Machine learning for sensing with a multimode exposed core fiber specklegram sensor.

Fiber specklegram sensors (FSSs) traditionally use statistical methods to analyze specklegrams obtained from fibers for sensing purposes, but can suffer from limitations such as vulnerability to noise and lack of dynamic range. In this paper we demonstrate that deep learning improves the analysis of specklegrams for sensing, which we show here for both air temperature and water immersion length measurements. Two deep neural networks (DNNs); a convolutional neural network and a multi-layer perceptron network, are used and compared to a traditional correlation technique on data obtained from a multimode fiber exposed-core fiber. The ability for the DNNs to be trained against a random noise source such as specklegram translations is also demonstrated.

Optical fiber specklegram sensor for multi-point curvature measurements.

We present a multi-point curvature sensor based on optical fiber specklegram measurements. Apart from the current approaches, the proposed system uses an ordinary multimode fiber excited with visible light as a reflection-type probe. Besides, this method discretizes the waveguide into segments connected by joints and assumes sequential bend events, simplifying the specklegram referencing for correlation analyses and avoiding laborious deep learning processing. Sensor characterization yielded a linear response with ∼1.3∘ resolution for single curvatures, whereas shape prediction experiments in the plane resulted in maximum errors of ∼3.5∘ and ∼5.4mm for angular and linear positioning, respectively. Furthermore, exploratory tests indicated errors <2.3∘ regarding probe curvatures in the space. This research introduces a feasible, straightforward alternative to the available shape sensors, enabling applications in medical probes and soft robotics.

Multimode exposed core fiber specklegram sensor.

The inception of photonic crystal fibers (PCFs) allowed for unprecedented tailoring of waveguide properties for specialty sensing probes. Exposed core microstructured fibers (ECFs) represent a natural evolution of the PCF design for practical liquid and gas sensing. Until now, to the best of our knowledge, only single-mode or few-modes ECFs have been explored. In this Letter, we demonstrate a highly multimode ECF with a lateral access that extends throughout the whole length of the fiber. The ECF is operated as a fiber specklegram sensor for assessing properties of fluids and interrogated using a simple and low-cost setup. The probe exhibits a refractive index resolution and sensitivity of at least 4.6×10-4 refractive index units (RIUs) and -10.97RIU-1, respectively. A maximum temperature resolution up to 0.017°C with a -0.20∘C-1 temperature sensitivity over the 23°C-28°C range and a liquid level sensing resolution up to 0.12 mm with -0.015mm-1 sensitivity over the 0.0-50.0 mm bathed the length range in water.

Evaluation of Silica Nanofluids in Static and Dynamic Conditions by an Optical Fiber Sensor.

This work presents an optical fiber dynamic light scattering sensor capable of simultaneously assessing concentration and flow speed of nanofluids. Silica nanoparticles (189 nm) in water were tested, yielding a sensitivity of 0.78288 × 10³ s-1 for static conditions. Then, the sensor was submitted to situations that simulate spatial concentration changes, offering better results than those obtained by traditional mathematical models. Finally, in flow tests, the light backscattered by the nanoparticles were collected by a fiber probe placed parallel to the streamline, whereas intensity values were processed by artificial neural networks. The sensor provides average errors of 0.09 wt% and 0.26 cm/s for concentration and speed measurements, respectively, and can be further applied to assess different types of nanofluids and inline processes.

Reusable polymer optical fiber strain sensor with memory capability based on ABS crazing.

A reusable memory capable polymer optical fiber (POF) strain sensor is reported. The fiber consists of an acrylonitrile butadiene styrene (ABS) core and polymethylmethacrylate cladding. The memory capability is derived from stress whitening due to crazing of the ABS core, which can be reversed by heating the fiber close to the ABS glass transition temperature. The probe was characterized under transverse compressive load, macrobending, and tensile loading. Testing shows that the optical properties of the fiber can be reversed to near pristine ABS conditions after thermal recovery and that the POF can be used on the accurate assessment of indentation, flexural and tensile loading, static or cyclical, even after removal of the load. The reusability of the proposed sensor combined with a deeper understanding of the memory mechanisms in POFs are of great interest for the development of new large-strain sensors for modern applications.

