On-Site Detection of Volatile Organic Compounds: Comparison
View Latest Version
Please note this is a comparison between Version 4 by Jessie Wu and Version 3 by Jessie Wu.

Volatile organic compounds (VOCs) are of interest in many different fields. Among them are food and fragrance analysis, environmental and atmospheric research, industrial applications, security or medical and life science. In the past, the characterization of these compounds was mostly performed via sample collection and off-site analysis with gas chromatography coupled to mass spectrometry (GC-MS) as the gold standard. While powerful, this method also has several drawbacks such as being slow, expensive, and demanding on the user. For decades, intense research has been dedicated to find methods for fast VOC analysis on-site with time and spatial resolution. The working principles of the most important, utilized are presented, and researched technologies for this purpose and highlight important publications from the last five years. 

  • volatile organic compounds
  • on-site detection
  • sensors

1. Nonselective Gas Sensors

There are several types of gas sensors that can be used to measure volatile analytes. These sensors are mostly used to measure the total volatile organic compound (VOC) content due to their nonselective nature. Most of these sensors can usually just detect the presence and concentration of volatiles in the air but not speciate them. The most used types of sensors that can be included in this category are photoionization detectors (PID) electrochemical sensors (ECS) or metal oxide sensors (MOS).

1.1. Photoionization Detectors

PIDs use light, typically in the UV range, to ionize gas molecules. The energy of the photons is enough to ionize most organic compounds but not the main constituents of air. Measurable compounds include almost all VOCs and some inorganic molecules such as ammonia and hydrogen sulfide but notably not methane and other very low molecular weight VOCs. The electric current produced by the ions on an electrode constitutes the detector output. They are commonly used to monitor the exposure of workers in different settings to VOCs.
Even though PIDs have been in use for decades, work on this technique continues to this day. Covington and Agbroke described a PID sensor that can discriminate between some VOCs and provide at least some compositional information [1]. Pang et al. explored the use of PIDs to replace an FID as a GC detector. They found it to perform similar with the advantage of better portability and independence from hydrogen. Additionally, options for the use of different lamps were explored [2]. The possibility of a standalone PID to discriminate between volatiles was investigated by Spadi et al. They were able to classify different rosemary species by looking into the temporal data acquisition of the PID. The temporal kinetics of the VOC emission were found to be distinct for each species and a ‘fingerprint’ for each variety could be obtained [3].
Similar to PID is a flame ionization detector (FID), although an FID uses a hydrogen flame to ionize the VOCs. FIDs are most commonly known as GC detectors but can also be utilized as stand-alone instruments. In contrast to a PID, an FID responds better to carbon chains while a PID is better suited for the detection of functional groups [4]. The signal is proportional to the number of non-oxidized carbon atoms. Both detectors are commonly used as total VOC detectors. However, even though FIDs are still frequently used in total VOC analysis innovation has shifted away from this well established and understood technique in recent years. One major drawback for their usability is the requirement of hydrogen gas.

1.2. Electrochemical (Amperometric) Sensors

Electrochemical sensors (ECSs) measure the current of a redox reaction in which a charge is transferred from an electrode to the analyte in an electrochemical cell of a varying constitution. In most ECSs a measuring cell is compared to a reference cell. In the measuring cell the VOC analytes diffuse into the cell (and the electrolyte) through a membrane. While in principle also nonselective, an ECS can be optimized for specific analytes. This can be performed by altering the membrane material, the electrode materials, the electrolyte, or the electrical state of the cell.
Silverster described the benefits of using ionic liquids as electrolytes in ECSs. They have a wide potential range and the capability to improve the lifespan of sensors in dry conditions. They also facilitate the miniaturization of the devices [5].
Miha et al. highlighted the use of polypyrroles as a sensor material in electrochemical sensors. They describe the effects of doping, surface modification, side-chain selection and other variants and elaborate on their utilities [6].
Kumar et al. report on the progress and challenges of using metal organic frameworks in electrochemical VOC detectors [7]. They discuss strategies of doping, tagging/functionalization, and post-synthesis modification to maximize performance.

