Chemical etching of optical fibre tips - experiment and model

The formation of chemically etched fibre tips for use in optical scanning probe microscopy is addressed. For tips formed at a cleaved fibre end in the bulk of a buffered HF acid solution the morphological features (tip height, cone angle) are found to depend strongly on the temperature and etchant composition. The tip formation process is analysed and explained in terms of a simple model in which the only pertinent physical parameters are the fibre core diameter and etch rates of the fibre core and cladding. The etch rates are determined in separate experiments as a function of temperature (in the range 24-50 degreesC) for etchant solutions of de ionised water: 50% HF acid: 40% NH4F in the volume ratio 1 : 1 : X for X=2, 4 and 6, and used in the model to yield a correct description of the experimental tip cone angles. The model is successfully extended to the intriguing case of negative tip formation which initiates in a normal, positive tip structure. By contrast, tip formation in the meniscus region of a bare fibre/etchant/organic solvent system is found to be independent of etchant composition and temperature. (C) 2000 Elsevier Science B.V. All rights reserved.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Effect of plasticizer-polymer compatibility on the response characteristics of optical thin CO2 and O-2 sensing films

A homologous family of dialkyl phthalates has been used to investigate the effect of plasticizer/polymer compatibility on the response characteristics of transparent, plastic, thin optical gas sensing films for CO2 and oxygen. Plasticizer/polymer compatibilities were determined through the value of the difference in solubility parameter, i.e. Delta delta, for the plasticizer and polymer with a Delta delta value of zero indicating high compatibility. A strong correlation was found between plasticizer/polymer compatibility and sensitivity in phenol red/ethyl cellulose CO2-sensitive films and this relationship extended to CO2-sensitive films based on other polymers such as polystyrene and poly(methyl methacrylate). It extended also to optical O-2-sensitive films implying that the relationship is general for thin-film optical sensors. Other results from the CO2-sensitive films in different polymers indicated that the film sensitivity is largely independent of the polymer matrix regardless of its inherent gas permeability, when a sufficient quantity of compatible plasticizer is present. (C) 1998 Elsevier Science B.V.

Study of reliability of fibre Bragg grating fibre optic strain sensors for field-test applications

In this paper, the reliability and thus the suitability of optical fibre strain sensors for surface strain measurement in concrete structures was investigated. Two different configurations of optical strain sensors were used each having different mountings making them suitable for different uses in various structures. Due to the very limited time available to install the sensors and take result, commercially packaged sensors were used. In the tests carried out each sensor was mounted onto a concrete beam which was then subjected to a range of known and calibrated loadings. The performance of the optical strain sensors thus evaluated was compared with the results of conventional techniques. This comparison allows for selecting the best performing combination of sensor/mounting, i.e. long-gauge sensor with mounts bolted to threaded rods glued into the concrete for use in future work in a field test where a limited time window was available for installation, testing and post-test demounting. © 2012 Elsevier B.V.

Fluorescence based fibre optic pH sensor for the pH 10–13 range suitable for corrosion monitoring in concrete structures

The design, development and evaluation of an optical fibre pH sensor for monitoring pH in the alkaline region are discussed in detail in this paper. The design of this specific pH sensor is based on the pH induced change in fluorescence intensity of a coumarin imidazole dye which is covalently attached to a polymer network and then fixed to the distal end of an optical fibre. The sensor provides a response over a pH range of 10.0–13.2 with an acceptable response rate of around 50 min, having shown a very good stability over a period of longer than 20 months thus far. The sensor has also demonstrated little cross-sensitivity to ionic strength (IS) and also excellent photostability through a series of laboratory tests. These features make this type of sensor potentially well suited for in situ long term monitoring of pH in concrete structures, to enhance structural monitoring in the civil engineering sector