Gold nanoblock-enhanced MIM waveguide sensor for simultaneous detection of gas and liquid concentrations

Methodology for estimating ethanol concentration with artificial intelligence in the presence of interfering gases and measurement delay

A novel gas recognition and concentration detection algorithm consisting of a dynamic wavelet convolutional neural network (DWCNN) and a many-to-many long short-term memory-recurrent neural network (LSTM-RNN), respectively, is proposed as a replacement for the traditional data processing algorithm in artificial olfaction. The proposed DWCNN gas recognition algorithm does not require gas signal preprocessing, and it directly converts the raw time-domain gas signal data to 64 * 64 2-D gray images as the input layer of a convolutional neural network (CNN). The experiments show that the recognition accuracy of CO, H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , and the gas mixture of CO and H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is nearly 100%, and the many-to-many LSTM-RNN algorithm requires only a few labeled data from the steady-state values of the gas sensor array signals. In addition, comparisons with other neural network multilayer perceptrons (MLPs), gated recurrent unit (GRU) algorithms, and conventional algorithms, such as the Bayesian ridge, support vector machines (SVMs), decision tree, k-nearest neighbor (KNN), random forest, AdaBoost, gradient-boosting decision tree (GBDT), and bagging, revealed that the algorithm can obtain a higher concentration detection accuracy, which was evaluated using two different kernel functions for the kernel principal component analysis (KPCA) dimensionality reduction: polynomial and rbf. The experimental results demonstrated that the proposed many-to-many LSTM gas concentration detection model outperformed the abovementioned algorithms and can more accurately estimate the concentration of different gases while using less labeled data.

As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH4 as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH4 concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.

This study presents the development of an omnidirectional robot for the detection of combustible and toxic gases. The purpose of this study is to fabricate a robot that can detect combustible and toxic gases that are harmful to the human body. The design of the robot aims to limit the chances of exposure of humans from hazardous gases. The omnidirectional robot is designed to have the mobility to traverse on confined and cramped spaces such as ducts and pipes where gas leakages can occur. This study consists of designing, programming, GUI development, fabrication, approximation of gas concentrations, preliminary tests, sensor tests, and actual tests. The robot is based on existing designs and mecanum wheels are used for the omnidirectional function. The robot is programmed using Arduino microcontroller for motor control and sensor readings. For gas detection, gas sensors such as MQ3, MQ7 and MQ9 were used. The robot was tested based on its mobility and capability in determining specific gas concentrations. There are six gases that the robot can detect namely alcohol, benzene, carbon monoxide, hydrogen, methane, and LPG. Results showed that the omnidirectional feature of the robot allows it to traverse obstacles with ease. In addition, the developed robot shown its capability in detecting gas concentrations using preliminary and actual tests. The actual tests were conducted on a commercial building, basement parking, and printing company.

Ultrasonic technology is widely used in the field of gas detection due to its advantages of low power consumption, high speed and strong adaptability to the environment. The current ultrasonic time-of-flight (TOF) measurement method has the large measurement error caused by ringing effect, and the phase detection method can only detect the phase change within 2π of a single cycle. These problems cannot meet the requirements of certain high concentration gas detection. This paper proposes a method of multi-frequency ultrasonic phase difference measurement to solve the problem that the phase difference across multiple cycles cannot be detected, hereby realizing full range gas concentration detection, and the continuous wave detection of this method also eliminates ringing effect in TOF. The phase difference within 2π of a single period is obtained by loading a single frequency driving signal on two channels, and the phase difference of low-frequency envelope of the modulated signal is obtained by loading multi-frequency modulated signals. The total cross-cycle phase difference can be obtained by combining the two results, and the measured gas concentration can be obtained by the relationship model between gas concentration and phase difference. The experimental results show that the average absolute error of 4% hydrogen concentration measurement is 0.12%, and the relative error of 99% hydrogen measurement is less than 5%. This method also provides a solution for extracting cross-cycle phase difference in other fields.

