Continuous-Conductivity-Gradient All-Organic Aerogels with Machine-Learning-Assisted Design toward Ultrabroadband, Ultralow-Reflection Electromagnetic Shielding.

Abstract

Ultralow-reflection electromagnetic interference (EMI) shielding across broad frequency ranges remains elusive as low reflection and low transmission are rarely achieved simultaneously, particularly in lightweight aerogels amenable to scalable manufacturing. Here, a continuous-conductivity-gradient (CCG) aerogel with machine-learning (ML)-assisted optimization is developed via diffusion-controlled in situ oxidative polymerization of pyrrole within an as-prepared, mechanically resilient porous aramid nanofiber scaffold, followed by an energy-efficient, scalable ambient-pressure-drying strategy. The resulting CCG aerogel integrates a continuous through-thickness gradient of polypyrrole (PPy) with a highly porous architecture, enabling a smooth impedance transition and progressive bulk microwave attenuation for ultrabroadband, ultralow-reflection EMI shielding. The optimized CCG aerogel delivers an effective absorption-dominated frequency bandwidth of 29.76 GHz spanning 10.24-40 GHz, with an EMW reflectivity below 0.1, while maintaining an EMI shielding effectiveness above 40 dB across the ultrabroadband frequency range of 8.2-40 GHz, surpassing the shielding performance of existing EMI shielding materials. Mechanistic analyses reveal that the continuous gradient couples efficient front-surface impedance matching with progressive internal dissipation, thereby circumventing the impedance discontinuities inherent to discrete multilayers. Overall, this ML-assisted strategy integrates novel electromagnetic and structural design with robust, scalable all-organic aerogel manufacturing, offering a general platform for ultrabroadband, ultrahigh-absorption, ultralow-reflection EMI shielding across diverse material systems.