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Summarizes some of the most commonly used gas sensors and describes how each one works. Covers solid state gas sensors; aqueous electrochemical gas sensors; paramagnetic gas sensors, photometric gas sensors; thermal conductivity gas sensors and fibre‐optic gas sensors.

Describes a new catalytic gas sensor which claims high stability and low‐consumed power by using extra fine wires of precious metals and the addition of a support stabilizer and pore‐making agents. Outlines the experiment methods and materials used, the principles of detection and the performance of the catalytic elements. Concludes that with the addition of a support stabilizer which makes the specific surface of the support decrease slowly during use, and a pore‐making agent which can eliminate the effect of the internal diffusion on the sensor, the novel catalytic sensor can be widely applied to the detection of damp gas in coal mines and various combustible gases on the earth’s surfaces.

Atmospheric dependent, gas sensitive resistors seem to be good candidates for detecting critical air pollution levels. Recently, great progress has been made in the development of various sensor types, but less attention seems to be paid to the integration of sensor elements with different characteristics. The aim of this international project is to develop a smart hybrid gas multi‐sensor module for environmental applications, i.e. by combining classical thick‐ and thin‐film elements with polymer‐film based sensors and also a signal processing ASIC within a single package, which should be useful for all sensor types. The module should enable multi‐sensor operation as well, when connected to an intelligent signal‐processing unit.

The purpose of this paper is to design and validate an electronic nose (E-nose) prototype using commercially available metal oxide gas sensors (MOX). This prototype has a sensor array board that integrates eight different MOX gas sensors to handle multi-purpose applications. The number of sensors can be adapted to match different requirements and classification cases. The paper presents the validation of this E-nose prototype when used to identify three gas samples, namely, alcohol, butane and cigarette smoke. At the same time, it discusses the discriminative abilities of the prototype for the identification of alcohol, acetone and a mixture of them. In this respect, the selection of the appropriate type and number of gas sensors, as well as obtaining excellent discriminative abilities with a miniaturized design and minimal computation time, are all drivers for such implementation.

The suggested prototype contains two main parts: hardware (low-cost components) and software (Machine Learning). An interconnection printed circuit board, a Raspberry Pi and a sensor chamber with the sensor array board make up the first part. Eight sensors were put to the test to see how effective and feasible they were for the classification task at hand, and then the bare minimum of sensors was chosen. The second part consists of machine learning algorithms designed to ensure data acquisition and processing. These algorithms include feature extraction, dimensionality reduction and classification. To perform the classification task, two features taken from the sensors’ transient response were used.

Results reveal that the system presents high discriminative ability. The K-nearest neighbor (KNN) and support vector machine radial basis function based (SVM-RBF) classifiers both achieved 97.81% and 98.44% mean accuracy, respectively. These results were obtained after data dimensionality reduction using linear discriminant analysis, which is more effective in terms of discrimination power than principal component analysis. A repeated stratified K-cross validation was used to train and test five different machine learning classifiers. The classifiers were each tested on sets of data to determine their accuracy. The SVM-RBF model had high, stable and consistent accuracy over many repeats and different data splits. The total execution time for detection and identification is about 10 s.

Using information extracted from transient response of the sensors, the system proved to be able to accurately classify the gas types only in three out of the eight MQ-X gas sensors. The training and validation results of the SVM-RBF classifier show a good bias-variance trade-off. This proves that the two transient features are sufficiently efficient for this classification purpose. Moreover, all data processing tasks are performed by the Raspberry Pi, which shows real-time data processing with miniaturized architecture and low prices.

This review paper aims to focus on recent advances of carbon nanotubes (CNTs) to produce gas sensors. Gas sensors are widely used for monitoring hazardous gas leakages and emissions in the industry, households and other areas. For better safety and a healthy environment, it is highly desirable to have gas sensors with higher accuracy and enhanced sensing features.

In this review, the authors focus on recent contributions of CNTs to the technology for developing different types of gas sensors. The design, fabrication process and sensing mechanism of each gas sensor are summarized, together with their advantages and disadvantages.

Nowadays, CNTs are well-known materials which have attracted a significant amount of attention owing to their excellent electrical, electronic and mechanical properties. On exposure to various gases, their properties allow the detection of gases using different methods. Therefore, over recent years, researchers have developed several different types of gas sensors along with other types of sensors for temperature, strain, pressure, etc.