1.3. Metal Oxide (Potentiometric) Sensors

Metal oxide sensors (MOS) sensors are composed of a metal oxide thin film. They are the most common and widely used VOC sensors. The resistance and conductivity are altered by ambient gases that desorb onto its surface [8]. Depending on the metal oxide, oxidizing, or reducing compounds can be measured. The working principle is not fully understood but depends on the interaction of the VOC compounds with chemisorbed oxygen which either liberates or removes electrons from the semiconductor surface. N-type MOS (e.g., TiO2) experiences a decrease in conductivity from oxidizing and an increase from reducing VOCs. P-type sensors (e.g., NiO) work consequently the opposite way [9]. The performance of MOSs can be influenced by, among others, their composition, surface area, doping level, temperature, humidity, or morphology [10].
Consequently, a lot of research in the past years focused on the fabrication methods and nanostructured MOSs. Some nanostructured MOSs showed significantly enhanced selectivity and were able to operate with lower temperatures [9][11][12][13][14][15][16]. Acharyya et al. were able to discriminate different VOCs with a tin-oxide-based hollow sphere MOS by calculating the kinetic properties of the analytes interaction with the active surface [17]. A significant improvement of selectivity and sensitivity was achieved by Fois et al. by using metal oxides doped with rare earth elements [18]. An overall increase in performance of MOSs was reported by Pargoletti and Cappelletti by coupling them with innovative carbonaceous materials [19]. Baur et al. were able to show hidden potential in MOSs by using them in a temperature cycled operation and data-based models trained with advanced machine learning [20]. Gao et al. were able to improve the fabrication of MOSs by synthesizing hierarchically porous metal oxide nanostructures [21].
All three types of these nonselective sensors mentioned to this point have the advantage of being typically relatively cheap, small (mm to cm range) and commercially available. Their limit of detection (LOD) ranges from mid ppb to ppm levels. PIDs are the simplest devices with a relatively low LOD, high measuring range and short measuring time [22]. However, not all VOCs are detectable. Halogenated species for example cannot be ionized. In comparison, ECSs are less sensitive and have a more limited detection range but respond to a broader range of analytes. A measurement typically takes >2 min due to the slow diffusion process. They may also need some humidity to work. On the other hand, they can be better tuned to measure specific VOCs and they require very little energy to run [23]. MOS sensors are typically very small and light. Their LOD differs vastly for different compounds. Their selectivity may be influenced by dopants or filters. Since they usually function only in higher temperature, they have a higher power consumption. The adsorption and desorption of VOCs onto the surface is additionally very slow which leads to measurement times of up to 1 h. They also show a response to some inorganic gases such as CO or NOx. The sensor is additionally affected by temperature and humidity. In general, MOS sensors are not stable for a very long time [23].

1.4. Pellistors, Surface Acoustic Wave, Quartz Crystal Microbalance and Other Sensors

One more sensor that belongs to the group of nonselective VOC sensors are pellistors or thermal sensors. These measure the changing resistance of a catalytically coated ceramic while volatiles that are combustible move towards them by diffusion. The heat increase through the combustion reaction leads to an increase in electrical resistance. Due to this nature, they are used to detect explosives though they have a high LOD (high ppm range) [24]. Pellistors are among the oldest sensor technologies still in use.
Besides these more established and widely used sensors, there is a wide range of emerging sensors that may play an increasingly important role in the future. These techniques include, for example, surface acoustic wave (SAW) sensors. Within the SAW, an acoustic wave travels through an adsorbent polymer film on a piezoelectric substate [25]. When a VOC is adsorbed onto the film the mass of the film increases resulting in a small change in phase of the wave relative to a reference. For this, films with different affinities can be used [26].
Gao et al. proposed a variation of a SAW named dual transduction SAW. By detection variations in both the mass and resistance in the sensing material and exploiting the relationship between them, they were able to identify different VOCs [27]. The sensitivity of a SAW sensor could be considerably increased by Viespe et al. by embedding nanoparticles in the polymer sensing film [28]. Similar effects were seen by Kus et al. by using functionalized gold nanorods [29]. A review for sensitive materials and coating technologies was performed by Palla-Papavlu et al. [30].
Another sensor that works on a similar principle is a quartz crystal microbalance (QCM). A voltage is aplite to a quartz crystal causing it to oscillate (reverse piezoelectric effect). The crystal is coated with a film like that of a SAW. When a VOC adsorbs onto the surface, the change in mass results in a change in frequency which is recorded [31][32][33].
Due to their viscoelastic nature ionic liquids have gained much interest as a coating for QCMs in recent years [34][35].
Besides these types, there are several other detector types being researched now that can be counted towards the class of microelectromechanical systems (MEMS), subcategories of them or sensors already mentioned here. The basic mechanism for all these sensors is an active layer that interacts with the VOCs and a transduction mechanism that translates that interaction into a signal. The transduction mechanism can be optical, acoustic, calorimetric, amperometry, conductometric, potentiometric or biological [22]. Since it would be impractical to cover all of them in this work, researchers will leave it in the above description of the most common and/or promising types.

