Fe-based high-entropy alloy with excellent mechanical properties enabled by nanosized precipitates and heterogeneous grain distribution

High-entropy alloys(HEAs)consisting of CoCrFeNiAlTi systems,with a face-centered cubic(FCC)matrix reinforced by ordered L12 precipitates,have demonstrated exceptional strength-ductility combinations.However,the current compositional design of HEAs heavily relies on high Ni and Co contents,compro-mising the balance between properties and cost.Thus,it is crucial to optimize the cost-performance trade-offby fine-tuning the range of Fe,Co,and Ni,while maintaining excellent strength-ductility com-bination.In this study,we propose a novel Fe-based HEA with nanosized precipitates and a heteroge-neous grain distribution,achieving a strength-ductility combination comparable to state-of-the-art Ni-or Co-based HEAs.The alloy benefits from both precipitation hardening and hetero-deformation-induced strengthening attributed to the heterogeneous grain distribution,resulting in excellent yield strength of 1433 MPa,tensile strength of 1599 MPa,and ductility of 22%.The microstructural evolution and its in-fluence on mechanical properties are unraveled with respect to the observation of precipitate-dislocation interaction and hetero-deformation-induced stress(HDI stress)evaluation.This study suggests that the challenge of balancing properties and cost can be addressed through optimized compositional and mi-crostructural design.

Deep-learning-based gas identification by timevariant illumination of a single micro-LEDembedded gas sensor

Electronic nose(e-nose)technology for selectively identifying a target gas through chemoresistive sensors has gained much attention for various applications,such as smart factory and personal health monitoring.To overcome the crossreactivity problem of chemoresistive sensors to various gas species,herein,we propose a novel sensing strategy based on a single micro-LED(μLED)-embedded photoactivated(μLP)gas sensor,utilizing the time-variant illumination for identifying the species and concentrations of various target gases.A fast-changing pseudorandom voltage input is applied to the μLED to generate forced transient sensor responses.A deep neural network is employed to analyze the obtained complex transient signals for gas detection and concentration estimation.The proposed sensor system achieves high classification(~96.99%)and quantification(mean absolute percentage error~31.99%)accuracies for various toxic gases(methanol,ethanol,acetone,and nitrogen dioxide)with a single gas sensor consuming 0.53 mW.The proposed method may significantly improve the efficiency of e-nose technology in terms of cost,space,and power consumption.