Abstract
1. Introduction
The human body is a remarkable integrated system with many attractive design features. A variety of functional devices inspired by humans have already been invented and applied into flexible intelligent robots, such as optoelectronics for computer vision[
Ever since the first electronic nose based on a gas sensor array (GSA) was introduced by Persaud and Dodd in 1981 to mimic the function of the mammalian olfactory system[
Figure 1.(Color online) Smart gas sensor array fabricated by different techniques assisted by artificial intelligence algorithms can find many real-life applications, exhibiting great potential as a mammalian olfactory system in the areas of air and water quality monitoring, non-invasive disease detection, and dangerous gases leakage alarming.
The two most important components of an electronic nose system are the GSA as the sensing material to chemical compounds and automated pattern recognition algorithms which are one of the most powerful tools in the AI era. Firstly, gas molecules can activate a signature or pattern on GSA containing a set of different gas sensing units[
2. Fabrication
Mammals can distinguish an immense range of inhaled odorants with the olfactory sensory organ made of a specialized olfactory neuroepithelium[
By imitating the special olfactory system of animals, researchers have developed plenty of approaches to achieve an electronic nose. An optical sensor system has been applied to the detection of carbon dioxide by measuring the absorbance in a special frequency range and a colorimetric sensor array has been demonstrated for the real-time indicators of analyte gases[
The sensing materials of the electronic nose above are usually fabricated by nanoengineering which can offer high surface-area-to-volume ratio for enhanced sensitivity[
3. Smart gas sensor arrays
The implementation of a chemosensory array to detect and discriminate chemical compounds including volatile organic compounds (VOCs) and other air pollutants, from the surroundings and convert them to electrical signals has attracted tremendous interest in recent years. This type of gas sensor array possesses special advantages of chip-scale miniaturization and ease-of-operation[
where M denotes the specific measurement metric used.
Recent efforts have been focused on developing nanostructured sensors with low-power consumption (operating at room temperature). Here we summarize representative achievements about fabrication methods and applications of GSA.
3.1. MOX gas sensor array
A variety of metal oxides (MOX) can be employed as gas sensing materials by simply measuring the change of resistance. Normally, ambient oxygen molecules will attract free electrons in MOX materials, forming oxygen ions. In this process free electrons inside the materials are consumed, resulting in band bending and an electron depletion region at the materials surfaces. The target gas molecules are either acting as reducing gas or oxidizing gas, corresponding to donor or acceptor of charge carriers. When the materials are placed in the environment of these gases, the resistance of the semiconductor metal oxide will increase or decrease depending upon the type of majority carriers in the sensing materials and types of target gases[
Gao[
The bottleneck factors impeding further development of MOX-based electronic nose attribute to: 1) normally in order to achieve decent sensitivity, fast response and recover time, and provide enough energy for the desorption of gases, gas sensors usually operate at high temperature (around 500 °C). Therefore, a power source is needed to provide enough power for the large scale of GSAs which consumes a great amount of electric power for real-life application. This leads to the research interests of developing devices working at room temperature. 2) Cross sensitivity problems produce interfering signals, because MOX films are sensitive to a wide variety of molecules and background interference in real-life application is always complex. To solve this problem, carefully picking up the sensor units will provide data without redundancy and pre-treatment of samples or a standard testing process may also be required. 3) The humidity level and temperature level affect sensors’ signals significantly and furthermore the humidity and temperature of environmental air fluctuate unpredictably. Reaction between surface oxygen and water molecules will cause a shift in the baseline resistance and deteriorate the sensitivity of MOX sensors[
In recent years, the MOX gas sensor array has been extensively studied in selective detection of volatile organic compounds (VOCs) as biomarkers of human diseases. In earlier years, Wang et al.[
Figure 2.(Color online) MOX sensor array fabrication method and morphology[
In addition to FSP methods discussed above, Fan’s group proposed another method called ultrasonic spray pyrolysis (USP) as shown in Fig. 2(d) for fabricating ultra-low-power-consumption 3-dimensional SnO2 nanotube arrays[
For the above GSA devices, the largely increased sensitivity and decent room-temperature performance are attributed to high surface-to-volume ratio provided by porous film or nanostructure. Therefore, further research efforts can help to uncover the physicochemical process behind the phenomenon and create more versatile nanostructures with large surface area[
3.2. Nanoparticles and nanowires gas sensor array
It is worth mentioning that other materials such as metal or metal nanoparticles can also be employed as a unit of gas sensor array for many applications[
Figure 3.(Color online) (a) Sensor array fabrication methods and morphology[
GSA urgently needs versatile and robust fabrication methods for chemical and biological sensing. Field-effect transistor (FET) technology is a candidate with great potential in the sensor array manufacturing area[
Combinations of different technologies can act favourably on gas sensing performance. The selectivity of a gas sensor can be accomplished by specificity of organic materials while the robustness can be achieved by inorganic materials. The signals are captured and amplified by FET for further processing. In summary, gas sensor arrays show great potential for real-world application with versatile fabrication methods, low-power consumption, and high sensitivity and selectivity.
