Ninik Irawati

A Study of Relative Humidity Fiber-Optic Sensors

A humidity sensor made of tapered plastic optical fiber (POF) coated with agarose gel or hydroxyethylcellulose/polyvinylidenefluoride (HEC/PVDF) detects humidity from the change in the refractive index (RI) of its coating. The RI of the deposited agarose gel or HEC/PVDF coating changes when it swells after absorbing water molecules from the surrounding. Similarly, when a tapered POF seeded with ZnO nanostructure is exposed to ambient humidity, a rapid surface adsorption of water molecules into the ZnO surface occurs. Therefore, the effective RI of its coating, which consists of the thin ZnO nanostrtucture and air, changes with humidity variation. For all of these sensors, the change in the RI of the coating affects the ability of the fiber to modulate light, thereby altering the output light intensity. In this paper, the performances of the three coating materials used with tapered fibers to construct humidity sensors are investigated. The results of the experiments show that agarose gel, HEC/PVDF, and ZnO-based optical fiber sensors are both sensitive and efficient for humidity sensing.

Tapered Plastic Optical Fiber Coated With Al-Doped ZnO Nanostructures for Detecting Relative Humidity

A relative humidity (RH) sensor is demonstrated using a tapered plastic optical fiber (POF) that is coated with Al-doped ZnO nanostructures. A simple etching method was used to fabricate the tapered POF that operates based on intensity modulation technique. The tapered fiber was then coated with Al-doped ZnO nanostructures using sol-gel immersion method with different mol% of Al nitrate that acts as a dopant. The 1 mol% of Al nitrate that used in the synthesis process exhibited better performance compared with the other doping concentrations. Then, results obtained for both undoped ZnO and 1 mol% of Al-doped ZnO were compared and investigated. The performance of 1 mol% of Al-doped ZnO demonstrated better linearity and sensitivity of 97.5% and 0.0172 mV/%, respectively, whereas the undoped ZnO yielded linearity and sensitivity of 93.3% and 0.0029 mV/%, respectively. The proposed sensor provides numerous advantages, such as simplicity of design, low cost of production, higher mechanical strength, and is easier to handle compared with silica fiber optic. Results show that tapered POF with Al-doped ZnO nanostructures enables the increase in sensitivity of fiber for detection of changes in RH.

Relative Humidity Sensing Using a PMMA Doped Agarose Gel Microfiber

Humidity sensors rely on humidity-induced refractive index change in the sensing material despite the sensor configuration. Polymer-based microwires can absorb water vapor molecules and detect humidity changes without the need of further coating. However, the sensitivity-simplicity trade-off is still a challenge. Sophisticated coating methods, complex resonating structures, and nanostructured films are reported as methods to enhance the device sensitivity. A simple technique, to build a high sensitivity RH sensor based on an agarose-doped Poly Methyl Methacrylate (PMMA) sensor head, is demonstrated. The waist diameter and uniform length of the PMMA doped agarose gel microfiber were measured to be 6 μm and 10 mm, respectively. The sensor can achieve power variation of up to 2.9 μW in a wide relative humidity range (50–80%), and display linear response with a correlation coefficient of 98.29%, sensitivity of 0.421 dB/%RH, and resolution of 0.431%RH. This agarose-based optical sensor provides a beneficial complement to the existing electrical ones, and will promote the employment of agarose in chemical sensing techniques.

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

Abstract. 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.