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Abstract Conjugated polyelectrolytes (CPEs) exhibit a strong interplay between ionic and electronic properties, enabling tunable photophysical properties and charge transport dynamics. Polyelectrolyte complexation represents a versatile self‐assembly strategy to control the properties of CPEs by forming dense phases with varying optoelectronic and mechanical characteristics. This study focuses on ionically assembled complexes comprising oppositely charged self‐doped CPE (CPE‐K) and bottlebrush polyelectrolyte (BPE). It is demonstrated that subtle adjustments in the composition of CPE‐K:BPE blends enables tuning of photophysical and viscoelastic properties. It is observed that increasing the CPE‐K:BPE monomeric ratio from 1:1 to 1:3 in the initial solution for complexation induces a significant bathochromic shift in the maximum photoluminescence intensity of the dense phase, from 1.8 to 1.4 eV. Additionally, a higher BPE content enhances the softness and adhesion of the solid complex, while maintaining yield‐stress behavior and cyclability of the dense phase. The ability to electrochemically and statically dope the CPE‐K–BPE complex, effectively modulating its charge transport and optoelectronic properties is also demonstrated. This work underscores the potential of these complex‐fluid phases for developing soft, adhesive, and elastic mixed ionic‐electronic conductors with tunable properties for functional applications and 3D‐printing.
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Data driven discovery of conjugated polyelectrolytes for optoelectronic and photocatalytic applications
Abstract Conjugated polyelectrolytes (CPEs), comprised of conjugated backbones and pendant ionic functionalities, are versatile organic materials with diverse applications. However, the myriad of possible molecular structures of CPEs render traditional, trial-and-error materials discovery strategy impractical. Here, we tackle this problem using a data-centric approach by incorporating machine learning with high-throughput first-principles calculations. We systematically examine how key materials properties depend on individual structural components of CPEs and from which the structure–property relationships are established. By means of machine learning, we uncover structural features crucial to the CPE properties, and these features are then used as descriptors in the machine learning to predict the properties of unknown CPEs. Lastly, we discover promising CPEs as hole transport materials in halide perovskite-based optoelectronic devices and as photocatalysts for water splitting. Our work could accelerate the discovery of CPEs for optoelectronic and photocatalytic applications.
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- Award ID(s):
- 1922042
- PAR ID:
- 10229948
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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