2. Electronic Noses

An electronic nose (EN) or e-nose refers to the principle of a sensor array. Several different simple and low-cost detectors (see Section 2.1) are bundled together to form the array [36]. While an individual sensor is rather nonselective, each different sensor has a slightly different selectivity towards a certain VOC compound. Through pattern recognition algorithms the information from all sensors is combined to form a fingerprint of an odor. These devices are usually used to compare odor samples (comprised of many VOCs) with a reference or with each other [37]. This may be utilized in food safety to identify off-odors, classify different foods or food from different sources [38]. Other fields of use are agriculture, forestry, military and security, medical or industrial applications. Although low-cost sensors are used to build electronic noses, the cost and size of them are considerably higher than that of just the gas sensors due to the advanced computational and software requirements. E-nose systems are among the most promising technologies for odor discrimination because they mimic our own olfactory system. The sensor array mimics our nose while the data analysis takes the equivalent role to our brain. The data analysis is performed by utilizing different machine learning algorithms such as pattern recognition, principle component analysis, linear discrimination analysis and others with the goal to form artificial neural networks [38].
ENs are most prominently used for the authentication and quality control of food [39][40][41][42][43][44][45]. Since the use of an EN requires no sample preparation and the calibration can be performed using just a reference sample this technique seems well suited for this task.
The detection of diseases in particular by analyzing exhaled breath is becoming an increasingly researched topic [46][47][48][49][50][51]. As it is often not feasible and impractical to utilize techniques such as GC-MS for breath analysis, focus on this field has shifted towards ENs. Since human breath can contain up to 3000 VOCs [46], formed mostly by metabolic processes, it is believed that an analysis can led to information on a person’s health. The recent applications for ENs in both food and disease detection are too numerous to mention individually but can be looked up in the cited literature above.
One aspect that may have prevented the widespread adoption of ENs is portability. The device needs to be small, light, low power consuming and fast in response to be applicable in the field and at the points of interest [52]. Wojnowski et al. constructed a particularly portable and modular ENs for the purpose of food analysis [39]. Hou et al. developed a handheld EN to identify liquors [53]. Huang et al. designed and validated a portable, battery-powered EN based on 10 MOSs and machine learning algorithms for the detection and classification of VOCs [54]. Matatagui et al. were able to construct a portable, low-cost, battery powered EN based on SAW sensors for the detection of BTX in air [55]. The main challenge in these advancements lays in the manufacturing of the appropriate electronic parts (acquisition and transmission module), the signal processing and the software to process the gathered data. The detection, classification, and prediction can be improved by using artificial intelligence in an EN [56].

3. Spectroscopic Methods

Optical sensors in general are more selective than simple gas sensors. While not molecule specific, they still offer better selectivity, a lower instrumental drift and faster response with the additional benefit of being non-destructive. On the other hand, they are also larger and costlier. Since the measurement is based on the direct measurement of physical properties of molecules it is reliable and self-referenced [57].

3.1. Nondispersive Infrared Sensors

Nondispersive Infrared Sensors (NDIR) measure the absorption of infrared light of components in the gas phase. The IR spectral region of 700 nm–14 µm causes molecular vibrations and rotations in analytes. In contrast to traditional IR spectrometers no diffraction grating, or prism is used to select a spectrum range [58]. Instead, an optical filter is used to select regions from a broadband source for analysis. With this semi-selective method, the decrease at wavenumber regions, which represent specific oscillation frequencies, can be observed. These typically represent chemical functionalities/bonds of the analytes. All types of compounds, including inorganic gases can be detected with these techniques. While some selectivity is given, the absorption of a specific compound may be overlaid by the absorption of another compound with the same molecular bonds [59]. While this technique is applicable for VOCs, it is most used to detect small gas molecules such as H2S, CH4, CO2, CO, NO, CH2O, NO2 or SO2.
TAN et al. reported on multiplexed NDIR gas sensing platform utilizing a narrowband infrared detector array. They achieved multi-gas sensing with nanoantenna integrated narrowband pyroelectric detectors instead of multiple pairs of bulky and expensive bandpass filters and detectors [60]. A similar system was shown by Xu et al. to be able to analyze multiple automobile exhaust gases simultaneously [61]. Esfahani et al. demonstrated that NDIRs can also be used as sensors in an electronic nose, overcoming common issues such as sensor drift, poor repeatability, lack of robustness, insufficient replicability and temperature and humidity effects [62].

3.2. UV Spectrometers

Besides NDIR, there are also several types of UV spectrometers that can analyze UV-active VOCs (e.g., aromatics). Absorption occurs when a valence electron is excited from a lower to a higher level by photons passing through the analyte. The energy of the photons absorbed matches the energy difference of the two electron levels and therefore is somewhat selective. The quantification is (in most cases) based on measuring the transmittance. Due to the simplicity of the principle this technique has been a staple in analytical chemical laboratories for decades. UV means the wavelength of the electromagnetic radiation spectrum between 180 nm and 400 nm. Miniaturization of the three main components can be achieved by utilizing LEDs as light sources, hollow core waveguides as a gas cell and photodiodes as detectors [57]. For gashouse analytes, UV spectroscopy is mostly used for the characterization of ozone, nitrogen oxides, sulfur dioxide and aromatics.
Hue et al. were able to build an absorption spectrometer capable of analyzing BTEX (benzene, toluene, ethylbenzene and xylenes) simultaneously to assess the quality of indoor air. They achieved this by combining five sensors containing nonporous disks with pore sizes tailored to entrap target analytes [63].
The emergence of deep UV emitting diodes could potentially expand the use of this technology over a wide range of fields [64].

