A Machine Learning Specklegram Wavemeter (MaSWave) Based On A Short Section Of Multimode Fiber As The Dispersive Element

Wavemeters are very important for precise and accurate measurements of both pulses and continuous-wave optical sources. Conventional wavemeters employ gratings, prisms, and other wavelength-sensitive devices in their design. Here, we report a simple and low-cost wavemeter based on a section of multimode fiber (MMF). The concept is to correlate the multimodal interference pattern (i.e., speckle patterns or specklegrams) at the end face of an MMF with the wavelength of the input light source. Through a series of experiments, specklegrams from the end face of an MMF as captured by a CCD camera (acting as a low-cost interrogation unit) were analyzed using a convolutional neural network (CNN) model. The developed machine learning specklegram wavemeter (MaSWave) can accurately map specklegrams of wavelengths up to 1 pm resolution when employing a 0.1 m long MMF. Moreover, the CNN was trained with several categories of image datasets (from 10 nm to 1 pm wavelength shifts). In addition, analysis for different step-index and graded-index MMF types was carried out. The work shows how further robustness to the effects of environmental changes (mainly vibrations and temperature changes) can be achieved at the expense of decreased wavelength shift resolution, by employing a shorter length MMF section (e.g., 0.02 m long MMF). In summary, this work demonstrates how a machine learning model can be used for the analysis of specklegrams in the design of a wavemeter

One-Dimensional Sensor Learns to Sense Three-Dimensional Space

A sensor system with ultra-high sensitivity, high resolution, rapid response time, and a high signal-to-noise ratio can produce raw data that is exceedingly rich in information, including signals that have the appearances of noise . The noise feature directly correlates to measurands in orthogonal dimensions, and are simply manifestations of the off-diagonal elements of 2nd-order tensors that describe the spatial anisotropy of matter in physical structures and spaces. The use of machine learning techniques to extract useful meanings from the rich information afforded by ultra-sensitive one-dimensional sensors may offer the potential for probing mundane events for novel embedded phenomena. Inspired by our very recent invention of ultra-sensitive optical-based inclinometers, this work aims to answer a transformative question for the first time: can a single-dimension point sensor with ultra-high sensitivity, fidelity, and signal-to-noise ratio identify an arbitrary mechanical impact event in three-dimensional space? This work is expected to inspire researchers in the fields of sensing and measurement to promote the development of a new generation of powerful sensors or sensor networks with expanded functionalities and enhanced intelligence, which may provide rich n-dimensional information, and subsequently, data-driven insights into significant problems

Solid State NMR Spectroscopy/Imaging in Situ Measuring Devices and Methods for Calibration and Determining One or More Quantitative Properties of a Target Sample

In situ measuring devices, methods of making the same, and methods of using the same are provided herein. The in situ measuring devices can include a capillary tube having a reference material sealed inside the capillary tube, where the capillary tube is positioned inside of a solid state or MAS NMR rotor. A target sample can also be positioned in the interior of the solid state or MAS NMR rotor but is sequestered from the reference material by a capillary tube wall. The in situ measuring devices can be used in solid state MAS NMR spectroscopy to quantify one or more parameters of a target sample, such as the quantity of a sample, chemical identity of a sample, or temperature of a sample

