Magnetocaloric materials undergoing reversible phase transitions are highly desirable for magnetic refrigeration applications. (Mn,Fe)₂(P,Si) alloys exhibit a giant magnetocaloric effect accompanied by a magnetoelastic transition, while the noticeable irreversibility causes drastic degradation of the magnetocaloric properties during consecutive cooling cycles. In the present work, we performed a comprehensive study on the magnetoelastic transition of the (Mn,Fe)₂(P,Si) alloys by high-resolution transmission electron microscopy, in situ field- and temperature-dependent neutron powder diffraction as well as density functional theory calculations (DFT). We found a generalized relationship between the thermal hysteresis and the transition-induced elastic strain energy for the (Mn,Fe)₂(P,Si) family. The thermal hysteresis was greatly reduced from 11 to 1 K by a mere 4 at.% substitution of Fe by Mo in the Mn_1.15Fe_0.80P_0.45Si_0.55 alloy. This reduction is found to be due to a strong reduction in the transition-induced elastic strain energy. The significantly enhanced reversibility of the magnetoelastic transition leads to a remarkable improvement of the reversible magnetocaloric properties, compared to the parent alloy. Based on the DFT calculations and the neutron diffraction experiments, we also elucidated the underlying mechanism of the tunable transition temperature for the (Mn,Fe)₂(P,Si) family, which can essentially be attributed to the strong competition between the covalent bonding and the ferromagnetic exchange coupling. The present work provides not only a new strategy to improve the reversibility of a first-order magnetic transition but also essential insight into the electron-spin-lattice coupling in giant magnetocaloric materials.

The latest progress of the multiple-input multiple-output (MIMO) radar system developed for small drones detection at Beijing Institute of Technology is presented herein. A low-cost S-band MIMO scanning radar system is designed for the detection of small drones. A practical design optimisation and implementation of a sparse array covering the whole airspace, and a target-based MIMO radar array calibration method to improve the signal-to-noise ratio of the target are proposed. An experiment in the microwave chamber was conducted to verify the designed sparse-array beam-pattern, and measurement campaigns for small drones were performed to test the system performance where the radar achieved detection and tracking of a small DJI-Phantom 4 drone at a range of up to 5 km. Target features, including micro-Doppler and radar cross-section signatures were extracted to assist target classification. The experimental results indicate that the system has promising capability in small drones detection and tracking, despite the challenges of low dwell time on targets due to the scanning nature of the system.