Perfusion Microfermentor Integrated into a Fiber Optic Quasi-Elastic Light Scattering Sensor for Fast Screening of Microbial Growth Parameters.

This research presents a microfermentor integrated into an optical fiber sensor based on quasi-elastic light scattering (QELS) to monitor and swiftly identify cellular growth kinetic parameters. The system uses a 1310 nm laser light that is guided through single-mode silica optical fibers to the interior of perfusion chambers, which are separated by polycarbonate membranes (470 nm pores) from microchannels, where a culture medium flows in a constant concentration. The system contains four layers, a superior and an inferior layer made of glass, and two intermediate poly(dimethylsiloxane) layers that contain the microchannels and the perfusion chambers, forming a reversible microfluidic device that requires only the sealing of the fibers to the inferior glass cover. The QELS autocorrelation decay rates of the optical signals were correlated to the cells counting in a microscope, and the application of this microsystem to the monitoring of alcoholic fermentation of Saccharomyces cerevisiae resulted in the kinetic parameters of KM = 4.1 g/L and µm = 0.49 h-1. These results agree with both the data reported in the literature and with the control batch test, showing that it is a reliable and efficient biological monitoring system.

Evaluation of image matching techniques for optical fiber specklegram sensor analysis.

A quantitative study of image matching techniques applied to fiber specklegram sensor analysis is presented. The fiber status is modulated by a microbending transducer, so the output speckle field can be correlated to the input displacements. Once acquired and preprocessed, the specklegrams' variations were evaluated according to seven approaches. Although the average intensity did not provide reliable information per se, the correlation and sum of differences methods yielded ∼11 mm-1 and ∼14 mm-1 sensitivities, respectively, within a ∼0.06 mm range and low linearity and hysteresis errors, with enhancement possibility by intensity level cancellation. Moreover, the phase-only correlation and the mutual information metrics provided very high sensitivities (22 mm-1 and 120 mm-1, respectively) for a <0.02 mm range, making these techniques suitable for detecting subtle variations in the fiber status due to physical or chemical stimuli.

Optical fiber specklegram sensor analysis by speckle pattern division.

An optical fiber specklegram sensor interrogation method based on speckle pattern fragmentation is presented. The acquired specklegram images are divided in a square grid, creating sub-images that are further processed by a correlation technique, allowing the quantification of localized changes in the specklegrams. The methodology was tested on the assessment of linear displacements using a microbending transducer, by evaluating different grid sizes. For a 5×5 grid, a 2.53 mm-1 sensitivity over a 0.27 mm range was obtained, representing an extension of 237.5% in comparison to the standard interrogation technique. Therefore, the presented technique allows enhancing the sensor dynamic range without modifying the experimental setup.

Agar-based optical sensors for electric current measurements.

Biodegradable optical waveguides are breakthrough technologies to light delivery and sensing in biomedical and environmental applications. Agar emerges as an edible, soft, low-cost, and renewable alternative to traditional biopolymers, presenting remarkable optical and mechanical characteristics. Previous works introduced agar-made optical fibers for chemical measurements based on their inherent response to humidity and surrounding concentration. Therefore, we propose, for the first time, an all-optical, biodegradable electric current sensor. As flowing charges heat the agar matrix and modulate its refractive index, we connect the optical device to a DC voltage source using pin headers and excite the agar sample with coherent light to project spatiotemporally deviating speckle fields. Experiments proceeded with spheres and no-core fibers comprising 2 wt% agar/water. Once the increasing current stimulates the speckles' motion, we acquire such images with a camera and evaluate their correlation coefficients, yielding exponential decay-like functions whose time constants provide the input amperage. Furthermore, the light granules follow the polarization of the applied voltage drop, providing visual information about the current direction. The results indicate a maximum resolution of [Formula: see text]0.4 [Formula: see text]A for electrical stimuli [Formula: see text] 100 [Formula: see text]A, which fulfills the requirements for bioelectrical signal assessment.

Synthesis of Carbon Nanodots from Sugarcane Syrup, and Their Incorporation into a Hydrogel-Based Composite to Fabricate Innovative Fluorescent Microstructured Polymer Optical Fibers.

