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Supramolecular polymer blends (SPBs) represent a versatile class of polymers whose morphology directly determines their macroscopic properties. However, rational design of SPBs remains hindered by the lack of predictive models describing how molecular features and intermolecular interactions determine morphology. Here, we report a data-driven high-throughput workflow integrating modular synthesis, robotic sample formulation and processing, automated morphology characterization, and machine learning (ML) for SPBs discovery. Using a plug-and-play modular synthetic strategy, 33 hydrogen-bonding end-functional homopolymer precursors were prepared and orthogonally paired to fabricate 260 SPBs within one day. A custom automated atomic force microscopy (AFM) protocol enabled systematic morphological characterization, producing 2340 images with little human intervention. Average phase separation sizes (e.g. domain spacings) was extracted from processed AFM data using multiple complementary approaches and applied to ML model training. Leveraging the high-throughput sample formation and characterization, a high-quality database was curated for SPBs, allowing training of ML models. Guided by support vector regression (SVR) model, target morphologies of 50, 100, and 150 nm were successfully predicted and experimentally validated. This work demonstrates the potential of coupling high-throughput experimentation with ML to accelerate polymer blends phase discovery, providing one of the first large-scale, experimentally derived datasets specifically designed for supramolecular polymer research. 

Personal thermal management textile/wearable is an effective strategy to expand the indoor temperature setpoint range to reduce a building’s energy consumption. Usually, textiles/wearables that were engineered for controlling conduction, convection, radiation, or sweat evaporation have been developed separately. Here, we demonstrate a multimodal adaptive wearable with moisture-responsive flaps composed of a nylon/metal heterostructure, which can simultaneously regulate convection, sweat evaporation, and mid-infrared emission to accomplish large and rapid heat transfer tuning in response to human perspiration vapor. We show that the metal layer not only plays a crucial role in low-emissivity radiative heating but also enhances the bimorph actuation performance. The multimodal adaptive mechanism expands the thermal comfort zone by 30.7 and 20.7% more than traditional static textiles and single-modal adaptive wearables without any electricity and energy input, making it a promising design paradigm for personal heat management. 

Abstract Precision healthcare relies upon ubiquitous biofeedback to optimize therapy individually for nuanced and dynamic needs. However, grand challenges reside in the lack of soft, highly personalizable monitors that are scalable in manufacturing and reversibly interchangeable upon the evolution of needs. Herein, a customizable soft wearable platform is presented that can seamlessly integrate diverse functional modules, including physical and biochemical sensors, stimulators, and energy storage devices, tailored to various health monitoring scenarios, while can self‐repair after certain mechanical damage. The platform supports versatile physiological sensing and therapeutic intervention due to its compatibility with wide‐ranging functional nanomaterials. A bilayer microporous foam embedded in the gel improves sweat management for comfortable and reliable on‐body biomarker monitoring. Furthermore, flexible self‐healing zinc‐air batteries using ion gel electrolytes provide opportunities for self‐powered, closed‐loop systems. On‐body demonstrations validate the platform's capability to monitor physiological and metabolic states under real‐world conditions. This work provides a scalable and adaptable materials‐based solution for real‐time personalized health monitoring, advancing wearable bioelectronics to meet evolving healthcare demands.