3.3. Chemiluminescence

Chemiluminescence (CL) describes a chemical reaction that emits light. While there are several liquid-based chemiluminescence reactions, the reaction with ozone is one gas phase reaction that can be used in VOC analysis [65]. Ozone can react in a chemiluminescence reaction with NO (used in car exhaust measurements), with reduced sulfur compounds (used in the study of the atmospheric sulfur cycle) and with compounds containing double bonds [66] (used in the study of isoprene emissions from plants [67]) [68]. Since this emission has a very low background and the wavelengths for each type of reaction differ, this type of detector can achieve very low limits of detections in the single digit ppt range. A CL detector can be used as a stand-alone device or as a sulfur or nitrogen specific detector in conjunction with a GC where all compounds are reduced in a hydrogen flame before the detector.
Ohira et al. used this principle to measure isoprene, which is involved in the biosynthetic pathway to cholesterol, in human breath [69]. Mukosera et al. were able to detect dinitrosyl iron complexes, important intermediates in the metabolism of NO with ozone chemiluminescence [70]. Zhao et al. introduced a chemiluminescence method to determine chemical oxygen demand in waters as an especially environmentally friendly and rapid method [71]. Similarly, Matsumoto was able to measure the total ozone reactivity stemming from BVOCs in forest air [72]. Conversely the chemiluminescence reaction of ozone can also be used to measure ozone concentrations for example in the atmosphere when using isoprene gas as a reaction partner [73][74].

4. Miniaturized Gas Chromatographic

There has been a lot of interest in miniaturizing classic GC instruments for many years and various fields. This can be achieved to varying degrees, though the nomenclature is not consistent [75]. “Compact” GC instruments are smaller versions of lab instruments [76]. They performed about the same as their full-size equivalents, but consume less power, materials, and space. They are not as mobile though and are best suited for normal or temporary stationary laboratories. Instruments labeled as “portable”, or “field” are of even lower weights and suited for on-site analysis. Devices labeled as “µGC”, “handheld”, “pocket”, or “personal” are the smallest category that usually weights less than one kilogram [77]. These are chip-based GCs machined on silicon wafers. In general, the smaller the instrument the more limited the performance is [78].
All main components of a GC system, the injector, column, and detector, are shrunken to achieve better portability [79]. Due to low ambient concentrations, a simple preconcentration column with thermal desorption is most common as an injection unit [77][80]. Traditional columns may be replaced by etched channels on a semiconductor chip or a multiple capillary column. Possible detectors are miniature versions of classic GC detectors or may utilize one or more detectors mentioned in Section 2.1 [81]. To achieve true portability, these devices can be run on batteries and may use air as a carrier gas or use a gas cylinder. Additionally, smaller pumps and valves are utilized. While mini/µ-GCs are certainly more expensive and complex than simpler gas sensors the main advantage to utilize them is the much greater capability to be selective towards specific target analytes.
Rodríguez-Cuevas et al. were able to manufacture a novel gas preconcentrator with which they were able to analyze BTEX components at ppt level concentrations [82]. Similarly, Zamponi et al. combined micromachined GC components with an MOX detector and their own innovative preconcentration material. The preconcentration column was based on a silicon cartridge machined from a waver filled with quinoxaline-bridged cavitand. This stationary phase was able to interact with aromatic VOCs by weak CH–π interactions [83]. You et al. reported on a real time monitoring portable GC for VOCs. By using compressed air as mobile phase with a PID detector and a carbon nanotube sponge preconcentrator they were able to analyze samples at sub ppb level concentrations in <10 min [84]. Even two-dimensional GC in a portable format was achieved in recent years. Lee et al. used this fully automated technique to analyze VOCs released from paints in indoor air [85]. An example of what can be achieved in miniaturization was published by Wang et al. who build a belt-mounted GC. The system was intended for VOC monitoring in industrial workplace environments. The battery-powered device was able to characterize typical VOC mixtures in the ppb concentration level range in less than five minutes [86][87]. The capabilities of a µPID detector were greatly improved by Li et al. Results show similar performance to a benchtop GC-FID and were demonstrated on car exhausts and breath analyzers [88].

