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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. 

ABSTRACT The introduction of degradable units into the backbone of commodity vinyl polymers represents a major opportunity to address the societal challenge of plastic waste and polymer recycling. Previously, we reported the facile copolymerization ofα‐lipoic acid derivatives containing 1,2‐dithiolane rings with vinyl monomers leading to the incorporation of degradable S–S disulfide bonds along the backbone at relatively high dithiolane monomer feed ratios. To further enhance the recyclability of these systems, here we describe a facile and user‐friendly strategy for backbone degradation at significantly lower dithiolane loading levels through cleavage of both SS and SC backbone units. Copolymers ofn‐butyl acrylate (nBA) or styrene (St) with small amounts of eitherα‐lipoic acid (LA) or ethyl lipoate (ELp) dissolved in DMF were observed to undergo efficient degradation when heated at 100°C under air. For example, at only 5 mol% ELp, a high molecular weight poly(ELp‐co‐nBA) (M_n = 62 kg mol⁻¹) degraded to low molecular weight oligomers (M_n = 3.2 kg mol⁻¹) by simple heating in DMF. In contrast, extended heating of either poly(nBA) or poly(St) homopolymers under the same conditions did not lead to any change in molecular weight or cleavage of the C–C backbone. This novel approach allows for the effective degradation of vinyl‐based polymers with negligible impact on properties and performance due to the low levels of dithiolane incorporation. 

ABSTRACT Block copolymers play a vital role in materials science due to their diverse self‐assembly behavior. Traditionally, exploring the block copolymer self‐assembly and associated structure–property relationships involve iterative synthesis, characterization, and theory, which is labor‐intensive both experimentally and computationally. Here, we introduce a versatile, high‐throughput workflow toward materials discovery that integrates controlled polymerization and automated chromatographic separation with a novel physics‐informed machine‐learning algorithm for the rapid analysis of small‐angle X‐ray scattering data. Leveraging the expansive and high‐quality experimental data sets generated by fractionating polymers using automated chromatography, this machine‐learning method effectively reduces data dimensionality by extracting chemical‐independent features from SAXS data. This new approach allows for the rapid and accurate prediction of morphologies without repetitive and time‐consuming manual analysis, achieving out‐of‐sample predictive accuracy of around 95% for both novel and existing materials in the training data set. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combination of controlled synthesis, automated chromatography, and data‐driven analysis creates a powerful workflow that markedly expedites the discovery of structure–property relationships in advanced soft materials.