Carbon nanodots (CNDs) are interesting materials due to their intrinsic fluorescence, electron-transfer properties, and low toxicity. Here, we report a sustainable, cheap, and scalable methodology to obtain CNDs from sugarcane syrup using a domestic microwave oven. The CNDs were characterized by infrared spectroscopy, dynamic light scattering, atomic force microscopy, absorption, and emission spectroscopies. The CNDs have 3 nm in diameter with low polydispersity and are fluorescent. A fluorescent hydrogel-CNDs composite was obtained using gelatin polypeptide as the polymeric matrix. The new hydrogel-CNDs composite was incorporated in the cavities of a double-clad optical fiber using an innovative approach that resulted in a microstructured polymer optical fiber with intrinsic fluorescence. This work shows a promising alternative for the fabrication of fluorescent materials since the CNDs synthesis is sustainable and environmentally friendly. These CNDs might substitute the rare-earth and other heavy metals of high cost and toxicity, which are usually incorporated in double-clad fibers for applications on lasers, amplifiers, and spectroscopy.

A modular, reversible sealing, and reusable microfluidic device for drug screening.

Preclinical tests for evaluating potential drug candidates using conventional protocols can be exhaustive and high-cost processes. Microfluidic technologies that can speed up this process and allow fast screening of drugs are promising alternatives. This work presents the design, concept, and operational conditions of a simple, modular, and reversible sealing microdevice useful for drug screening. This microdevice allows for the operation of 4 parallel simultaneous conditions and can also generate a diffusive concentration gradient in sextuplicates. We used laminated polydimethylsiloxane (PDMSLAM) and glass as building materials as proof of concept. The PDMSLAM parts can be reused since they can be easily sterilized. We cultured MCF-7 (Michigan Cancer Foundation-7) breast cancer cells. Cells were exposed to a doxorubicin diffusive concentration gradient for 3 h. They were monitored by automated microscopy, and after data processing, it was possible to determine cell viability as a function of doxorubicin concentration. The reversible sealing enabled the recovery of the tested cells and image acquisition. Therefore, this microdevice is a promising tool for drug screening that allows assessing the cellular behavior in dynamic conditions and the recovery of cells for afterward processing and imaging.

Agarose-based structured optical fibre.

Biocompatible and resorbable optical fibres emerge as promising technologies for in vivo applications like imaging, light delivery for phototherapy and optogenetics, and localised drug-delivery, as well as for biochemical sensing, wherein the probe can be implanted and then completely absorbed by the organism. Biodegradable waveguides based on glasses, hydrogels, and silk have been reported, but most of these devices rely on complex fabrication procedures. In this sense, this paper proposes a novel structured optical fibre made of agarose, a transparent, edible material used in culture media and tissue engineering. The fibre is obtained by pouring food-grade agar into a mould with stacked rods, forming a solid core surrounded by air holes in which the refractive index and fibre geometry can be tailored by choosing the agarose solution composition and mould design, respectively. Besides exhibiting practical transmittance at 633 nm in relation to other hydrogel waveguides, the fibre is also validated for chemical sensing either by detecting volume changes due to agar swelling/dehydration or modulating the transmitted light by inserting fluids into the air holes. Therefore, the proposed agarose-based structured optical fibre is an easy-to-fabricate, versatile technology with possible applications for medical imaging and in vivo biochemical sensing.

Integrated Optical Fiber Force Myography Sensor as Pervasive Predictor of Hand Postures.

Force myography (FMG) is an appealing alternative to traditional electromyography in biomedical applications, mainly due to its simpler signal pattern and immunity to electrical interference. Most FMG sensors, however, send data to a computer for further processing, which reduces the user mobility and, thus, the chances for practical application. In this sense, this work proposes to remodel a typical optical fiber FMG sensor with smaller portable components. Moreover, all data acquisition and processing routines were migrated to a Raspberry Pi 3 Model B microprocessor, ensuring the comfort of use and portability. The sensor was successfully demonstrated for 2 input channels and 9 postures classification with an average precision and accuracy of ~99.5% and ~99.8%, respectively, using a feedforward artificial neural network of 2 hidden layers and a competitive output layer.