4. AI algorithms
Algorithms applied to GSAs resemble the human neural system connected to the human nose, being the indispensable part of the whole electronic nose system. Algorithms for mature electronic nose systems typically consist of three parts: 1) drift calibration algorithms, 2) signal pre-processing and feature extraction part, 3) pattern recognition system that can recognize the odours of chemical compounds.
Most gas sensors including MOX suffered a lot from drift issues consisting of small and non-deterministic temporal variations of the sensor response when they are exposed to the same analytes under identical conditions. Drift is caused by different reasons for different kinds of gas sensing materials, for example, the drift of MOX gas sensors is attributed to chemical diffusion of oxygen vacancies (as the simultaneous transport of oxygen vacancies and conduction electrons, the conductivity changes in the bulky or space charge layer of MOX grains). To deal with the drift issues of a sensor and ensure signal repeatability over time, after a period of time (several weeks) the artificial electronic nose system must be completely re-calibrated to ensure valid results. Cho et al.[
Following drift calibration, the next step of data analysis is pre-processing of the data collected from GSA which can form patterns of target gases, similar to the mammalian olfactory system. In this step, important features, such as sensitivity, response and recovery time, the slope at t10, t50, t90 of response and recovery curve can be extracted from the raw data. Next, redundancy of the data is removed by data analysis methods in which principal component analysis (PCA)[
The following step after the pre-processing of data is applying pattern recognition and classification algorithms, thus providing more insights into the pattern generated by GSA. The simplest method to process the data is to compare the testing data to those of known sources in the knowledge base through graphical evaluation including bar charts, profiles polar and offset polar plots[
Pattern recognition algorithms can be divided into several categories according to certain standards as summarized in Fig. 4. Basically, classification algorithms of gas sensors can be classified into linear/non-linear algorithms or supervised/unsupervised algorithms. PCA can reduce high dimensionality of multivariate data into smaller dimensions and then k-nearest neighbours (KNN) will sort out different data. KNN takes into account the Euclidean distance from the test sample to each of its k nearest neighbours among all stored training samples, and then form a clustering of similar samples[
Figure 4.(Color online) Artificial intelligence algorithms adopted in a gas sensor array.
Artificial neural networks (ANNs) inspired by animal brains is a type of algorithm quite popular in recent years due to the benefits of greatly improved computation power and big data. It is a data driven self-adaptive method in that they can adjust themselves to the data without any explicit definition of the mathematical form for the underlying model and have potential to work as universal functional approximations with arbitrary accuracy. Up to now, ANN has found particular success in information processing areas such as computer vision, speech recognition, and natural language processing. Artificial olfactory system, equivalent to the human organ for smelling, will certainly prevail when powered by ANN. Pai Peng et al.[
5. Applications
In the past four decades since Persaud and Dodd’s work in 1981[
5.1. Disease diagnosis
In ancient China, smell, one of the four disease diagnostic methods (observation, olfaction, inquiry, palpation), has already been applied into disease diagnosis. With the fast development of modern measuring-instrument engineering and medical science, smell is fading from the stage of disease diagnosis because of the insufficient knowledge of the relationship between diseases and exhaled gases, and measurement inaccuracy of equipment. However, modern medical diagnosis methods such as chest X-ray, computerized tomography and biopsy may cause damage to the human body. Besides, the first two are expensive and emit radiation. Biopsy is usually painful with frequent complications. Benefiting from advanced technology, clinical trials demonstrated the possible relationship between diseases and corresponding gas species[
Figure 5.(Color online) Application of gas sensor array [
An ideal GSA for disease diagnosis of breath testing should be sensitive to low concentrations (tens of ppb) of VOC as thousands of VOCs in breath are mostly in ppb levels[
(1) Exhaled acetone level is a sign of diabetes, especially type I diabetes. Reverse correlations between blood glucose and breath acetone concentration has been established[
(2) Gang Peng et al.[
(3) Parkinson’s has been identified to have a distinct volatile-associated signature, including altered levels of perillic aldehyde and eicosane, through a comprehensive analysis of sebum from Parkinson’s disease patients[
More disease related breath VOCs can be looked up in Fig. 5(c), which is a concentration map for different diseases.
