Identification and quantification of gases and their mixtures using GaN sensor array and artificial neural network

Khan, Ashfaque Hossain; Motayed, Abhishek; Rao, Mulpuri V.

doi:10.1088/1361-6501/abd5f0

Cited by 16 publications

(6 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial neural networks (ANNs) have been highly efficient to capture the non-linear response pattern of gas mixtures. In another study, various artificial neural network (ANN) algorithms were trained and tested for the identification and quantification of gas mixtures based on GaN nanowires [73]. A back-propagation neural network model was found to be the optimal classifier among all the considered ANN algorithms based on the statistical and computational complexity results.…”

Section: Machine Learning Algorithms On Gan Nanostructured Sensorsmentioning

confidence: 99%

Gallium Nitride (GaN) Nanostructures and Their Gas Sensing Properties: A Review

Khan

Rao

2020

Sensors

Self Cite

View full text Add to dashboard Cite

In the last two decades, GaN nanostructures of various forms like nanowires (NWs), nanotubes (NTs), nanofibers (NFs), nanoparticles (NPs) and nanonetworks (NNs) have been reported for gas sensing applications. In this paper, we have reviewed our group’s work and the works published by other groups on the advances in GaN nanostructures-based sensors for detection of gases such as hydrogen (H2), alcohols (R-OH), methane (CH4), benzene and its derivatives, nitric oxide (NO), nitrogen dioxide (NO2), sulfur-dioxide (SO2), ammonia (NH3), hydrogen sulfide (H2S) and carbon dioxide (CO2). The important sensing performance parameters like limit of detection, response/recovery time and operating temperature for different type of sensors have been summarized and tabulated to provide a thorough performance comparison. A novel metric, the product of response time and limit of detection, has been established, to quantify and compare the overall sensing performance of GaN nanostructure-based devices reported so far. According to this metric, it was found that the InGaN/GaN NW-based sensor exhibits superior overall sensing performance for H2 gas sensing, whereas the GaN/(TiO2–Pt) nanowire-nanoclusters (NWNCs)-based sensor is better for ethanol sensing. The GaN/TiO2 NWNC-based sensor is also well suited for TNT sensing. This paper has also reviewed density-functional theory (DFT)-based first principle studies on the interaction between gas molecules and GaN. The implementation of machine learning algorithms on GaN nanostructured sensors and sensor array has been analyzed as well. Finally, gas sensing mechanism on GaN nanostructure-based sensors at room temperature has been discussed.

show abstract

Section: Machine Learning Algorithms On Gan Nanostructured Sensorsmentioning

confidence: 99%

Gallium Nitride (GaN) Nanostructures and Their Gas Sensing Properties: A Review

Khan

Rao

2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…By and by, pushed noses have offered outside focal points to a variety of corporate ventures, agriculture, biomedical, someone very, environmental, food, water, and various helpful research disciplines. Electronic nostrils have been employed in a variety of corporate green-related initiatives, including agronomy, biochemical handling, plant science, cell customisation, and plant cultivar selection [5]. Pollution can take the form of a mixture of chemicals, such as solid particles, liquid dots, or gasoline [6], as well as a characteristic that combines commotion, warmth, and light.…”

Section: Introductionmentioning

confidence: 99%

Detection of harmful gases present in the environment

Rai

Saeed

2023

IJEECS

View full text Add to dashboard Cite

The electronic nose (e-nose) is demonstrated in this research for detecting and identifying several forms of hazardous gases. We describe an e-noses for detecting several gases, including butane, acetone, methane, and ethanol. For dimensionality reduction in 3D representation, data processing approaches are based on the partial least square (PLS) method. The suggested system can be utilised for sensor optimization since different sensors with varied operating temperatures can be tested in many devices to find the best array for a specific detection or application. The results reveal that, depending on the sensor array characteristics, varying success rates in classification can be attained when discriminating contaminants. The preceding criteria lead to a new search for a portable, dependable, low-cost, and most efficient gas sensor. The major purpose of this study is to create a gas sensor array that can detect and monitor toxic and poisonous gases in the environment, as well as warn against dangerous organic compounds. Our goal is to create a sensor system that can distinguish the most significant decontamination gases while also being highly responsive, precise, low-effort, and low-power demanding.

show abstract

“…It can be very useful to indicate a predictive relationship of temporal time-series data, which can be exploited for real-time applications. Among the state-of-the-art machine learning models, ANN has been increasingly used in various engineering fields including the gas identification area owing to their excellent architecture flexibility and extensibility [ 26 , 28 , 29 , 30 , 31 , 32 , 33 ]. The recurrent neural network (RNN), especially its variants so-called long short-term memory neural network (LSTM) and gated recurrent unit (GRU), is capable of capturing the temporal characteristics of the target system.…”

Section: Introductionmentioning

confidence: 99%

Early-Stage Gas Identification Using Convolutional Long Short-Term Neural Network with Sensor Array Time Series Data

Zhou

Liu

2021

Sensors

View full text Add to dashboard Cite

Gas identification/classification through pattern recognition techniques based on gas sensor arrays often requires the equilibrium responses or the full traces of time-series data of the sensor array. Leveraging upon the diverse gas sensing kinetics behaviors measured via the sensor array, a computational intelligence- based meta-model is proposed to automatically conduct the feature extraction and subsequent gas identification using time-series data during the transitional phase before reaching equilibrium. The time-series data contains implicit temporal dependency/correlation that is worth being characterized to enhance the gas identification performance and reliability. In this context, a tailored approach so-called convolutional long short-term memory (CLSTM) neural network is developed to perform the identification task incorporating temporal characteristics within time-series data. This novel approach shows the enhanced accuracy and robustness as compared to the baseline models, i.e., multilayer perceptron (MLP) and support vector machine (SVM) through the comprehensive statistical examination. Specifically, the classification accuracy of CLSTM reaches as high as 96%, regardless of the operating condition specified. More importantly, the excellent gas identification performance of CLSTM at early stages of gas exposure indicates its practical significance in future real-time applications. The promise of the proposed method has been clearly illustrated through both the internal and external validations in the systematic case investigation.

show abstract

Identification and quantification of gases and their mixtures using GaN sensor array and artificial neural network

Cited by 16 publications

References 32 publications

Gallium Nitride (GaN) Nanostructures and Their Gas Sensing Properties: A Review

Gallium Nitride (GaN) Nanostructures and Their Gas Sensing Properties: A Review

Detection of harmful gases present in the environment

Early-Stage Gas Identification Using Convolutional Long Short-Term Neural Network with Sensor Array Time Series Data

Contact Info

Product

Resources

About