High-performance aerogels featuring exceptional thermal insulation and integrated motion-sensing capabilities are pivotal for the advancement of next-generation flexible electronics and protective systems in extreme environments. However, achieving a synergistic balance between multi-functional integration within a single lightweight framework remains a significant challenge due to the structural limitations of conventional isotropic materials. Inspired by the hierarchical porous structures of aquatic plants, we herein report an anisotropic polyimide/reduced graphene oxide aerogel (PI/rGOA) with a biomimetic architecture, fabricated via a synergistic strategy of directional freezing and interfacial regulation. This approach combines directional freezing with hydrogen-bonding-assisted interfacial regulation between polyimide nanofibers (PINF) and graphene oxide (GO), enabling the construction of highly ordered anisotropic channels. The resulting PI/rGOA demonstrates superior thermal regulation, maintaining substantial temperature gradients at both −190 and 200 °C, while exhibiting rapid self-extinguishing behavior and structural integrity under a 1300 °C flame. Furthermore, the simultaneous regulation of heat conduction and infrared radiation enables effective stealth performance. The sensor encapsulated with polydimethylsiloxane (PDMS) provides high sensitivity and stability over 500 cycles. Overall, this work provides a versatile paradigm for developing multifunctional and structurally ordered materials, offering a promising solution for aerospace, defense, and wearable electronic devices.

The translation of self-powered electrochemical sensors from laboratory demonstrations to regulatory-compliant field deployment is impeded by two coupled barriers: (1) liquid electrolyte leakage and evaporation cause baseline drift under fluctuating conditions, and (2) black-box machine learning models lack uncertainty quantification and transparency for compliance testing. We report a co-designed quasi-solid-state Zn-air fuel cell platform that addresses both barriers through synergistic integration of materials engineering and algorithmic innovation, using acetamiprid (ACE) as a model pollutant. A physically cross-linked Gel-Car-PVA hydrogel electrolyte eliminates leakage and evaporation, suppressing signal attenuation to below 0.20% over 30 days across -20 to 25 ℃, resolving the baseline instability that undermines field sensor credibility. Parallel to this, an uncertainty-calibrated one-dimensional convolutional neural network (1D-CNN) with Monte Carlo Dropout and SHapley Additive exPlanations (SHAP) delivers prediction intervals with quantified confidence mechanistic feature attribution, meeting regulatory transparency standards. This co-design enables a trimodal transduction strategy wherein aptamer-triggered release of MXene@Au nanohybrids generates electrochemical, photothermal, and colorimetric responses from a single assay, decoded by the interpretable AI framework. Benchmarking against liquid chromatography-tandem mass spectrometry (LC-MS/MS) in tomato, soil, and field water samples, the platform achieves quantitative agreement (R² = 0.991, slope = 0.83), confirming on-site screening reliability. This paradigm bridges the portability-accuracy gap in field-deployable trace pollutant surveillance, with implications for agricultural runoff and drinking water protection.