Although numerous efforts have been dedicated towards developing fiber sensors with high performances, challenges still remain in achieving high-quality temperature sensors with high sensitivity, large measurement range and high stability. This study proposes a compact fiber optic temperature sensor based on PDMS-coated Mach-Zehnder interferometer (MZI) combined with FBG, and it can realize both high-sensitivity and large-range temperature measurement. The MZI is based on Thin No-Core Fiber (TNCF) with lateral-offset. Owing to the high refractive index sensitivity of MZI and the high thermo-optic coefficient of PDMS, the sensor can achieve a high temperature sensitivity (>10 nm/°C). Besides, by optimizing the TNCF length, the cascaded FBG can be used to locate different temperature intervals in units of approximately 10 °C, and therefore the detectable temperature range is largely extended. The experimental test demonstrates that the average sensitivities of 11.19 nm/°C, 8.53 nm/°C, 7.76 nm/°C, 7.27 nm/°C are achieved at the temperature around 30 °C, 40 °C, 50 °C and 60 °C, and it shows excellent consistency and repeatability during the thermal cycle tests.

A Ti₃CN MXene enabled ultra-sensitive optical fiber sensor is proposed, and a salinity measurement is conducted to evaluate its sensing performance in a low-concentration target molecule detection environment. Owing to the abundance of hydrophilic functional groups (–O, –F, and –OH), large specific surface, and broad-spectrum absorption characteristics of the MXene layers, the sensing performance of the MXene-incorporated sensor is greatly improved and an ultra-high salinity sensitivity of −5.34 nm/‰ is achieved (equivalent to a refractive index sensitivity of −33429 nm/RIU). Such an excellent sensing performance is 137.33% higher than that of the bare fiber sensor and is significantly enhanced over previously reported fiber sensors. Furthermore, the sensing performance of the sensor is improved without damaging the fiber structure, which is a huge advantage when compared with the traditional fiber post-processing techniques. Finally, because the refractive index is commonly used to characterize the detection ability of biosensors, our contribution suggests the integration of MXene as a potential approach to develop high-performance optical fiber biosensors.

A fiber temperature sensor based on four-wave mixing (FWM) with an oil-filled photonic crystal fiber (PCF) is proposed in this study, and a multipoint measurement based on the wavelength multiplexing of such sensors is constructed for the first time. The sensing performance and signal spectral characteristics of the temperature sensor are theoretically and experimentally studied. The maximum temperature sensitivity of the signal light of 0.207 nm/°C is achieved using a FWM sensing fiber with a length of 10 cm. The signal wavelength response to excitation power is also explored in this experiment. Results showed that the temperature sensor is relatively insensitive to the fluctuation of power change. The wavelength multiplexing of a FWM-based PCF temperature sensor also presents the possibility of multiplexing measurement and multipoint sensing, and high multiplexed capability is theoretically predicted to be obtainable with optimized sensitivity and splicing loss.

This study evaluates the interrogation techniques of a hybrid fiber optic sensor based on surface plasmon resonance (SPR) and multimode interference (MMI). The sensor is based on a single mode, fiber-no core, fiber-single mode fiber (SMF-NCF-SMF) structure with a deposited gold film layer. Both SPR and MMI effects are excited in a single sensor structure without enlarging the device size. However, at the same time, the interference fringe patterns are also mixed with the SPR transmission spectra, and the traditional SPR interrogation technique becomes unavailable since the resonant wavelength is hard to be located. In this study, the fast Fourier transform and different filtering algorithms are applied, both SPR signal and interference signal with different orders are separated effectively due to their different spatial frequency distributions, and they are processed individually for refractive index (RI) sensing. The experimental results verify that the overall RI sensitivity of the hybrid sensor is significantly enhanced. This study provides an important supplement to the traditional SPR and MMI functions.

In this paper, a compact temperature sensor was constructed based on controllable four-wave mixing (FWM) achieved by a partially filled solid-core photonic crystal fiber (PCF). With the help of a hollow-core fiber, the air holes of the PCF were partially filled with one layer, two layers, and four holes, respectively, thereby realizing a controllable output of signal wave. The response to temperature was tested and analyzed in the experiment. A sensitivity of 0.61 nm/°C was obtained from the room temperature to 150 °C with a fiber shortened to 25.8 mm. Both experimental and theoretical results showed that the response to the temperature could be with great linearity for the one-layer filled PCF, and the sensitivity of FWM-based sensor was almost unchanged when the sensing fiber is shortened, which could be benefit for constructing compact or ultra-compact inline FWM-based sensors. Furthermore, the FWM-based temperature sensor also presented good stability against the fluctuation of the excited power.

Although fiber-optic distributed acoustic sensing (DAS) has been widely applied for intrusion recognition in the perimeter security field, challenges still remain for high-accuracy recognition of new types of intrusion events. Drone, as a non-contact, stealthy intrusion event, has not been reported to achieve effective monitoring and recognition by employing the fiber-optic DAS technology. By introducing drone intrusion, the scope of the threatening intrusion in the perimeter security monitoring field is extended. This study proposes a multi-source threatening event recognition scheme targeting drone intrusion in the fiber optic DAS system. To achieve this objective, a dual-stage recognition method is proposed. Besides, the wavelet denoising method is applied to extract the effective signal from weak disturbances introduced by drone flight. The variational mode decomposition (VMD)-based hybrid feature vector is formed to remove the noise and further extract the effective signal characteristics. Next, we demonstrate a hybrid model framework based on convolutional neural network (CNN)+long short term memory (LSTM)+self-attention mechanism to achieve the effective recognition of multi-source threatening event targeting drone intrusion. Particularly, we thoroughly discuss the distinguishing ability between drone intrusion and non-threatening wind blowing, and the recognition ability for the simultaneous occurrence of both human-contact intrusion and non-contact drone intrusion. The experimental results show that, the proposed recognition scheme can effectively distinguish the drone intrusion from the non-threatening wind blowing event with a high accuracy of 100%. In addition, when drone intrusion, environmental disturbances and human-contact intrusion occur simultaneously, a high recognition accuracy of 96.25% is achieved with a fast response time of 0.733 s.