References

  1. Agbroko, S.O.; Covington, J. A novel, low-cost, portable PID sensor for the detection of volatile organic compounds. Sens. Actuators B Chem. 2018, 275, 10–15.
  2. Pang, X.; Nan, H.; Zhong, J.; Ye, D.; Shaw, M.D.; Lewis, A.C. Low-cost photoionization sensors as detectors in GC× GC systems designed for ambient VOC measurements. Sci. Total Environ. 2019, 664, 771–779.
  3. Spadi, A.; Angeloni, G.; Guerrini, L.; Corti, F.; Maioli, F.; Calamai, L.; Parenti, A.; Masella, P. A Conventional VOC-PID Sensor for a Rapid Discrimination among Aromatic Plant Varieties: Classification Models Fitted to a Rosemary Case-Study. Appl. Sci. 2022, 12, 6399.
  4. Zabiegała, B.; Przyk, E.; Przyjazny, A.; Namieśnik, J. Evaluation of indoor air quality on the basis of measurements of VOC concentrations. Chem. Anal. 2000, 45, 11–26.
  5. Silvester, D.S. New innovations in ionic liquid–based miniaturised amperometric gas sensors. Curr. Opin. Electrochem. 2019, 15, 7–17.
  6. Miah, M.R.; Yang, M.; Khandaker, S.; Bashar, M.M.; Alsukaibi, A.K.D.; Hassan, H.M.; Znad, H.; Awual, M.R. Polypyrrole-based sensors for volatile organic compounds (VOCs) sensing and capturing: A comprehensive review. Sens. Actuators A Phys. 2022, 347, 113933.
  7. Kumar, P.; Kim, K.-H.; Mehta, P.K.; Ge, L.; Lisak, G. Progress and challenges in electrochemical sensing of volatile organic compounds using metal-organic frameworks. Crit. Rev. Environ. Sci. Technol. 2019, 49, 2016–2048.
  8. Meixner, H.; Lampe, U. Metal oxide sensors. Sens. Actuators B Chem. 1996, 33, 198–202.
  9. Dey, A. Semiconductor metal oxide gas sensors: A review. Mater. Sci. Eng. B 2018, 229, 206–217.
  10. Wang, C.; Yin, L.; Zhang, L.; Xiang, D.; Gao, R. Metal oxide gas sensors: Sensitivity and influencing factors. Sensors 2010, 10, 2088–2106.
  11. Sayago, I.; Aleixandre, M.; Santos, J.P. Development of tin oxide-based nanosensors for electronic nose environmental applications. Biosensors 2019, 9, 21.
  12. Han, D.; Zhai, L.; Gu, F.; Wang, Z. Highly sensitive NO2 gas sensor of ppb-level detection based on In2O3 nanobricks at low temperature. Sens. Actuators B Chem. 2018, 262, 655–663.
  13. Zeng, H.; Zhang, G.; Nagashima, K.; Takahashi, T.; Hosomi, T.; Yanagida, T. Metal–oxide nanowire molecular sensors and their promises. Chemosensors 2021, 9, 41.
  14. John, R.A.B.; Kumar, A.R. A review on resistive-based gas sensors for the detection of volatile organic compounds using metal-oxide nanostructures. Inorg. Chem. Commun. 2021, 133, 108893.
  15. Fazio, E.; Spadaro, S.; Corsaro, C.; Neri, G.; Leonardi, S.G.; Neri, F.; Lavanya, N.; Sekar, C.; Donato, N.; Neri, G. Metal-oxide based nanomaterials: Synthesis, characterization and their applications in electrical and electrochemical sensors. Sensors 2021, 21, 2494.
  16. Amiri, V.; Roshan, H.; Mirzaei, A.; Neri, G.; Ayesh, A.I. Nanostructured metal oxide-based acetone gas sensors: A review. Sensors 2020, 20, 3096.
  17. Acharyya, S.; Nag, S.; Kimbahune, S.; Ghose, A.; Pal, A.; Guha, P.K. Selective discrimination of VOCs applying gas sensing kinetic analysis over a metal oxide-based chemiresistive gas sensor. ACS Sens. 2021, 6, 2218–2224.
  18. Fois, M.; Cox, T.; Ratcliffe, N.; de Lacy Costello, B. Rare earth doped metal oxide sensor for the multimodal detection of volatile organic compounds (VOCs). Sens. Actuators B Chem. 2021, 330, 129264.
  19. Pargoletti, E.; Cappelletti, G. Breakthroughs in the design of novel carbon-based metal oxides nanocomposites for vocs gas sensing. Nanomaterials 2020, 10, 1485.
  20. Baur, T.; Amann, J.; Schultealbert, C.; Schütze, A. Field study of metal oxide semiconductor gas sensors in temperature cycled operation for selective VOC monitoring in indoor air. Atmosphere 2021, 12, 647.
  21. Gao, Y.; Kong, Q.; Zhang, J.; Xi, G. General fabrication and enhanced VOC gas-sensing properties of hierarchically porous metal oxides. RSC Adv. 2017, 7, 35897–35904.
  22. Ollé, E.P.; Farré-Lladós, J.; Casals-Terré, J. Advancements in microfabricated gas sensors and microanalytical tools for the sensitive and selective detection of odors. Sensors 2020, 20, 5478.
