In this work, we present a study on photonic biosensors based on Si3N4 asymmetric Mach-Zehnder Interferometers (aMZI) for Aﬂatoxin M1 (AFM1) detection. AFM1 is an hepatotoxic and a carcinogenic toxin present in milk. The biosensor is based on an array of four Si3N4 aMZI that are optimized for 850nm wavelength. We measure the bulk Sensitivity (S) and the Limit of Detection (LOD) of our devices. In the array, three devices are exposed and have very similar sensitivities. The fourth aMZI, which is covered by SiO2, is used as an internal reference for laser (a VCSEL) and temperature ﬂuctuations. We measured a phase sensitivity of 14300±400 rad/RIU. To characterize the LOD of the sensors, we measure the uncertainty of the experimental readout system. From the measurements on three aMZI, we observe the same value of LOD, which is ≈ 4.5×10−7 RIU. After the sensor characterization on homogeneous sensing, we test the surface sensing performances by ﬂowing speciﬁc Aﬂatoxin M1 and non-speciﬁc Ochratoxin in 50 mM MES pH 6.6 buﬀer on the top of the sensors functionalized with Antigen-Recognising Fragments (Fab’). The diﬀerence between speciﬁc and non-speciﬁc signals shows the speciﬁcity of our sensors. A moderate regeneration of the sensors is obtained by using glycine solution.
The automatic detection of geometric features, such as edges and creases, from objects represented by 3D point clouds (e.g., LiDAR measurements, Tomographic SAR) is a very important issue in different application domains including urban monitoring and building reconstruction. A limitation of many methods in the literature is that they rely on rasterization or interpolation of the original grid, with consequent potential loss of detail. Recently, a second-order variational model for edge and crease detection and surface regularization has been presented in literature and succesfully applied to DSMs. In this paper we address the generalization of this model to unstructured grids. The model is based on the Blake-Zisserman energy and allows to obtain a regularization of the original data (noise reduction) which does not affect crucial regions containing jumps and creases. Specifically, we focus on the detection of these features by means of two auxiliary functions that are computable by solving specific differential equations. Results obtained on LiDAR data by solving the equations via Finite Element Method are presented.