NMR Studies of Loaded Microspheres

Porous-wall hollow glass microspheres (PWHGMs) are a novel form of glass materials that consist of 1-μm-thick porous silica shells, 20-100 μm in diameter, with a hollow cavity in the center. Utilizing the central cavity for material storage and the porous walls for controlled release is a unique combination that renders PWHGMs a superior vehicle for targeted drug delivery. In this study, NMR spectroscopy was used to characterize PWHGMs for the first time. A vacuum-based loading system was developed to load PWHGMs with various compounds followed by a washing procedure that uses solvents immiscible with the target material. Immiscible binary model systems (chloroform/water, n-dodecane/water), as well as the hydrolysis of isopropyl acetate, were investigated to obtain NMR evidence for material loading into PWHGMs and their subsequent release to the surrounding solutions. The NMR peaks of the loaded materials were distinguishable from the NMR peaks of the materials in the surrounding solution. The formation of the reaction product isopropanol provided evidence of encounters of isopropyl acetate in the microspheres and concentrated H2SO4 added to the surrounding solution. Also, microspheres loaded with H2O were suspended in D2O and monitored to obtain quantitative release kinetics of H2O encapsulated in PWHGMs. A five-parameter double-exponential curve fit of experimental signal intensity data as a function of time indicated two release rates for H2O encapsulated in PWHGMs with time constants of 18 - 20 minutes and 160 minutes. The results demonstrate that NMR is a particularly useful tool to study developments and applications of PWHGMs in targeted drug delivery

Distributed Fiber-Optic Pressure Sensor based on Bourdon Tubes Metered by Optical Frequency-Domain Reflectometry

We report a distributed fiber-optic pressure sensor based on Bourdon tubes using Rayleigh backscattering metered by optical frequency-domain reflectometry (OFDR). In the proposed sensor, a piece of single-mode fiber (SMF) is attached to the concave surfaces of Bourdon tubes using a thin layer of epoxy. The strain profiles along the concave surface of the Bourdon tube vary with applied pressure, and the strain variations are transferred to the attached SMF through the epoxy layer, resulting in spectral shifts in the local Rayleigh backscattering signals. By monitoring the local spectral shifts of the OFDR system, the pressure applied to the Bourdon tube can be determined. By cascading multiple Bourdon tubes and correspondingly attaching SMF sections (i.e., a series of SMF-modified Bourdon tubes), distributed pressure measurements can be realized. Three Bourdon tubes are employed to demonstrate the proposed spatially distributed sensing scheme. The experimental results showed that linear relationships between spectral shift and pressure were obtained in all three SMF-Bourdon tubes (i.e., at three spatial locations). It is expected that the proposed sensing device, the SMF-Bourdon tube, can be used in applications where distributed/multipoint pressure measurements are needed

Probing Changes in Tilt Angle with 20 Nanoradian Resolution using an Extrinsic Fabry-Perot Interferometer-Based Optical Fiber Inclinometer

In this paper, we introduce and demonstrate a novel optical fiber extrinsic Fabry-Perot interferometer (EFPI) for tilt measurements with 20 nrad resolution. Compared with inline optical fiber inclinometers, an extrinsic sensing structure is used in the inclinometer reported herein. Our design greatly improves on the tilt angle resolution, the temperature stability, and the mechanical robustness of inclinometers with advanced designs. An EFPI cavity, which is formed between endfaces of a suspended rectangular mass block and a fixed optical fiber, is packaged inside a rectangular container box with an oscillation dampening mechanism. Importantly, the two reflectors of the EFPI sensor remain parallel while the cavity length of the EFPI sensor meters a change in tilt. According to the Fabry-Perot principle, the change in the cavity length can be determined, and the tilt angle of the inclinometer can be calculated. The sensor design and the measurement principle are discussed. An experiment based on measuring the tilt angle of a simply-supported 70-cm beam induced by a small load is presented to verify the resolution of our prototype inclinometer. The experimental results demonstrate significantly higher resolution (ca. 20 nrad) compared to commercial devices. The temperature cross-talk of the inclinometer was also investigated in a separate experiment and found to be 0.0041 µrad /°C. Our inclinometer was also employed for monitoring the daily periodic variations in the tilt angle of a windowsill in a cement building caused by local temperature changes during a five-day period. The multi-day study demonstrated excellent stability and practicability for the novel device. The significant inclinometer improvements in differential tilt angle resolution, temperature compensation, and mechanical robustness also provide unique opportunities for investigating spatial-temporal modulations of gravitational fields

A Uniform Strain Transfer Scheme for Accurate Distributed Optical Fiber Strain Measurements in Civil Structures