  23. Spinelle, L.; Gerboles, M.; Kok, G.; Persijn, S.; Sauerwald, T. Review of portable and low-cost sensors for the ambient air monitoring of benzene and other volatile organic compounds. Sensors 2017, 17, 1520.
  24. Szulczyński, B.; Gębicki, J. Currently commercially available chemical sensors employed for detection of volatile organic compounds in outdoor and indoor air. Environments 2017, 4, 21.
  25. Ho, C.K.; Lindgren, E.R.; Rawlinson, K.S.; McGrath, L.K.; Wright, J.L. Development of a surface acoustic wave sensor for in-situ monitoring of volatile organic compounds. Sensors 2003, 3, 236–247.
  26. Horrillo, M.; Fernández, M.; Fontecha, J.; Sayago, I.; Garcıa, M.; Aleixandre, M.; Santos, J.; Arés, L.; Gutiérrez, J.; Gracia, I. Detection of volatile organic compounds using surface acoustic wave sensors with different polymer coatings. Thin Solid Film. 2004, 467, 234–238.
  27. Gao, F.; Boussaid, F.; Xuan, W.; Tsui, C.-Y.; Bermak, A. Dual transduction surface acoustic wave gas sensor for VOC discrimination. IEEE Electron Device Lett. 2018, 39, 1920–1923.
  28. Viespe, C.; Miu, D. Characteristics of surface acoustic wave sensors with nanoparticles embedded in polymer sensitive layers for VOC detection. Sensors 2018, 18, 2401.
  29. Kus, F.; Altinkok, C.; Zayim, E.; Erdemir, S.; Tasaltin, C.; Gurol, I. Surface acoustic wave (SAW) sensor for volatile organic compounds (VOCs) detection with calix arene functionalized Gold nanorods (AuNRs) and silver nanocubes (AgNCs). Sens. Actuators B Chem. 2021, 330, 129402.
  30. Palla-Papavlu, A.; Voicu, S.I.; Dinescu, M. Sensitive materials and coating technologies for surface acoustic wave sensors. Chemosensors 2021, 9, 105.
  31. Speight, R.E.; Cooper, M.A. A survey of the 2010 quartz crystal microbalance literature. J. Mol. Recognit. 2012, 25, 451–473.
  32. O’sullivan, C.; Guilbault, G. Commercial quartz crystal microbalances–theory and applications. Biosens. Bioelectron. 1999, 14, 663–670.
  33. Liu, K.; Zhang, C. Volatile organic compounds gas sensor based on quartz crystal microbalance for fruit freshness detection: A review. Food Chem. 2021, 334, 127615.
  34. Vaughan, S.R.; Pérez, R.L.; Chhotaray, P.; Warner, I.M. Quartz crystal microbalance based sensor arrays for detection and discrimination of VOCs using phosphonium ionic liquid composites. Sensors 2020, 20, 615.
  35. Regmi, B.P.; Adhikari, P.L.; Dangi, B.B. Ionic liquid-based quartz crystal microbalance sensors for organic vapors: A tutorial review. Chemosensors 2021, 9, 194.
  36. Wilson, A.D.; Baietto, M. Applications and advances in electronic-nose technologies. Sensors 2009, 9, 5099–5148.
  37. Röck, F.; Barsan, N.; Weimar, U. Electronic nose: Current status and future trends. Chem. Rev. 2008, 108, 705–725.
  38. Karakaya, D.; Ulucan, O.; Turkan, M. Electronic nose and its applications: A survey. Int. J. Autom. Comput. 2020, 17, 179–209.
  39. Wojnowski, W.; Majchrzak, T.; Dymerski, T.; Gębicki, J.; Namieśnik, J. Portable electronic nose based on electrochemical sensors for food quality assessment. Sensors 2017, 17, 2715.
  40. Gliszczyńska-Świgło, A.; Chmielewski, J. Electronic nose as a tool for monitoring the authenticity of food. A review. Food Anal. Methods 2017, 10, 1800–1816.
  41. Sanaeifar, A.; ZakiDizaji, H.; Jafari, A.; de la Guardia, M. Early detection of contamination and defect in foodstuffs by electronic nose: A review. TrAC Trends Anal. Chem. 2017, 97, 257–271.
  42. Zhu, D.; Ren, X.; Wei, L.; Cao, X.; Ge, Y.; Liu, H.; Li, J. Collaborative analysis on difference of apple fruits flavour using electronic nose and electronic tongue. Sci. Hortic. 2020, 260, 108879.
  43. Okur, S.; Sarheed, M.; Huber, R.; Zhang, Z.; Heinke, L.; Kanbar, A.; Wöll, C.; Nick, P.; Lemmer, U. Identification of mint scents using a QCM based e-nose. Chemosensors 2021, 9, 31.
  44. Majchrzak, T.; Wojnowski, W.; Dymerski, T.; Gębicki, J.; Namieśnik, J. Electronic noses in classification and quality control of edible oils: A review. Food Chem. 2018, 246, 192–201.
