incorporating DKPDO into the terpolymers results in a universal improvement of hole mobilities and operational stabilities over the DKPDO-free counterpart. Remarkably, the terpolymer containing 2.5% DKPDO achieves a high hole mobility of 4.12 cm 2 V –1 s –1, approximately 6-fold higher than that of the parent CP and among the highest for CPs based on center-inserted isoindigo units. The study not only, for the first time, realizes charge transport enhancement with CBS in polymer backbones but also sets a precedent for the strategic use of nonconjugated units in developing high-performing CPs.

Topics & Concepts

Charge (physics)Materials scienceCopolymerPolymer chemistryChemical physicsChemistryPolymer scienceNanotechnologyPolymerChemical engineeringPhysicsComposite materialEngineeringQuantum mechanicsOrganic Electronics and PhotovoltaicsGraphene research and applicationsAdvanced Memory and Neural Computing