Near-infrared (NIR) phosphor-converted light-emitting diodes/laser diodes (LEDs/LDs) are prospective lighting sources for NIR spectroscopy. However, developing a NIR phosphor material with desired thermal robustness and high photoelectric efficiency is a crucial challenge for its applications. In this work, based on the cationic radius matching effect, a series of (Lu,Y)₃(Al,Sc,Cr)₂Al₃O₁₂ NIR phosphor ceramics (LuYScCr NIR-PCs) were fabricated via vacuum sintering. An excellent thermal stability (95%@150 ^oC) was obtained in the prepared NIR-PC, owing to its weak electron phonon coupling effect (small Huang Rhys factor). Being excited at 460 nm, the NIR-PCs realized a broadband emission (650-850 nm) with an IQE of 60.68 %. Combining NIR-PCs with LED/LD chips, the maximum output power of the encapsulated LED prototype was 447 mW @ 300 mA with a photoelectric efficiency of as high as 18.6 % @ 180 mA, and the maximum output power of the LD prototype was 814mW @ 2.5 A. The working temperatures of the NIR-PCs were 70.8 ^oC @ 300 mA (LED) and 102.8 ^oC @ 3 A (LD), respectively. Finally, the prepared NIR-PCs applied in the food detection were verified in this study, demonstrating its anticipated application prospects in the future.

This article presents an adaptive model predictive control (AMPC) approach to address increased body vibration resulting from unbalanced electromagnetic forces for semi-active suspension (SAS) systems in-wheel-motor-drive electric vehicles (IWMD EVs). In particular, the proposed framework consists of three key steps. Firstly, the Magneto-Rheological (MR) shock absorber characteristics have been analyzed and compared with the simulation and bench testing. Then, the dynamic model of a half-vehicle SAS system with IWMD EVs is obtained. Secondly, an AMPC preview control framework is formulated based on the road roughness and distance information obtained from binocular vision. The size of the control step is dynamically adjusted based on road preview data, and the weight matrix within the objective function is adaptively modified through the genetic algorithm according to the acceleration of the mass sprung by the suspension. This SAS control system enables optimal suspension adjustments in various road conditions. Simulation and hardware-in-the-loop experiment studies verify that the proposed framework performs better than existing methods in typical road conditions. The proposed AMPC control strategy significantly enhances ride comfort in IWMD EVs.