The main purpose of this review is to introduce CNTs as candidates for future research in the field of gas sensing applications and to focus on current technical challenges associated with CNT-based gas sensors.

This paper reviews the range of sensors used in electronic nose (e‐nose) systems to date. It outlines the operating principles and fabrication methods of each sensor type as well as the applications in which the different sensors have been utilised. It also outlines the advantages and disadvantages of each sensor for application in a cost‐effective low‐power handheld e‐nose system.

Describes research into methods for treating solid state gas sensors to create a multisensor system with both multi‐gas analysis and self‐diagnostic capabilities. Outlines the three main categories of solid state gas sensors and then discusses the need for a systematic approach to semiconductor sensor design and the phenomenology of new sensor materials. Concludes that theoretically a self‐diagnostic sensor array device can be conceived for utilisation in gas sensing.

This paper aims to give an overview about the state of the art and novel technologies used in gas sensing. It also discusses the miniaturization potential of some of these technologies in a comparative way.

In this article, the authors state the most of the methods used in gas sensing discuss their advantages and disadvantages and at last the authors discuss the ability of their miniaturization comparing between them in terms of their sensing parameters like sensitivity, selectivity and cost.

In this article, the authors will try to cover most of the important methods used in gas sensing and their recent developments. The authors will also discuss their miniaturization potential trying to find the best candidate among the different types for the aim of miniaturization.

In this article, the authors will review most of the methods used in gas sensing and discuss their miniaturization potential delimiting the research to a certain type of technology or application.

This paper aims to study different possibilities for implementing easy-to-use and cost-effective micro-systems to detect and trace expelled gases from rotten food. The paper covers various radio-frequency identification (RFID) technologies and gas sensors as the two promoting feasibilities for the tracing of packaged food. Monitoring and maintaining quality and safety of food in transport and storage from producer to consumer are the most important concerns in food industry. Many toxin gases, even in parts per billion ranges, are produced from corrupted and rotten food and can endanger the consumers’ health. To overcome the issues, intelligent traceability of food products, specifically the packaged ones, in terms of temperature, humidity, atmospheric conditions, etc., has been paid attention to by many researchers.

Food poisoning is a serious problem that affects thousands of people every year. Poisoning food must be recognized early to prevent a serious health problem.

Contaminated food is usually detectable by odor. A small gas sensors and low-cost tailored to the type of food packaging and a communication device for transmitting alarm output to the consumer are key factors in achieving intelligent packaging.

Conducting polymer composite, intrinsically conducting polymer and metal oxide conductivity gas sensors, metal–oxide–semiconductor field-effect transistor (MOSFET) gas sensors offer excellent discrimination and lead the way for a new generation of “smart sensors” which will mould the future commercial markets for gas sensors.

Small size, low power consumption, short response time, wide operating temperature, high efficiency and small area are most important features of introduced system for using in package food.

Gas transmission pipelines are at constant risk of gas leakage or fire due to various atmospheric environments, corrosion on pipe metal surfaces and other external factors. This study aims to reduce the human and financial risks associated with gas transmission by regularly monitoring pipeline performance, controlling situations and preventing disasters.

Facility managers can monitor the status of gas transmission lines in real-time by integrating sensor information into a building information modeling (BIM) 3D model. Using the Monitoring Panel plugin, coded in C# programming language and operated through Navisworks software, the model provides up-to-date information on pipeline safety and performance.

By collecting project information on the BIM and installing critical sensors, this approach allows facility manager to observe the real-time safety status of gas pipelines. If any risks of gas leakage or accidents are identified by the sensors, the BIM model quickly shows the location of the incident, enabling facility managers to make the best decisions to reduce financial and life risks. This intelligent gas transmission pipeline approach changes traditional risk management and inspection methods, minimizing the risk of explosion and gas leakage in the environment.

This research distinguishes itself from related work by integrating sensor data into a BIM model for real-time monitoring and providing facility managers with up-to-date safety information. By leveraging intelligent gas transmission pipelines, the system enables quick identification and location of potential hazards, reducing financial and human risks associated with gas transmission.