The detection of the amount of aflatoxin M1 (AFM1) in milk is crucial for food safety. Here, we utilize a fiber optic (FO) localized surface plasmon resonance (LSPR) biosensor by constructing gold nanoparticle (AuNP) multimers, in which the nanogaps amplified the LSPR signal by the hot spot effect, and achieved a highly sensitive detection of f AFM1. Through the optimization of parameter conditions for the fabrication of the sensor and detection system, a high performance result from the FO LSPR biosensor was obtained, and the method for AFM1 detection was established, with a wide detection range of 0.05-100 ng/mL and a low limit of detection (LOD) of 0.04 ng/mL, and it has been successfully validated with the actual sample milk. Therefore, it is a good strategy to fabricate highly sensitive FO LSPR sensors for detecting AFM1 by constructing AuNP multimers, and this approach is suitable for developing other biosensors.

Modern vehicles are using control and safety driving algorithms fed by various evaluations such as wheel speeds or road environmental conditions. Wheel load evaluation could be useful for such algorithms, particularly for extreme vehicle loading or uneven loads. For now, smart tires are only equipped by tire pressure monitoring systems (TPMS) and temperature sensors. Manufacturers are still working on in-tire sensors, such as load sensors, to create the next generation of smart tires. The present work aims at demonstrating that a static tire instrumented with an internal optical fiber allows the wheel load estimation for every wheel angular position. Experiments have been carried out with a static tire loaded with a hydraulic press and instrumented with both an internal optical fiber and an embedded laser. Load estimation is performed both from tire deflection and contact patch length evaluations. For several applied loads from 2800 to 4800 N, optical fiber load estimation is realized with a relative error of 1% to 3%, almost as precisely as that with the embedded laser, but with the advantage of the load estimation regardless of the wheel angular position. In perspective, the developed methodology based on an in-tire optical fiber could be used for continuous wheel load estimation for moving vehicles, benefiting control and on-board safety systems.