High-temperature measurements are of significant importance in various harsh-environment engineering fields such as fossil fuel production, and the metallurgical and aviation industries. In recent years, there is a steady trend to shift from conventional electronic sensors to optical fiber sensors for high-temperature applications. In particular, optical fiber sensors are small in size, immune to electromagnetic interference, readily applicable for remote sensing, have high elasticity, and incorporate capabilities for multiplexing and distributed sensing. However, commonly used fused silica optical fiber sensors exhibit severe limitations at ultra-high temperatures due to significantly degraded optical and mechanical properties at temperatures >1000°C. The excellent optical transparency, thermal and chemical stability, mechanical robustness, and high melting temperature (~2040°C) of single-crystal sapphire fibers make them a strong candidate for sensing applications in high-temperature environments. Translation of the sensing schemes from mature silica fiber sensors to sapphire fiber sensors has undergone tremendous growth and advancements in the past two decades. However, hurdles to the development of a near-term deployable sapphire fiber sensing system have proven persistent due to the highly multimodal nature of sapphire fibers. This paper reviews sensing techniques that have been implemented with sapphire fibers recently. The aim is to provide a comprehensive summary of past research on sapphire fiber sensing systems. Perspectives on further research into the challenging yet promising arena are also discussed.

Metal-organic frameworks (MOFs), a newer class of crystalline nanoporous materials, have been in the limelight owing to their exceptional tunability for structures and physicochemical properties, and have found successful applications in gas storage, gas separation, and catalysis. The mesmerizing properties of MOFs, especially the extensive and tunable porosity and chemical selectivity, also make them an excellent candidate class as chemo-sensory materials. Moreover, MOF-based sensors have attracted considerable attention in the past decade. Recent literature reviews focused on the progress of MOF-based electronic sensors and luminescent MOF sensors, while sensors exploiting the dielectric properties (refractive index) of MOFs were also demonstrated and discussed very recently. The motivation of this report is to provide, for the first time, a general review on such MOF sensors with a particular focus on miniature optical fiber (OF) based MOF sensors and to demonstrate the promising potential of MOFs as dielectric coatings on OF for highly sensitive chemical sensing. The fundamental principle of OF-MOF sensors relies on the tunability of the refractive index of a MOF, which is dependent on the amount and type of adsorbed guest molecules in the MOF pores. MOF sensors based on different optical sensing principles are reviewed; challenges and perspectives on further research into the field of OF-MOF sensors are also discussed.

This paper presents a spatially distributed fiber-optic sensor system designed for demanding applications, like temperature measurements in the steel industry. The sensor system employed optical frequency domain reflectometry (OFDR) to interrogate Rayleigh backscattering signals in single-mode optical fibers. Temperature measurements employing the OFDR system were compared with conventional thermocouple measurements, accentuating the spatially distributed sensing capability of the fiber-optic system. Experiments were designed and conducted to test the spatial thermal mapping capability of the fiber-optic temperature measurement system. Experimental simulations provided evidence that the optical fiber system could resolve closely spaced temperature features, due to the high spatial resolution and fast measurement rates of the OFDR system. The ability of the fiber-optic system to perform temperature measurements in a metal casting was tested by monitoring aluminum solidification in a sand mold. The optical fiber, encased in a stainless steel tube, survived both mechanically and optically at temperatures exceeding 700 °C. The ability to distinguish between closely spaced temperature features that generate information-rich thermal maps opens up many applications in the steel industry.

This paper proposes phase-shift-amplified optical fiber interferometry based on microwave photonics (MWP) for sensing applications with substantially-improved sensitivity. The principal idea of the system combines a destructive interference-based phase-shift amplification technique with optical carrier-based microwave interferometry (OCMI). The phase sensitivity of the OCMI system is significantly improved due to the phase amplifier, and more importantly, can be adjusted by simply varying the amplitude ratio of the two beams used in the interferometer. The amplification of the phase sensitivity is numerically investigated and experimentally demonstrated using a Mach-Zehnder interferometer for temperature and strain measurements. The measurement results accurately match theoretical predictions. Moreover, we demonstrate that light-scattering dots in the optical fiber core, created by tightly-focused femtosecond laser pulses, can be used to precisely tune the amplitude ratio of the two-beam interferometer. We postulate that amplification of several orders of magnitude in phase sensitivity can be achieved in the OCMI system by employing micromachining methods.

Continuous casting in steel production uses specially developed oxyfluoride glasses (mold fluxes) to lubricate a mold and control the solidification of the steel in the mold. The composition of the flux impacts properties, including basicity, viscosity, and crystallization rate, all of which affect the stability of the casting process and the quality of the solidified steel. However, mold fluxes interact with steel during the casting process, resulting in flux chemistry changes that must be considered in the flux design. Currently, the chemical composition of mold flux must be determined by extracting flux samples from the mold during casting and then processing these samples offline to estimate the working chemical composition and, therefore, the expected properties of the flux. Raman spectroscopy offers an alternative method for performing flux analysis with the potential to perform measurements online during the casting process. Raman spectroscopy uniquely identifies specific chemical bonds and symmetries in the glassy flux by revealing peaks that are a fingerprint of the vibration modes of molecules in the flux. The intensities of specific peaks in Raman spectra can be correlated with the chemical composition of the melt and associated properties such as basicity and viscosity. This paper reports on the first use of a portable fiber-optic Raman sensor for in situ Raman spectroscopic measurements of molten flux at 1400°C. The work demonstrates the advantages of fiber-optic Raman spectroscopy to document the structure and chemical composition of flux samples at temperatures typically encountered in the mold during continuous caster operation. Experimental results demonstrate that the composition-dependent Raman signal shifts can be detected at caster operating temperatures, and the use of high-temperature Raman analysis for in-line flux monitoring shows significant promise for the in situ detection of changes in flux composition and physical properties during casting.

Mild traumatic brain injury (mTBI) strongly associates with chronic neurodegenerative impairments such as post-traumatic stress disorder (PTSD) and mild cognitive impairment. Early detection of concussive events would significantly enhance the understanding of head injuries and provide better guidance for urgent diagnoses and the best clinical practices for achieving full recovery. A smart helmet was developed with a single embedded fiber Bragg grating (FBG) sensor for real-time sensing of blunt-force impact events to helmets. The transient signals provide both magnitude and directional information about the impact event, and the data can be used for training machine learning (ML) models. The FBG-embedded smart helmet prototype successfully achieved real-time sensing of concussive events. Transient data "fingerprints" consisting of both magnitude and direction of impact, were found to correlate with types of blunt-force impactors. Trained ML models were able to accurately predict (R2 ∼ 0.90) the magnitudes and directions of blunt-force impact events from data not used for model training. The combination of the smart helmet data with analyses using ML models provides accurate predictions of the types of impactors that caused the events, as well as the magnitudes and the directions of the impact forces, which are unavailable using existing devices. This work resulted in an ML-assisted, FBG-embedded smart helmet for real-time identification of concussive events using a highly accurate multi-metric strategy. The use of ML-FBG smart helmet systems can serve as an early-stage intervention strategy during and immediately following a concussive event.