  45. Viejo, C.G.; Fuentes, S.; Godbole, A.; Widdicombe, B.; Unnithan, R.R. Development of a low-cost e-nose to assess aroma profiles: An artificial intelligence application to assess beer quality. Sens. Actuators B Chem. 2020, 308, 127688.
  46. Dragonieri, S.; Pennazza, G.; Carratu, P.; Resta, O. Electronic nose technology in respiratory diseases. Lung 2017, 195, 157–165.
  47. Van de Goor, R.; van Hooren, M.; Dingemans, A.-M.; Kremer, B.; Kross, K. Training and validating a portable electronic nose for lung cancer screening. J. Thorac. Oncol. 2018, 13, 676–681.
  48. Tirzïte, M.; Bukovskis, M.; Strazda, G.; Jurka, N.; Taivans, I. Detection of lung cancer with electronic nose and logistic regression analysis. J. Breath Res. 2018, 13, 016006.
  49. Behera, B.; Joshi, R.; Vishnu, G.A.; Bhalerao, S.; Pandya, H.J. Electronic nose: A non-invasive technology for breath analysis of diabetes and lung cancer patients. J. Breath Res. 2019, 13, 024001.
  50. Sarno, R.; Sabilla, S.I.; Wijaya, D.R. Electronic Nose for Detecting Multilevel Diabetes using Optimized Deep Neural Network. Eng. Lett. 2020, 28, 31–42.
  51. Riscica, F.; Dirani, E.; Accardo, A.; Chapoval, A. An Inexpensive, Portable, and Versatile Electronic Nose for Illness Detect. Известия Алтайскoгo гoсударственнoгo университета 2021, 1, 47–52.
  52. Cheng, L.; Meng, Q.-H.; Lilienthal, A.J.; Qi, P.-F. Development of compact electronic noses: A review. Meas. Sci. Technol. 2021, 32, 062002.
  53. Hou, H.-R.; Meng, Q.-H.; Qi, P.-F.; Jing, T. A hand-held electronic nose system for rapid identification of Chinese liquors. IEEE Trans. Instrum. Meas. 2021, 70, 2006411.
  54. Huang, Y.; Doh, I.-J.; Bae, E. Design and validation of a portable machine learning-based electronic nose. Sensors 2021, 21, 3923.
  55. Matatagui, D.; Bahos, F.A.; Gràcia, I.; Horrillo, M.D.C. Portable low-cost electronic nose based on surface acoustic wave sensors for the detection of BTX vapors in air. Sensors 2019, 19, 5406.
  56. Illahi, A.A.C.; Dadios, E.P.; Bandala, A.A.; Vicerra, R.R.P. Electronic Nose Technology and Application: A Review. In Proceedings of the 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines, 28–30 November 2021; pp. 1–5.
  57. Khan, S.; Newport, D.; Le Calvé, S. Gas detection using portable deep-UV absorption spectrophotometry: A review. Sensors 2019, 19, 5210.
  58. Jha, R.K. Non-Dispersive infrared gas sensing technology: A review. IEEE Sens. J. 2021, 22, 6–15.
  59. Dinh, T.-V.; Choi, I.-Y.; Son, Y.-S.; Kim, J.-C. A review on non-dispersive infrared gas sensors: Improvement of sensor detection limit and interference correction. Sens. Actuators B Chem. 2016, 231, 529–538.
  60. Tan, X.; Zhang, H.; Li, J.; Wan, H.; Guo, Q.; Zhu, H.; Liu, H.; Yi, F. Non-dispersive infrared multi-gas sensing via nanoantenna integrated narrowband detectors. Nat. Commun. 2020, 11, 5245.
  61. Xu, M.; Peng, B.; Zhu, X.; Guo, Y. Multi-Gas Detection System Based on Non-Dispersive Infrared (NDIR) Spectral Technology. Sensors 2022, 22, 836.
  62. Esfahani, S.; Tiele, A.; Agbroko, S.O.; Covington, J.A. Development of a tuneable NDIR optical electronic nose. Sensors 2020, 20, 6875.
  63. Hue, J.; Dupoy, M.; Bordy, T.; Rousier, R.; Vignoud, S.; Schaerer, B.; Tran-Thi, T.-H.; Rivron, C.; Mugherli, L.; Karpe, P. Benzene and xylene detection by absorbance in the range of 10–100 ppb application: Quality of indoor air. Sens. Actuators B Chem. 2013, 189, 194–198.
  64. Kneissl, M.; Seong, T.-Y.; Han, J.; Amano, H. The emergence and prospects of deep-ultraviolet light-emitting diode technologies. Nat. Photonics 2019, 13, 233–244.
  65. Toby, S. Chemiluminescence in the reactions of ozone. Chem. Rev. 1984, 84, 277–285.
  66. Finlayson, B.; Pitts, J., Jr.; Atkinson, R. Low-pressure gas-phase ozone-olefin reactions. Chemiluminescence, kinetics, and mechanisms. J. Am. Chem. Soc. 1974, 96, 5356–5367.
  67. Hills, A.J.; Zimmerman, P.R. Isoprene measurement by ozone-induced chemiluminescence. Anal. Chem. 1990, 62, 1055–1060.
  68. Jiménez, A.; Navas, M.; Galán, G. Air analysis: Determination of ozone by chemiluminescence. Appl. Spectrosc. Rev. 1997, 32, 141–149.
