Miniature Optical Fiber Fabry-Perot Interferometer Based on a Single-Crystal Metal-Organic Framework for the Detection and Quantification of Benzene and Ethanol at Low Concentrations in Nitrogen Gas.

This study reports for the first time, to the best of our knowledge, a real-time detection of ultralow-concentration chemical gases using fiber-optic technology, combining a miniaturized Fabry-Perot interferometer (FPI) with metal-organic frameworks (MOFs). The sensor consists of a short and thick-walled silica capillary segment spliced to a lead-in single-mode fiber (SMF), housing a tiny single crystal of HKUST-1 MOF, imparting chemoselectivity features. Ethanol and benzene gases were tested, resulting in a shift in the FPI interference signal.

Miniaturized fluorescence pH sensor with assembly free ball lens on a tapered multimode optical fiber.

In biochemistry, the absence of a compact, assembly-free pH sensor with high sensitivity and signal-to-noise ratio has been a persistent hurdle in achieving accurate pH measurements in real time, particularly in complex liquid environments. This manuscript introduces what we believe to be a novel solution in the form of a miniaturized pH sensor utilizing an assembly-free ball lens on a tapered multimode optical fiber (TMMF), offering the potential to revolutionize pH sensing in biochemical applications. A multimode optical fiber (MMF) was subjected to tapering processes, leading to the creation of an ultra-thin needle-like structure with a cross-sectional diameter of about 12.

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).

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 2-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.