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A mussel-inspired mechanism was used to solve the problem of filler aggregation in rubber composites. This research aims to improve carbon black (CB) dispersion in epoxidized natural rubber (ENR) composites through π−π stacking and cation−π interactions by adding dopamine (D). In this study, various aromatic interactions (π−π stacking and cation−π interactions) between the D-functionalized ENR molecules and the surface of the CB were observed by Fourier transform infrared (FTIR) and Raman spectroscopy. Notably, the small and wideangle X-ray scattering (SAXS/WAXS) analyses supported our inference from the rubber processing analysis (RPA) and transmission electron microscopy (TEM) results that the aromatic interactions enhanced the CB dispersion in ENR composites. This phenomenon improved the tensile strength (138%), Young’s modulus (93%), and energy-saving properties (50%). Finally, this research provided an alternative strategy using mussel-inspired material to solve the CB aggregation problem in rubber products, yielding ENR composites with superior performance properties. 

Abstract Lipid carriers of hydrophobic paclitaxel (PTX) are used in clinical trials for cancer chemotherapy. Improving their loading capacity requires enhanced PTX solubilization. We compared the time-dependence of PTX membrane solubility as a function of PTX content in cationic liposomes (CLs) with lipid tails containing one (oleoyl; DOPC/DOTAP) or two (linoleoyl; DLinPC/newly synthesized DLinTAP)cisdouble bonds by using microscopy to generate kinetic phase diagrams. The DLin lipids displayed significantly increased PTX membrane solubility over DO lipids. Remarkably, 8 mol% PTX in DLinTAP/DLinPC CLs remained soluble for approximately as long as 3 mol% PTX (the solubility limit, which has been the focus of most previous studies and clinical trials) in DOTAP/DOPC CLs. The increase in solubility is likely caused by enhanced molecular affinity between lipid tails and PTX, rather than by the transition in membrane structure from bilayers to inverse cylindrical micelles observed with small-angle X-ray scattering. Importantly, the efficacy of PTX-loaded CLs against prostate cancer cells (their IC50 of PTX cytotoxicity) was unaffected by changing the lipid tails, and toxicity of the CL carrier was negligible. Moreover, efficacy was approximately doubled against melanoma cells for PTX-loaded DLinTAP/DLinPC over DOTAP/DOPC CLs. Our findings demonstrate the potential of chemical modifications of the lipid tails to increase the PTX membrane loading while maintaining (and in some cases even increasing) the efficacy of CLs. The increased PTX solubility will aid the development of liposomal PTX carriers that require significantly less lipid to deliver a given amount of PTX, reducing side effects and costs. 

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.