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Abstract The terahertz frequency range is gaining popularity in security, stealth technology, and the future 6G network communication. For the control of severe terahertz electromagnetic interference (EMI) pollution, frequency‐selective stealth‐capable shielding materials are being explored to mask terahertz signals. For the realization of masking terahertz signals, the robustness, lightweight, and shape‐conformable materials with excellent terahertz EMI shielding/absorption are crucial. Here, the study reports the fabrication of 3D symmetric pyramidal architectural MXene composite films with frequency‐selective stealth performance characteristics via the facile drop casting method. With the high absorption capability of 2D MXene layers, the MXene composite films exhibit substantial terahertz stealth performance. 3D pyramidal microstructure design leads to frequency selective surface‐assisted reflection resonance in the frequency range of 0.6–1.1 THz. The MXene composite film demonstrates an outstanding maximum terahertz shielding effectiveness (SE) of up to 70.4 dB and a specific SE of 0.55 dB µm−1. These terahertz SE values exceed all of those for MX‐based shielding material designs reported in the literature. The investigation will open a new direction toward developing terahertz EMI shielding thin films with easy integration into any surface for stealth capabilities.

Thermoelectric‐based waste heat recovery requires efficient materials to replace conventional non‐eco‐friendly Te‐ and Pb‐based commercial devices. Ternary copper chalcogenide‐based famatinite (Cu3SbS4) compound is one of the practical substitutes for traditional thermoelectric materials. However, the pristine Cu3SbS4 inherits poor structural complexion, large thermal conductivity, and low power conversion efficiency. To develop high‐efficiency Cu3SbS4, InSb nanoinclusions are incorporated via high‐energy ball milling followed by the hot‐press densification method. Incorporating InSb nanoinclusions to lower thermal conductivity via phonon scattering while increasing the thermopower via a carrier energy filtering process. The thermoelectric performance (ZT) of ≈0.4 at 623 K is obtained in Cu3SbS4‐3 mol% InSb nanocomposite, which is ≈140% higher than pure Cu3SbS4. Both mechanical and thermal stability are improved by grain boundary hardening and dispersion strengthening. Thus, a facile nanostructured Cu3SbS4 with added InSb nanoinclusions is delivered as a highly efficient, eco‐friendly, structurally‐, thermally‐, and mechanically‐stable material for next‐generation thermoelectric generators.

Abstract For decades, the widespread application of thermoelectric generators has been plagued by two major limitations: heat stagnation in its legs, which limits power conversion efficiency, and inherent brittleness of its constituents, which accelerates thermoelectric generator failure. While notable progress has been made to overcome these quintessential flaws, the state-of-the-art suffers from an apparent mismatch between thermoelectric performance and mechanical toughness. Here, we demonstrate an approach to potentially enhance the power conversion efficiency while suppressing the brittle failure in thermoelectric materials. By harnessing the enhanced thermal impedance induced by the cellular architecture of microlattices with the exceptional strength and ductility (>50% compressive strain) derived from partial carbonization, we fabricate three-dimensional (3D) architected thermoelectric generators that exhibit a specific energy absorption of ~30 J g−1 and power conversion efficiency of ~10%. We hope our work will improve future thermoelectric generator fabrication design through additive manufacturing with excellent thermoelectric properties and mechanical robustness.

Abstract Neuromorphic artificial intelligence systems are the future of ultrahigh performance computing clusters to overcome complex scientific and economical challenges. Despite their importance, the advancement in quantum neuromorphic systems is slow without specific device design. To elucidate biomimicking mammalian brain synapses, a new class of quantum topological neuristors (QTN) with ultralow energy consumption (pJ) and higher switching speed (µs) is introduced. Bioinspired neural network characteristics of QTNs are the effects of edge state transport and tunable energy gap in the quantum topological insulator (QTI) materials. With augmented device and QTI material design, top notch neuromorphic behavior with effective learning‐relearning‐forgetting stages is demonstrated. Critically, to emulate the real‐time neuromorphic efficiency, training of the QTNs is demonstrated with simple hand gesture game by interfacing them with artificial neural networks to perform decision‐making operations. Strategically, the QTNs prove the possession of incomparable potential to realize next‐gen neuromorphic computing for the development of intelligent machines and humanoids.