Multifunctional flexible optical waveguide sensor: on the bioinspiration for ultrasensitive sensors development

Abstract

This paper presents the development of a bioinspired multifunctional flexible optical sensor (BioMFOS) as an ultrasensitive tool for force (intensity and location) and orientation sensing. The sensor structure is bioinspired in orb webs, which are multifunctional devices for prey capturing and vibration transmission. The multifunctional feature of the structure is achieved by using transparent resins that present both mechanical and optical properties for structural integrity and strain/deflection transmission as well as the optical signal transmission properties with core/cladding configuration of a waveguide. In this case, photocurable and polydimethylsiloxane (PDMS) resins are used for the core and cladding, respectively. The optical transmission, tensile tests, and dynamic mechanical analysis are performed in the resins and show the possibility of light transmission at the visible wavelength range in conjunction with high flexibility and a dynamic range up to 150 Hz, suitable for wearable applications. The BioMFOS has small dimensions (around 2 cm) and lightweight (0.8 g), making it suitable for wearable application and clothing integration. Characterization tests are performed in the structure by means of applying forces at different locations of the structure. The results show an ultra-high sensitivity and resolution, where forces in the μN range can be detected and the location of the applied force can also be detected with a sub-millimeter spatial resolution. Then, the BioMFOS is tested on the orientation detection in 3D plane, where a correlation coefficient higher than 0.9 is obtained when compared with a gold-standard inertial measurement unit (IMU). Furthermore, the device also shows its capabilities on the movement analysis and classification in two protocols: finger position detection (with the BioMFOS positioned on the top of the hand) and trunk orientation assessment (with the sensor integrated on the clothing). In both cases, the sensor is able of classifying the movement, especially when analyzed in conjunction with preprocessing and clustering techniques. As another wearable application, the respiratory rate is successfully estimated with the BioMFOS integrated into the clothing. Thus, the proposed multifunctional device opens new avenues for novel bioinspired photonic devices and can be used in many applications of biomedical, biomechanics, and micro/nanotechnology.

Multifunctional Integration of Optical Fibers and Nanomaterials for Aircraft Systems

“…In this approach, there is a microscale proof mass and a silicon frame to create the accelerometer structure, where the displacement and acceleration are obtained from the spectral features’ variation of the cavity, with the possibility of tuning the accelerometer parameters (such as proof mass and stiffness) to achieve high resonant frequencies and small noise. In another MOEMS accelerometer, the bioinspired shape was proposed in [ 143 ], where a battery was used as proof mass and the sensors were embedded in a transparent web-like structure for movement analysis and highly sensitive displacement measurements.…”

Recent Development in Metasurfaces: A Focus on Sensing Applications

“…There are many different types of EM sensors described in the literature, including interferometers [ 39 ], waveguides [ 40 , 41 ], gratings [ 42 , 43 , 44 ], cavities/resonators [ 45 , 46 , 47 ], SPR-based structures [ 48 ], and, most recently, all-dielectric MMs [ 49 ]. They exhibit some benefits, but they also have several downsides and restrictions, including electrically massive structures, high losses, dispersive behavior, polarization dependency, and difficulty in manufacturing [ 50 ].…”

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

“…Due to improvement of spatial and temporal resolution of satellite imagery and availability of synthetic aperture radar (SAR) dataset, disaster mapping based on RS data has been converted into a hot topic [33,34]. Meanwhile, algorithm development and computer sciences revolutionized the accuracy and time of different sensors' data processing in various fields such as RS, prediction of natural disaster, feature detection, and biomedical [35,36]. The machine learning (ML) algorithms are among the most popular methods in the image processing and computer vision.…”