While important milestones have been established in the field of breath testing up to now, there is still much to accomplish in this field. Practical use of sensor arrays is impeded by individual difference which causes difficulties in obtaining a generalizable result. More systematic study or algorithms need to be considered to deal with it. Further check-up of correlations between exhaled breath and diseases should be performed in the medical fields. The false negative rate of diagnosis results by electronic nose should be minimized. Critical parameters such as low-concentration-VOC sensitivity, selectivity, power-consumption should also be optimized. A combination of different technologies will increase the accuracy of VOC detection to a large scale and have the potential to solve the cross-sensitivity problems of compounding factors, enabling miniaturization and integration of medical devices. In the near future, the GSA will even be able to fit into smart phone for daily applications. In conclusion, disease diagnosis by GSA will require and in return boost the development of several areas including medicine, material science, and electronics.
5.2. Environmental monitoring
GSA nowadays is of great interest in environmental monitoring as a result of people’s increasing concerns of health impact of air pollution. Meanwhile, a sensor array is capable of detecting and discriminating a wide variety of gases such as CO (resulting in smog formation), CO2 (the leading greenhouse gases), NOx (a toxic gas), SOx (acid rain), and ammonia (caustic and hazardous). Traditional methods for air quality monitoring by analytical instruments such as GC-MS are not suitable due to not being on-site, real-time, and cost-effective. To tackle these issues, GSAs can work as an alternative to GS-MS in evaluating the existence of pollutants in air and water. The number and types of sensor units are selected generally based on different application cases.
By means of GSA, Abdullah et al.[
As there are various sources of environmental problems including both anthropogenic sources and natural sources, GSAs can be employed to build a sensor network in smart city design. Recently, specific chemical molecules have been used as a sign of air quality. By analysing the response pattern of the whole sensor array, the pollutants emission sources can be located. However, despite sensor array responses showing appreciable correlation to chemical compound species and concentration, it is also heavily influenced by the humidity level of the ambient environment[
GSA can be used not only in air quality monitoring, but also for the measurement of “biological oxygen demand” (BOD) concentration in water, a gauge of waste water treatment effectiveness, by analysing the headspace (air space over the water surface) of water[
As discussed above, the application of GSA in real-life cases is still considerably problematic as the environmental condition including temperature and humidity are changing all the time accompanied with complex chemical compounds in ambient air. All in all, due to the unique superiority of GSA including on-site real-time continuous monitoring capability and low-cost, easy to operate features, it is certain to be preferable as a solution for environmental monitoring.
5.3. Smart home and smart city
The IoT industry is growing vigorously nowadays and foresees even greater opportunities in the future with 5G network enabling more equipment to connect into network. A typical application of GSA in this field is to cooperate with smart phone applications for building a smart home, and to form sensor networks for smart buildings and smart cities.
Typical scenarios of such field are gas leakage detection, indoor air quality control, explosive detection, and environmental safety monitoring[
Portable and scalable GSA devices are of special importance for the purposes outlined above. Fig. 5(c) shows a GPRS-based gas sensor network in smart city plan and Fig. 5(d) depicts real-time indoor gas monitoring by smart phone in smart building which consists of SnO2 nanotube array as sensing materials, data transmission unit and data processing application in smart phone with user interface. This platform makes it possible to detect the chemical environments at home in a real-time and interactive manner.
6. Conclusion
GSA coupled with AI greatly promotes the development of the electronic nose. Low-power-consumption and chip-scale devices can be manufactured by various scalable techniques including FSP, USP, and FET fabrication techniques. As a result, such a device has the potential to be placed at our home and operated by ourselves without the need of skilled specialists. In addition to the original purpose of imitating human odor sensing, GSA can also be utilized to detect odorless gas including explosives and toxics. GSAs are application-oriented devices, thus, to arrange a GSA system with minimum redundancy, one needs to select each type of sensor unit based on the target gas molecules, requiring prior knowledge about the chemical compounds to be detected in the corresponding scenarios.
Research efforts around GSA in the future may fall into several parts. Devices working at room temperature are highly expected for package simplification and continuous working mode with ultra-low-power consumption[
Acknowledgements
The work was supported by the Hong Kong Innovation and Technology Fund (ITS/115/18) from the Innovation and Technology Commission, and Shenzhen Science and Technology Innovation Commission (Project No. JCYJ20180306174923335).
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