This Letter reports a polymer optical fiber (POF) based large strain sensor based on the multimode interference (MMI) theory for the application of structural health monitoring. A section of POFs is sandwiched between two silica single mode fibers to construct a single-mode-multimode-single-mode structure that produces a MMI spectrum. The strain sensing mechanism of the device was investigated and experimentally verified. A large dynamic range of 2×10(4) με (2%) and a detection limit of 33 µε have been demonstrated.

pH sensing at the single-cell level without negatively affecting living cells is very important but still a remaining issue in the biomedical studies. A 70 μm reflection-mode fiber-optic micro-pH sensor was designed and fabricated by dip-coating thin layer of organically modified aerogel onto a tapered spherical probe head. A pH sensitive fluorescent dye 2′, 7′-Bis (2-carbonylethyl)-5(6)-carboxyfluorescein (BCECF) was employed and covalently bonded within the aerogel networks. By tuning the alkoxide mixing ratio and adjusting hexamethyldisilazane (HMDS) priming procedure, the sensor can be optimized to have high stability and pH sensing ability. The in vitro real-time sensing capability was then demonstrated in a simple spectroscopic way, and showed linear measurement responses with a pH resolution up to an average of 0.049 pH unit within a narrow, but biological meaningful pH range of 6.12–7.81. Its novel characterizations of high spatial resolution, reflection mode operation, fast response and high stability, great linear response within biological meaningful pH range and high pH resolutions, make this novel pH probe a very cost-effective tool for chemical/biological sensing, especially within the single cell level research field.

Single-cell research is essential for understanding cell heterogeneity, cell differentiation, and carcinogenesis, among other important cellular processes. New techniques for intracellular pH monitoring are urgently needed to gain new insights into single-cell responses to external stimuli. In this study, fiber-optic reflection-based pH micro (μ)-probes (tip diameter: 500-3000 nm) were designed and fabricated using a novel hexagonal 1-in-6 fiber configuration. An organic-modified silicate (OrMoSils) sol-gel doped with a pH-sensitive dye, 2',7'-bis(2-carboxyethyl)-5-(and-6)-carboxyfluorescein (BCECF), were coated onto the probe sensing tip for pH detection. These probes enabled neutral pH monitoring and quasi-real-time data acquisition (response time: 20 ± 5 s). The fluorescence signals of the newly developed probes were found to correlate linearly with pH (R(2) = 0.9869 when coupling laser power was at 8.2 mW) within a biologically relevant pH range (6.18-7.80). The pH resolution was 0.038 pH unit. The miniaturized probes were validated in single human lung cancer A549 cells to demonstrate applicability in single-cell experiments. In summary, novel pH μ-probes with excellent resolution and response times within a biologically relevant pH range were developed, and they can be used for measuring pH changes in single cells.

An all-in-fiber prototype optofluidic device was fabricated by femtosecond laser irradiation and subsequent selective chemical wet etching. Horizontal and vertical microchannels can be flexibly created into an optical fiber to form a fluidic cavity with inlets/outlets. The fluidic cavity also functions as an optical Fabry-Perot cavity in which the filled liquid can be probed. The assembly-free microdevice exhibited a fringe visibility of 20 dB and was demonstrated for measurement of the refractive index of the filling liquids. The proposed all-in-fiber optofluidic micro device is attractive for chemical and biomedical sensing because it is flexible in design, simple to fabricate, mechanically robust, and miniaturized in size.

In previous research, a watershed-based algorithm was shown to be useful for automatic lesion segmentation in dermoscopy images, and was tested on a set of 100 benign and malignant melanoma images with the average of three sets of dermatologist-drawn borders used as the ground truth, resulting in an overall error of 15.98%. In this study, to reduce the border detection errors, a neural network classifier was utilized to improve the first-pass watershed segmentation; a novel “Edge Object Value (EOV) Threshold” method was used to remove large light blobs near the lesion boundary; and a noise removal procedure was applied to reduce the peninsula-shaped false-positive areas. As a result, an overall error of 11.09% was achieved.