In this article, we designed and studied silicon oxynitride (SiON) microring-based photonic structures for biosensing applications. We designed waveguides, directional couplers, and racetrack resonators in order to measure refractive index changes smaller than 10⁻⁶ refractive index units (RIU). We tested various samples with different SiON refractive indexes as well as the waveguide dimensions for selecting the sensor with the best performance. Propagation losses and bending losses have been measured on test structures, along with a complete characterization of the resonator’s performances. Sensitivities and limit of detection (LOD) were also measured using glucose-water solutions and compared with expected results from simulations. Finally, we functionalized the resonator and performed sensing experiments with Aflatoxin M1 (AFM1). We were able to detect the binding of aflatoxin for concentrations as low as 12.5 nm. The results open up the path for designing cost-effective biosensors for a fast and reliable sensitive analysis of AFM1 in milk.

We report on the investigation and implementation of a lumped-component, radio-frequency resonator used in a cryogenic vacuum environment to drive an ion trap. The resonator was required to achieve the voltages necessary to trap (∼100 V), while dissipating very little power. Ultimately, for an input voltage of 1.35 V, a voltage gain of 100 was measured at 5.7 K, using a design which dissipated only 18 mW. The resonator operated at a frequency of 7.64 MHz and had a Q of 700. Single 40Ca+ ions were confined in a trap driven by this device, providing proof of successful resonator operation at low temperature.

In this paper, we report on the measurement and modeling of enhanced optical refractometric sensors based on whispering gallery modes. The devices under test are optical microresonators made of silicon nitride on silicon oxide, which differ in their sidewall inclination angle. In our approach, these microresonators are vertically coupled to a buried waveguide with the aim of creating integrated and cost-effective devices. Device modeling shows that the optimization of the device is a delicate balance of the resonance quality factor and evanescent field overlap with the surrounding environment to analyze. By numerical simulations, we show that the microdisk thickness is critical to yield a high figure of merit for the sensor and that edge inclination should be kept as high as possible. We also show that bulk-sensing figures of merit as high as 1600 RIU^-1 (refractive index unit) are feasible.

Fusion laser cutting allows for the processing of metallic sheets with high-edge quality, provided that process parameters are selected accurately. To guarantee quality while being robust to various existing uncertainties, velocity is typically set conservatively. This ensures complete cuts with limited defects such as low dross. However, such an approach significantly impacts productivity because the cutting velocity is empirically limited, often more than necessary. Literature has demonstrated that real-time dross estimation using the analysis of process emission images with Machine Learning algorithms, combined with control-based approaches, can effectively maximize productivity, while maintaining reference quality conditions. However, to date, this technique has been demonstrated only on linear cuts, limiting its industrial applicability. As a matter of fact, variations in the propagation of the process emission light within the coaxial monitoring chain, as well as intrinsic variations due to different cutting directions, significantly impact the performances of the estimation algorithm. This work presents an effective approach to extend the applicability of the velocity-based control strategy to multidirectional and curved geometries. A Neural Network was trained and tested to predict dross formation during linear cuts in different directions of 5 mm thick AISI304. The model predictions are robust, regardless of the direction of the cuts, with R2 values above 70% and limited Root Mean Square Error. The control architecture was then designed and tested on circular trajectories with variable curvatures, demonstrating resilient performance in terms of dross prediction and regulation. Finally, the controlled cut was tested on representative geometries, proving its industrial applicability.