Superhydrophobic surfaces (SHS), created based on a combination of surface texture and hydrophobic chemistry, have a variety of applications from reducing drag to protecting underwater surfaces from icing, corrosion, and biofouling. These applications mainly rely on the presence of micro/nano-scale gas bubbles trapped within the surface texture when the SHS contacts with the liquid. However, the gas on the SHS can be slowly dissolved by the surrounding liquid if the liquid is undersaturated with gas. Once all the gas is dissolved, the SHS is a purely rough surface and loses most of the aforementioned benefits. This thesis aims to understand the longevity of SHS in undersaturated liquid due to the effect of gas diffusion. First, we developed an experimental method which improved the measurement accuracy of SHS longevity compared to prior works. The higher measurement accuracy in our experiments was achieved for three reasons: (i) to measure the status of gas on SHS, we applied a non-intrusive optical method that did not disturb the gas diffusion process; (ii) to address the nonuniform gas diffusion at different regions of the SHS, we measured the SHS longevity based on the gas status at the entire SHS sample; and (iii) we induced the gas diffusion by using liquid with a low dissolved gas concentration so that the stability of SHS was not affected by pressure. Second, we studied the influence of the undersaturation level of the liquid on the SHS longevity, which is an open question in the literature. We defined the undersaturation level of liquid s as the ratio of the gas concentration in the liquid to the gas concentration at the gas-liquid interface. We found that the SHS longevity tƒ and the undersaturation level s followed a power-law relation: tƒ~(1-s)ˉ², which is in good agreement with a previous numerical model. This scaling relation suggested that as gas slowly dissolves into the liquid, the gas concentration in liquid near the SHS increases, and the mass flux of gas from SHS to the liquid decreases. We also found that the diffusion length, representing the height of the liquid affected by gas dissolution, was inversely proportional to the undersaturation level. Lastly, we studied the influences of liquid pressure and surfactants on the longevity of the SHSs. Overall, this study improved our understanding of SHS longevity in undersaturated liquids and could guide the applications of SHS for reducing icing, corrosion, and biofouling.

Dynamic inversion and correction of ammonia pressure within the range 6611–6614 cm−1

Interdigital structure with thin film sensitive organic layer is the base of many sensors for gases and vapours detection. This structure with sensitive layer has capacitive character where the influence of gas and vapour causes the change of its capacitance and dissipation factor. Resonance circuit, created by interdigital structure and an inductor, is then suitable for gas and vapour concentration detection. This article describes methods of relative humidity detection from the resonance circuit curve and presents many experimental results as well.

According to the environment in mines and the infrared spectrum technique advantage, It is adopted widely to apply the principle of infra-red spectrum by counting the coefficient of methane gas and the gas concentration curve, then transferring the methane gas to light signal, at last achieving the goals to detect the gas concentration based on the infra-red spectrum.

Based on the photoacoustic spectroscopy technology, this research designed a photoacoustic cell system for detecting gas concentration. By injecting a beam of periodically modulated light, the light reacts with the mixed gas. The gas molecules absorb the light and transition from the ground state to the excited state and then release heat. The periodically changing heat will generate periodically changing pressure, thereby generating sound waves. After receiving the sound wave through a microphone, the gas concentration can be calculated by analyzing the collected signal. This article analyzes the relationship between different gas concentration detection and amplitude output.

A novel ultra-sensitive Parity-Time symmetry based Graphene FET (PT-GFET) sensor is studied for gas concentration detection. The PT-GFET sensor effectively integrates the sensitivity of the PT symmetry around its Exceptional Point (EP) and the tunability of the GFET conductance. The change of GFET conductance with the gas concentration can be brought back to the EP of the PT-GFET by tuning the gate voltage on the GFET. Thus, the applied gate voltage indicates the gas concentration. The minimum detectable gas concentration has been derived and estimated based on the experimental data, which shows that PT-GFET sensors can detect gas concentration below 50 ppb.

The performance of an erbium-doped fiber ring laser based intra-cavity absorption gassensor was evaluated with performance enhanced techniques. A multi-line wavelength sweep techniqueand a weighted averaging technique were proposed for better gas detection. By selecting appropriatesystem parameters, 6 strong absorption lines near 1 530 nm of C2H2 were obtained with good spectrumresolution in one scanning period. One group with higher absorption coefficients was used for relativelylow gas concentration detection and the other with lower absorption coefficients was used for relativelyhigh gas concentration. Both the groups can be used for medium gas concentration detection. For variousconcentration cases, by choosing proper absorption lines and performing weighted averaging, detectionaccuracy can be obtained over an extended detection range. The minimum detection limit could be verylow after optimization.

Fast switching hydrogen gas leakage identification using FPGA

Dissolved gas analysis (DGA) is technique to relate the transformer fault with the gases dissolved in the insulating oil of oil-immersed transformer, which can be used to analyze some of the faults inside the transformer. The on-line DGA system designed in this article adopts the polymer film to extract the gases dissolved in oil, uses the gas sensors to detect the gas concentrations and the optical fibers to transmit the gas concentration information to the IPC in the control room. The system has been installed for more than one month, and the measurement results have shown that the maxim error of the method in gas concentration detection is 11% compared with the off-line chromatography. The method presented in this article is succeeding in improving the security situation of the transformer in operation at a low cost.

Based on the photoacoustic spectroscopy technology, this research designed a photoacoustic cell system for detecting gas concentration. By injecting a beam of periodically modulated light, the light reacts with the mixed gas. The gas molecules absorb the light and transition from the ground state to the excited state and then release heat. The periodically changing heat will generate periodically changing pressure, thereby generating sound waves. After receiving the sound wave through a microphone, the gas concentration can be calculated by analyzing the collected signal. This paper analyzes the principle of harmonic detection and analyzes the relationship between input thermal amplitude and sound pressure.