We report a screw-like package design for an embeddable distributed optical fiber strain sensor for civil engineering applications. The screw-like structure is the exterior support for an optical fiber sensor. The bare optical fiber is embedded and secured in a longitudinal groove of the screw-like package using a rigid adhesive. Our packaging scheme prevents damage to the bare optical fiber and ensures that the packaged sensor is accurately and optimally sensing strain fields in civil structures. Moreover, our screw-like design has an equal area in a cross-section perpendicular to and along the screw axis, so strain field distributions are metered faithfully along the length of the embedded optical fiber. Our novel screw-like package optical fiber sensor, interfaced to a Rayleigh scattering-based optical frequency domain reflectometer system enables undistorted, accurate, robust, and spatially-distributed strain measurements in bridges, tunnels, pipelines, buildings, etc. along structural dimensions extending from centimeters to kilometers

Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning

In This Paper, We Report an Array of Fiber-Optic Sensors based on the Fabry-Perot Interference Principle and Machine Learning-Based Analyses for Identifying Volatile Organic Liquids (VOLs). Three Optical Fiber Tip Sensors with Different Surfaces Were Included in the Array of Sensors to Improve the Accuracy for Identifying Liquids: An Intrinsic (Unmodified) Flat Cleaved End face, a Hydrophobic-Coated End face, and a Hydrophilic-Coated End face. the Time-Transient Responses of Evaporating Droplets from the Optical Fiber Tip Sensors Were Monitored and Collected Following the Controlled Immersion Tests of 11 Different Organic Liquids. a Continuous Wavelet Transform Was Used to Convert the Time-Transient Response Signal into Images. These Images Were Then Utilized to Train Convolution Neural Networks for Classification (Identification of VOLs). We Show that Diversity in the Information Collected using the Array of Three Sensors Helps Machine Learning-Based Methods Perform Significantly Better. We Explore Different Pipelines for Combining the Information from the Array of Sensors within a Machine Learning Framework and their Effect on the Robustness of Models. the Results Showed that the Machine Learning-Based Methods Achieved High Accuracy in their Classification of Different Liquids based on their Droplet Evaporation Time-Transient Events

Chemical Classification By Monitoring Liquid Evaporation Using Extrinsic Fabry-Perot Interferometer With Microwave Photonics

Identification of liquids is essential in chemical analysis, safety, environmental protection, quality control, and research. A novel liquid identification system based on Microwave Photonics (MWP) measured time transient evaporation signals is investigated. An extrinsic Fabry-Perot Interferometer (EFPI) based optical probe using single-mode fiber (SMF) is proposed to monitor evaporation of different liquids. The MWP system is used to measure the optical path changes during liquid evaporation due to its high sensitivity, selectivity, and Signal-to-Noise Ratio (SNR). The measured S21 continuous wave (CW) time Magnitude and Phase signals were processed to extract features such as histogram and Fast Fourier Transform (FFT) peaks. Using features extracted from droplet evaporation time transient events, machine learning classification accurately identified chemicals in each liquid with an accuracy rate of over 99%, employing three algorithms: Decision Trees, Support Vector Machine (SVM), and K-nearest neighbors (KNN). The classification results demonstrate accurate liquid identification based on evaporation measurements by the MWP system

In Situ NMR Parameter Monitoring Systems and Methods for Measuring PH and Temperature

Devices and methods are provided for measuring temperatures and pHs of a sample in situ using NMR spectroscopy, and for sealing one or more ends of a capillary tube after a reference material has been added to the capillary tube, which is used in an in situ NMR temperature measurement device. A method for measuring a pH of a sample in situ using NMR spectroscopy includes providing an in situ NMR pH measurement device. This device includes a sample housing member configured to house a target sample, at least one pH sensor configured to exhibit an NMR spectral change due to a change in pH value of the target sample, and a pH sensor containment member configured to house the at least one pH sensor. The target sample is added to the sample housing member. NMR spectra are obtained to then determine the pH of the target sample