Ultra-high sensitive dual-parameter sensor based on double-hole fiber for simultaneous detection of magnetic field and temperature.

An ultra-high sensitive dual-parameter sensor based on double-hole fiber (DHF) is proposed for simultaneous detection of magnetic fields and temperatures. The sensor utilizes the DHF containing a Ge-doped core with two large air holes symmetrically arranged at its two sides. To enhance the sensitivity to both a magnetic field and temperature, Al wires with different diameters are embedded on the inner walls of the air holes in the DHF, creating a magnetic field sensing channel filled with magnetic fluid and a temperature sensing channel filled with thermo-sensitive liquid. Structural parameters and metal materials of the sensor are optimized by using the finite element method. Numerical results demonstrate that this DHF-based dual-parameter sensor can detect magnetic fields ranging from 40 Oe to 130 Oe and temperatures ranging from 24.3 °C to 49.3 °C simultaneously. The maximum magnetic field sensitivity reaches up to 64000 pm/mT, while the maximum temperature sensitivity is approximately 44.6 nm/°C, both exceeding current reports by more than one order of magnitude for simultaneous detection of magnetic field and temperature. With its high sensitivity, low fabrication difficulty, and simple structure, this DHF-based dual-parameter sensor has potential applications in the fields of material characterization analysis, geological environmental monitoring, and aeronautical engineering.

Ultra-high sensitivity weak magnetic field detecting magnetic fluid surface plasmon resonance sensor based on a single-hole fiber.

An ultra-high sensitivity weak magnetic field detecting magnetic fluid surface plasmon resonance (SPR) sensor based on a single-hole fiber (SHF) is proposed for detecting weak magnetic fields. The sensor is constructed with a single-hole fiber in which an exclusive air hole in the cladding is embedded with a metal wire and filled with a magnetic fluid (MF) to enhance the magnetic field sensitivity. The effects of the structural parameters, embedded metals, and refractive index difference between the core and cladding on the magnetic field sensitivity and peak loss are investigated and optimized. The sensitivity, resolution, figure of merit (FOM), and other characteristics of the sensor are analyzed systematically. The numerical results reveal a maximum magnetic field sensitivity of 451,000 pm/mT and FOM of 15.03 mT-1. The ultra-high magnetic field sensitivity renders the sensor capable of detecting weak magnetic fields at the pT level for the first time, in addition to a detection range from 3.5 mT to 17 mT. The SHF-SPR magnetic field sensor featuring high accuracy, simple structure, and ease of filling has immense potential in applications such as mineral resource exploration as well as geological and environmental assessment.

Using machine learning to enlarge the measurement range and promote the compactness of the optical fiber torsion sensor based on the Sagnac interferometer.

The support vector regression (SVR) algorithm is presented to demodulate the torsion angle of an optical fiber torsion sensor based on the Sagnac interferometer with the panda fiber. Experimental results demonstrate that with the aid of SVR algorithm, the information in the transmission spectrum of the sensor can be used fully to realize the regression prediction of the directional torsion angle. The full torsion angle ranges from -360° to 360° can be predicted with a mean absolute error (MAE) of 2.24° and determination coefficient (R2) of 0.9996. The impact of the angle sampling interval and wavelength resolution of the spectrometer on the prediction accuracy of the directional torsion angle and the suitability of the SVR algorithm for compact optical fiber sensor and other optical fiber torsion sensors based on the Sagnac interferometer are discussed. Moreover, the multi-objective SVR algorithm is used to eliminate the interference of strain during torsion angle measurement. The SVR algorithm can efficiently enlarge the measurement range of the torsion angle and break through the challenge of demodulating sensing signal for compact fiber torsion sensor. Compared to the prediction accuracy of common machine learning algorithms of artificial neural network (ANN) algorithm, random forest (RF) algorithm, and K-nearest neighbor (KNN) algorithm, the SVR algorithm has the advantages of higher measurement accuracy and shorter testing time.