Temperature sensing using CdSe quantum dot doped poly(methyl methacrylate) microfiber.

This work describes noncontact temperature measurements using wavelength shifts of CdSe quantum dot (QD) doped poly(methyl methacrylate) microfiber. The sensor is fabricated using a drawing method by bridging two tapered single mode fibers with a polymer microfiber (PMF) approximately 3 µm in diameter. A set of a PMF section with and without the doping of the CdSe-ZnS core-shell QD was applied as sensing probes and used to measure temperatures over the range of 25°C-48°C. The experimental results show that the doped PMF is able to achieve a higher performance with a reasonably good sensitivity of 58.5 pm/°C based on the wavelength shifting, which is about 18 times that of the undoped PMF temperature sensitivity. The proposed sensor showed a linear temperature sensing range that matches well with the physiologically relevant temperatures. Moreover, these results open the way for long-term and high-stability realization of temperature sensing optical fibers.

Potassium permanganate (KMnO4) sensing based on microfiber sensors.

Two straight microfiber sensors are proposed and demonstrated for the detection of various concentrations of a potassium permanganate (KMnO4) solution. Two types of straight microfibers, namely, silica microfiber and poly(methyl methacrylate) microfiber, have been fabricated by using the flame brushing technique and the direct drawing technique, respectively. Based on the varied KMnO4 concentrations of the solution from 1% to 6%, the measurement of the peak voltage of the transmission power was made. The results show that the sensitivity of the silica microfiber sensor and the polymer microfiber is obtained at 184.5 µW/% and 32.57 µW/% with a resolution of 0.0035% and 0.0064%, respectively. Hence, the silica microfiber is more sensitive than the polymer microfiber for KMnO4 concentration measurements.

Classification of reflected signals from cavitated tooth surfaces using an artificial intelligence technique incorporating a fiber optic displacement sensor.

An enhanced dental cavity diameter measurement mechanism using an intensity-modulated fiber optic displacement sensor (FODS) scanning and imaging system, fuzzy logic as well as a single-layer perceptron (SLP) neural network, is presented. The SLP network was employed for the classification of the reflected signals, which were obtained from the surfaces of teeth samples and captured using FODS. Two features were used for the classification of the reflected signals with one of them being the output of a fuzzy logic. The test results showed that the combined fuzzy logic and SLP network methodology contributed to a 100% classification accuracy of the network. The high-classification accuracy significantly demonstrates the suitability of the proposed features and classification using SLP networks for classifying the reflected signals from teeth surfaces, enabling the sensor to accurately measure small diameters of tooth cavity of up to 0.6 mm. The method remains simple enough to allow its easy integration in existing dental restoration support systems.