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.