Guodong Li

Electromagnetic wave pollution has seriously affected human body health owing to rapid technological development, such as 5G/6G communication networks. The demand for wearable materials with high‐performance electromagnetic protection and excellent mechanical properties is urgent, but still lacks reasonable preparing strategies to realize multifunctional features. Here, we develop a dual‐network structural strategy to construct a high‐performance ionogel fiber by using a scalable dry‐spinning method. In this approach, semi‐crystalline hydroxypropyl cellulose (HPC) combined with poly thioctic acid (PTA) serves as a structural backbone by hydrogen bonding network; simultaneously, MXene nanosheets and IL [EMI][ES] build an interconnected 3D conductive network, imparting functional properties to the backbone matrix. The ionogel fiber exhibits excellent EMI shielding effectiveness (60.6 dB), high strength (9.5 MPa), superior elongation (>200%), good self‐healing, and recyclability. Notably, we found that the electromagnetic shielding performance of the fabric woven from the ionogel fibers increases by up to 7.5% within a strain range of ≤10.0%. This enhancement can be due to a stretch‐induced orientation of the MXene nanosheets, which optimizes the internal conductive network of the ionogel. This work offers a promising route toward next‐generation, wearable electromagnetic protection materials adaptable to both static and dynamic environments.

Enhancing the heat resistance of cast Al-Si alloys is a critical prerequisite for expanding their service scope in automotive and aerospace sectors. To address the high cost and inherent inefficiency of conventional trial-and-error approaches for complex alloy composition design, this study proposes an interpretable machine learning strategy for developing heat-resistant cast aluminum alloys. This strategy employs a multi-step filtering process to identify synergistic microalloying element combinations, and leverages SHapley Additive exPlanations (SHAP) to elucidate the key intrinsic determinants and underlying mechanisms governing the high-temperature ultimate tensile strength (HTUTS) and high-temperature elongation (HTEL) of Al alloys, thus imparting explicit physical interpretability to the prediction model. Building on this, a novel heat-resistant cast Al-Si alloy with the composition Al-9.10Si-3.50Cu-0.50Mg-0.35Mn-0.25Zr-0.20Ti is rationally designed via coupling with the multi-objective coati optimization algorithm (MOCOA). The designed alloy delivers an exceptional HTUTS of 192.2 MPa at 300 ℃, outperforming all previously reported cast Al-Si alloys to date. Microstructural characterization confirms that the co-addition of Zr, Mn and Ti promotes the formation of thermally stable intermetallic phases, namely Al15(Fe, Mn)3Si2, (Al, Si)3ZrTi and Al3Zr. These phases synergistically impede dislocation motion at elevated temperatures, constituting the dominant mechanism underlying the remarkable enhancement in high-temperature strength. Meanwhile, the grain refinement induced by Zr and Ti, coupled with Mn-mediated mitigation of detrimental effects from brittle Fe-rich phases, accounts for the well-retained ductility. Overall, the findings of this work demonstrate the strong feasibility and high reliability of machine learning in multi-objective, performance-oriented alloy design, providing an efficient and interpretable methodological reference for the development of advanced heat-resistant cast Al-Si alloy.

Ultrafine-grained (UFG) 2024 aluminum alloys with a heterogeneous lamellar structure were fabricated by powder metallurgy routes, including spark plasma sintering and low temperature hot extrusion. Subsequent deliberate short-duration solution (495 °C/25 min) and aging (150 °C/10 h) treatments preserved the UFGs while inducing dense nano-sized α-AlMnSi precipitates, resulting in superior mechanical properties, i.e., yield strength of 391 MPa, ultimate tensile strength of 522 MPa and uniform elongation of 12.7%. The synergistic strength-ductility enhancement originates from the precipitation strengthening of nano-sized α-AlMnSi precipitates, grain boundary strengthening of UFGs as well as the pinning effects of MgO particles at grain boundaries. The study highlights an effective approach to achieving superior strength and ductility in aluminum alloys through optimized processing and heat treatment, offering valuable insights for the development of high-performance aluminum alloys in diverse industrial applications.