Design of a Surface-Plasmon-Resonance Sensor Based on a Microstructured Optical Fiber with Annular-Shaped Holes

A novel design is proposed for highly sensitive surface-plasmon-resonance sensors. The sensor is based on a microstructured optical fiber with two layers of annular-shaped holes. A gold layer is deposited on the inner surface of the second hole-layer, in which the holes have several micrometers thickness in size, facilitating analyte infiltration and metal layer deposition. In the first layer of holes, the sector-ring^shaped arms, used as supporting strips, are utilized to tune the resonance depth of the sensor. Numerical results indicate that the sensor operation wavelength can be tuned across the C＋L-band. The spectral sensitivity of 1.0.104 nm. RIU-1 order of magnitude and a detection limit of 1.0.10-4 RIU order are demonstrated over a wide range of analyte refractive index from 1.320 to 1.335.

Ultraflat, broadband, and highly coherent supercontinuum generation in all-solid microstructured optical fibers with all-normal dispersion

High flatness, wide bandwidth, and high-coherence properties of supercontinuum(SC) generation in fibers are crucial in many applications. It is challenging to achieve SC spectra in a combination of the properties, since special dispersion profiles are required, especially when pump pulses with duration over 100 fs are employed. We propose an all-solid microstructured fiber composed only of hexagonal glass elements. The optimized fiber possesses an ultraflat all-normal dispersion profile, covering a wide wavelength interval of approximately 1.55 μm. An SC spectrum spanning from approximately 1030 to 2030 nm(corresponding to nearly one octave) with flatness<3 dB is numerically generated in the fiber with 200 fs pump pulses at 1.55 μm. The results indicate that the broadband ultraflat SC sources can be all-fiber and miniaturized due to commercially achievable 200-fs fiber lasers. Moreover, the SC pulses feature high coherence and a single pulse in the time domain, which can be compressed to 13.9-fs pulses with high quality even for simple linear chirp compensation. The Fourier-limited pulse duration of the spectrum is 3.19 fs, corresponding to only 0.62 optical cycles.

Characteristics of Bragg Gratings in All-Solid Photonic Bandgap Fiber

We report on fiber Bragg gratings in all-solid photonie bandgap fiber that was composed of a triangular array of high-index Ge-doped rods in pure silica background with fluorine-doped index-depressed layer surrounding the Ge-doped rod. Fiber Bragg gratings were photowritten with 193 nm ArF excimer laser and characterized for their response to strain, temperature, bending, and torsion. These gratings couple light from the forward core mode to not only backward core mode but also backward rod modes. This results in multiple resonance peaks in the reflection spectrum. All resonance wavelengths exhibited the same temperature and strain response with coefficient similar to that of Bragg gratings in standard single-mode fiber. The strength of the resonance peaks corresponding to the backward rod modes showed high sensitivity to bending and torsion.

Deep-learning cell imaging through Anderson localizing optical fiber

We demonstrate a deep-learning-based fiber imaging system that can transfer real-time artifact-free cell images through a meter-long Anderson localizing optical fiber.The cell samples are illuminated by an incoherent LED light source.A deep convolutional neural network is applied to the image reconstruction process.The network training uses data generated by a setup with straight fiber at room temperature(∼20°C)but can be utilized directly for high-fidelity reconstruction of cell images that are transported through fiber with a few degrees bend or fiber with segments heated up to 50°C.In addition,cell images located several millimeters away from the bare fiber end can be transported and recovered successfully without the assistance of distal optics.We provide evidence that the trained neural network is able to transfer its learning to recover images of cells featuring very different morphologies and classes that are never“seen”during the training process.

Multi-direction bending sensing based on spot pattern demodulation of dual-hole fiber

A multi-direction bending sensor based on spot pattern demodulation of a dual-hole fiber(DHF)is proposed.By using the interference and scattering in a DHF,the related multidirectional variations can be captured by the optical field.Furthermore,the multi-directional bending characteristics of the fiber are quantitatively described by the pattern of the output light spot,achieving multidirectional bending sensing.In addition,considering the subtle changes in the deformation patterns over time,a convolutional neural network(CNN)model based on deep learning is introduced for accurate recognition and prediction of the bending angle.The experimental results show that the sensor can perceive different bending angles in four directions.These outstanding results indicate that the multi-directional bending sensor based on dual-hole interference pattern decoding has potential applications in multi-directional quantitative sensing and artificial intelligence perception.

Recent Progress in Fiber Optofluidic Lasing and Sensing

Fiber optofluidic laser(FOFL)integrates optical fiber microcavity and microfluidic channel and provides many unique advantages for sensing applications.FOFLs not only inherit the advantages of lasers such as high sensitivity,high signal-to-noise ratio,and narrow linewidth,but also hold the unique features of optical fiber,including ease of integration,high repeatability,and low cost.With the development of new fiber structures and fabrication technologies,FOFLs become an important branch of optical fiber sensors,especially for application in biochemical detection.In this paper,the recent progress on FOFL is reviewed.We focuse mainly on the optical fiber resonators,gain medium,and the emerging sen sing applicatio ns.The prospects for FOFL are also discussed.We believe that the FOFL sensor provides a promising technology for biomedical analysis and environmental monitoring.