  69. Ohira, S.-I.; Li, J.; Lonneman, W.A.; Dasgupta, P.K.; Toda, K. Can breath isoprene be measured by ozone chemiluminescence? Anal. Chem. 2007, 79, 2641–2649.
  70. Mukosera, G.T.; Liu, T.; Ahmed, A.S.I.; Li, Q.; Sheng, M.H.-C.; Tipple, T.E.; Baylink, D.J.; Power, G.G.; Blood, A.B. Detection of dinitrosyl iron complexes by ozone-based chemiluminescence. Nitric Oxide 2018, 79, 57–67.
  71. Zhao, Y.; Zhou, J.; Song, Y.; Li, Z.; Liu, X.; Zhang, M.; Zang, H.; Cao, X.; Lv, C. An environment-friendly device for rapid determination of chemical oxygen demand in waters based on ozone-induced chemiluminescence technology. Anal. Methods 2019, 11, 1707–1714.
  72. Matsumoto, J. Measurements of total ozone reactivity in a suburban forest in Japan. Atmos. Environ. 2021, 246, 117990.
  73. Takayanagi, T. Monitoring of Ambient Ozone: Instrumentations and Applications Mini-Review. J. Flow Inject. Anal. 2017, 34, 67–70.
  74. Long, R.W.; Whitehill, A.; Habel, A.; Urbanski, S.; Halliday, H.; Colón, M.; Kaushik, S.; Landis, M.S. Comparison of ozone measurement methods in biomass burning smoke: An evaluation under field and laboratory conditions. Atmos. Meas. Tech. 2021, 14, 1783–1800.
  75. Witkiewicz, Z.; Wardencki, W. Transportable, portable and micro gas chromatographs. Anal. Chem. Indian J. 2019, 19, 142.
  76. Meuzelaar, H.L.; Dworzanski, J.P.; Arnold, N.S.; McClennen, W.H.; Wager, D.J. Advances in field-portable mobile GC/MS instrumentation. Field Anal. Chem. Technol. 2000, 4, 3–13.
  77. Regmi, B.P.; Agah, M. Micro gas chromatography: An overview of critical components and their integration. Anal. Chem. 2018, 90, 13133–13150.
  78. Yashin, Y.I.; Yashin, A.Y. Miniaturization of gas-chromatographic instruments. J. Anal. Chem. 2001, 56, 794–805.
  79. Yang, Z.; Ren, Z.; Cheng, Y.; Sun, W.; Xi, Z.; Jia, W.; Li, G.; Wang, Y.; Guo, M.; Li, D. Review and prospect on portable mass spectrometer for recent applications. Vacuum 2022, 199, 110889.
  80. Lara-Ibeas, I.; Cuevas, A.R.; Le Calvé, S. Recent developments and trends in miniaturized gas preconcentrators for portable gas chromatography systems: A review. Sens. Actuators B Chem. 2021, 346, 130449.
  81. Qu, H.; Duan, X. Recent advances in micro detectors for micro gas chromatography. Sci. China Mater. 2019, 62, 611–623.
  82. Rodríguez-Cuevas, A.; Lara-Ibeas, I.; Leprince, A.; Wolf, M.; Le Calvé, S. Easy-to-manufacture micro gas preconcentrator integrated in a portable GC for enhanced trace detection of BTEX. Sens. Actuators B Chem. 2020, 324, 128690.
  83. Zampolli, S.; Elmi, I.; Mancarella, F.; Betti, P.; Dalcanale, E.; Cardinali, G.; Severi, M. Real-time monitoring of sub-ppb concentrations of aromatic volatiles with a MEMS-enabled miniaturized gas-chromatograph. Sens. Actuators B Chem. 2009, 141, 322–328.
  84. You, D.-W.; Seon, Y.-S.; Jang, Y.; Bang, J.; Oh, J.-S.; Jung, K.-W. A portable gas chromatograph for real-time monitoring of aromatic volatile organic compounds in air samples. J. Chromatogr. A 2020, 1625, 461267.
  85. Lee, J.; Sayler, S.K.; Zhou, M.; Zhu, H.; Richardson, R.J.; Neitzel, R.L.; Kurabayashi, K.; Fan, X. On-site monitoring of occupational exposure to volatile organic compounds by a portable comprehensive 2-dimensional gas chromatography device. Anal. Methods 2018, 10, 237–244.
  86. Wang, J.; Nuñovero, N.; Nidetz, R.; Peterson, S.J.; Brookover, B.M.; Steinecker, W.H.; Zellers, E.T. Belt-mounted micro-gas-chromatograph prototype for determining personal exposures to volatile-organic-compound mixture components. Anal. Chem. 2019, 91, 4747–4754.
  87. Wang, J.; Bryant-Genevier, J.; Nuñovero, N.; Zhang, C.; Kraay, B.; Zhan, C.; Scholten, K.; Nidetz, R.; Buggaveeti, S.; Zellers, E.T. Compact prototype microfabricated gas chromatographic analyzer for autonomous determinations of VOC mixtures at typical workplace concentrations. Microsyst. Nanoeng. 2018, 4, 17101.
  88. Wei-Hao Li, M.; Ghosh, A.; Venkatasubramanian, A.; Sharma, R.; Huang, X.; Fan, X. High-Sensitivity Micro-Gas Chromatograph–Photoionization Detector for Trace Vapor Detection. ACS Sens. 2021, 6, 2348–2355.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback