Mana Sriyudthsak

Methanol and ammonia sensing characteristics of sol–gel derived thin film gas sensor

Abstract SnO 2 thin film gas sensors have been prepared by sol–gel technique. The tin alkoxide precursor was synthesized by chemical reactions involving tin tetrachloride and sodium ethoxide. The gas sensing characteristics have been studied using a flow injection analysis and liquid samples of alcohol and ammonia. The experimental results revealed that the film thickness strongly affected gas sensitivity of the SnO 2 films. The SnO 2 gas sensors gave linear relationship between logarithm of gas sensitivity and logarithm of sample concentration in the range of 0.26–10% by volume for alcohol and 0.05–10% by volume for ammonia with the operation temperature of 350°C.

Thin tin-oxide film alcohol-gas sensor

Abstract Alcohol-gas sensors have been fabricated from thin-film tin oxide. The thickness of the tin-oxide film is 500A˚. The sensor electrode has been formed by evaporating 500A˚of titanium and 1000A˚of platinum on to the tin-oxide film by using an electron-beam evaporator. Calcium oxide and aluminium have been used as dopants for improving the sensor characteristics. It is found that by doping the sensor with 2% calcium oxide, the operating temperature can be reduced from 500°C to 200 or 300°C, and the sensitivity and selectivity of the sensor are enhanced. The sensor has a linear response to alcohol concentration from 1 to 10% by volume. The sensor has been also employed in testing the alcohol concentration in commercial wines. The results obtained from the gas sensor correlate with the value labelled on the wine bottles.

Enzyme-epoxy membrane based glucose analyzing system and medical applications

A semi-automatic glucose analyzing system has been developed. It consists of thin film glucose sensors and a flow injection system with a computerized data processing unit. The sensor is made of titanium and platinum, using the well-established semiconductor process technology. Glucose oxidase is immobilized into the network of epoxy on the electrode surface together with ferrocene, which is used as an electron mediator for reducing the operation voltage of the sensor. The system can measure glucose from 50 to 4000 mg/dl with good linearity up to 400 mg/dl. This can cover the range needed in medical and biotechnological applications. The system can measure samples at a speed of more than 60 samples per hour and has a coefficient of variation of 3%. Selectivity tests were performed and the results showed that there was no interference from sucrose, lactose, maltose, ascorbic acid and uric acid. The sensors were then employed in detecting glucose in human blood plasma samples. The results show good correlation with the results from commercial machines.

Effect of carrier gas on response of oxide semiconductor gas sensor

Abstract It is known that response mechanism of an oxide semiconductor gas sensor based on a redox process occurs at the surface of the sensor. The present investigation used an alcohol gas sensor made of tin oxide as an example to demonstrate the role of oxygen molecules in carrier gases on the response of the sensor. It is found that high concentration of oxygen in the carrier gases reduced the sensor recovery time and background conductance, and enhanced its dynamic range and linearity of measurement. These results can be explained by a reaction model of oxidation by oxygen and reduction by reducing gas at the surface of the sensor.

Radial basis neural networks for identification of volatile organic compounds

Abstract In this paper, radial basis neural network (RB-NN) was proposed for the identification of volatile organic compounds (VOCs). The measuring system with four 20 MHz quartz crystal microbalances (QCMs) as sensors was used in the experiments. The four sensors were modified with SnCl2 and PdCl2 to change the response characteristics. A flow-through type system was used to measure the VOC samples including ethyl alcohol, acetone, chloroform, and de-ionized water. Rise-time, peak, and fall-time data from the response characteristic curves were used as information for training the neural networks. It was found that the RB-NNs could be learned faster and better than the conventional back-propagation neural networks (BP-NNs). The samples were clearly separated and recognized with the RB-NNs, which could not be done with the BP-NNs.

Micromorphology of Porosity Related to Electrical Resistance of Dental Luting Cements

The aim was to investigate the relation between micromorphology of porosity and electrical resistance of dental luting cements. Five dental luting cements were evaluated: zinc phosphate, glass ionomer, and three types of resin luting cements. Porosity of the specimen was analyzed by micro-CT and electrical resistance of cement was measured at voltage of 125 V up to 30 days and solubility of each specimen was calculated. It showed that the resin luting cements provided the highest electrical resistance regardless of amount of porosity. Zinc phosphate and glass ionomer had high porosity and the lowest resistance (14 and 3 kΩ, respectively). It was found that the electrical resistance of luting cement was not directly affected by the amount of porosity, but it seems to be related to pore connection. There is no correlation between electrical resistance and percentage of porosity but the morphology of porosity may have an influence on the electrical property of luting cement. Models of pore connection were proposed to explain the electrical resistance of luting cement.

Stacked probabilistic regularized LDA on partitioning non-stationary EEG data for left/right hand imagery classification

Electroencephalography (EEG) is a user interface for communicating with patients, especially for tasks such as classifying Left/Right hand to represent YES/NO. Although there were many proposed classification techniques, none of them considered the non-stationary characteristic of brainwaves; thus, they cannot really be employed in real-world situations. In this paper, we aim to tackle the non-stationary issue by capturing all change points in EEG signal and partitioning it into a set of signals. Then Regularized LDA is applied to each of the partitioning signals, resulting in stacked of classifiers. The experiment was conducted on the BCI Competition IV (2b) data set. Our proposed method was compared to nine baseline techniques in four groups: (i) static algorithms, (ii) variants of adaptive Pool Mean algorithms, (iii) Adaptive CSP, and (iv) Corrected Sequential EM. The results showed that our method yields higher accuracy and significantly outperformed all baselines.

Effect of Electron Acceptor in Bio-Fuel Cell

Bio-fuel cell (BFC) using bakers yeast was constructed to examine electricity production. Glucose was used as fuel and electricity was produced by the overall redox reaction in the cell. Neoseptareg membrane was used as proton exchange membrane for separating an anodic compartment and a cathodic compartment. As electron mediator, methylene blue was used for transporting electron from yeast to electrode. Besides, carbon fiber cloth and carbon paper were used as electrodes for comparing the cell performance. Combination of two types of electron acceptor at the cathodic compartment, i.e. potassium ferricyanide and oxygen, were used to optimize efficiency of the system. It was found that electron acceptor affected the voltage generated by the BFC. The best performance was obtained from the system using carbon fiber cloth electrodes and two types of electron acceptors. Open circuit voltage of 450 mV and short circuit current density higher than 165 mA/m2 were obtained. Power density of 18 mW/m2 was obtained at 750 Omega load