Electronics‐Free Wearable Platform for Computationally Guided Sweat Biomarker Recognition

ABSTRACT Wearable colorimetric sensors are powerful tools for noninvasive health monitoring, yet they frequently lack the necessary chemical specificity when operating in complex biofluids. Here, we report an electronics‐free microfluidic wearable platform that achieves seamlessly integrated “acquisition‐transduction‐decoding” of sweat biomarkers. For active information acquisition, we engineered a highly porous iron‐vermiculite‐carbon composite acting as a thermal actuator. Its optimized exothermic kinetics autonomously stimulate targeted sweat secretion (∼37°C) without external power. To ensure high‐fidelity signal transduction, we established an optical array driven by computationally screened supramolecular receptors. We employed density functional theory (DFT) to map the physicochemical fingerprints of target analytes (K + , Ca 2 + , and uric acid), thereby rationally selecting macrocycles with precisely matched electrostatic and topological profiles and intelligently coupling them with target‐specific chromogenic reporters (gold nanoparticles, dibromo‐core‐substituted naphthalene diimide (Br 2 ‐cNDI), and resorufin, respectively) for direct optical signal transduction. Finally, for intelligent decoding, a K‐nearest neighbor (KNN) machine‐learning algorithm is applied to decrypt the multiplexed multidimensional RGB outputs, achieving robust semi‐quantitative profiling with exceptional classification accuracy (up to 100%). This supramolecularly engineered wearable platform establishes a computation‐driven approach for selective, noninvasive detection of low‐abundance sweat biomarkers, and holds strong potential for personalized preventive healthcare.