Breath Gas Sensors for Diabetes and Lung Cancer Diagnosis

1. INTRODUCTION

Currently, digital healthcare technologies based on Internet of Things (IOT) are emerging, and the market for digital healthcare was estimated to be 151.5 billion dollars by 2020 [1]. Among the digital healthcare technologies, disease diagnosis is attracting significant attention. Traditional diagnostic methods, such as radiology and human biological material analysis, have limitations of long study time, high cost, radiation exposure, pain, and bleeding. Therefore, non-invasive diagnostics methods, for early diagnosis of specific diseases and personal healthcare management, have attracted attention.

Breath sensors are one of the promising candidates for the realization of non-invasive diagnosis owing to their ease of use, safety, and cost effectiveness. In the last year, World Economic Forum reported breath sensors for disease diagnostics as one of top 10 emerging technologies of 2021 [2].

Exhaled breath (EB) contains various compounds including volatile organic compounds (VOCs). The composition and concentration of VOCs in the EB vary based on malfunctioning of organs, and these changes can provide information of specific diseases in early stages [3, 4]. Therefore, many researchers have focused on detecting VOCs in the exhaled human breath. For example, nitric acid, acetone, and ammonia are regarded as biomarkers of asthma, diabetes, and kidney diseases, respectively [5].

Diagnostics devices are required to be highly accurate and reliable. Detection from EB have been approached using two sensing strategies to satisfy these requirements [6]. Fig. 1 shows schematic of the sensing strategies [7]. One is development of highly selective, sensitive gas sensor, which interacts and detects a single specific compound in exhaled gas. Another is development of artificially intelligent nanoarrays. This approach uses an array of cross-reactive sensors with pattern recognition methods. In this review, we summarize recent advancements in breath gas sensors for diagnosis of diseases, especially focusing on diabetes and lung cancer. The review is divided to two main parts including single nanosensor and nanoarrays. At last, future scope and challenges of the breath sensors are discussed.

Fig. 1.

Schematic of the working principal of breath gas sensors for disease diagnosis. (a) Selective nanoscale sensors. (b) Artificially intelligent nanoarrays. Reprinted with permission from Ref [7]. Copyrights (2015) Wiley-VCH GmbH.

2. BIOMARKERS AND VOCs

EB contains more than 3,500 different VOCs [8], which are compounds or byproducts released by metabolic processes in the body. The VOCs in human breath can be divided into following main groups: hydrocarbons (produced mainly by peroxidation of polyunsaturated fatty acids and lipids), alcohols (adsorbed through the gastrointestinal tract into the blood and metabolized by enzymes), aldehydes (arise from metabolized alcohols, reduction of hydroperoxide, tobacco smoke, detoxification processes of tobacco by-products, and dietary sources), ketones (produced by the liver from fatty acids and influenced by diet and metabolisms), and aromatic and nitrile VOCs (adsorbed by exogeneous sources like cigarette, smoke, and pollution and stored in the fatty tissues) [7].

The changes in acetone concentration in EB have been considered as a key biomarker of diabetes for few decades [9-14]. Diabetic patients have a high blood glucose level due to an insulin disorder. Healthy people use glucose as an energy source, however diabetic patients metabolize fatty acid [15]. The acetoacetate is produced by lipid peroxidation and transformed into acetone in EB by non-enzymatic decarboxylation. The concentration of acetone in EB varies from 300 to 900 ppb in healthy people [16] and exceeds 1800 ppb in diabetic patients [17].

On the other side, lung cancer does not have a specific biomarker. For example, Ligor et al. selected potential biomarker candidates using gas chromatography and application of an artificial neural network model [18]. Eight compounds including butane, 2-methyl-butane, 4-methyle-octane, propane, 2-pentanone, propanal, 2,4-dimethyl heptane, and propene were selected for lung cancer biomarkers. Saalberg et al. reviewed recent studies of VOCs related to lung cancer and concluded that 77 different VOCs are potential lung cancer biomarkers. [19]. Among them, 33 VOCs were repeatably mentioned in several studies, as summarized in Table. 1. In addition, some VOCs are still not identified as directly related to lung cancer. Most of studies have compared clinical data of lung cancer patients and healthy individuals. The composite VOC patterns, called breathprint, can be analyzed using sensor arrays [20, 31-34].

Table. 1.

Biomarkers for lung cancer diagnosis. Reprinted with permission from Ref [19]. Copyrights (2016) Elsevier.

3. GAS SENSORS FOR BREATH ANALYSIS

3.1 Nanosesnors for diabetes

For accurate diagnosis of diseases, gas sensors with new nanomaterials have been studied. Since the difference in the concentration of the biomarkers in EB is minute, the gas sensors should be highly sensitive.

Recently, semiconductor metal oxide (SMO) gas sensors with new sensing materials have been researched for detecting sub-ppm level of acetone for diabetes diagnosis. Fig. 2(a) illustrates a synthetic scheme of ZnO-CuO core-hollow cube nanostructures for acetone detection [9]. CuO hollow cubes were synthesized by thermal oxidation of CuO₂ cubes and then directly attached to ZnO spherical aggregates. They formed core-shell-like structures and p-n heterojunctions, and hence showed remarkable response (R_gas/R_air, where R_gas is the resistance in target gas, and R_air is the resistance in atmospheric condition) of 11.14 at ppm acetone, as shown in Fig. 2(b). Fig. 2(c) shows responses of ZnO-CuO nanostructures and previously reported sensing materials; the ZnO-CuO core-hollow cube nanostructures exhibit much higher response to acetone. The limit of detection (LOD) of the sensor is estimated as 9 ppb. Hanh et al. fabricated Pt-Zn₂SnO₄ hollow octahedra sensors with hydrothermal reaction and detected acetone at ppb level with high selectivity and repeatability [10]. Ma et al. fabricated a highly sensitive gas sensor using phase transition of h-CoO nanoparticles to CO₃O₄ nanoplates [11]. Thermal oxidation of h-CoO led to deposition of Co₃O₄, and phase-transitioned Co₃O₄ showed high sensitivity and selectivity to acetone. Li et al. synthesized SnO₂ nanosheets using the hydro-solvothermal treatment and decorated them with PdAu bimetallic nanoparticles [12]. The SnO₂-PdAu nanosheet sensors showed high responses to acetone and formaldehyde and their LODs were 45 and 30 ppb, respectively. Fig. 2(d) shows Co₃O₄ nanofibers and Cds nanospheres composite fabricated using electrospinning with a hydro-thermal method [13]. Fig. 2(e) shows dynamic response of CdS/Co₃O₄ sensor to acetone gas under green light illumination. The green light generates electron-hole pairs on the surface of the sensor film, and the photogenerated holes promote reaction between acetone and nanocomposites. Fig. 2(f) shows responses to diabetes breath, healthy breath, and 2 ppm acetone and verifies that the sensor can diagnose diabetic patients. Fig. 2(g) illustrates schematics of the fabrication of Pt@In₂O₃ core-shell nanowires [14]. As shown in Fig. 2(h), the fabricated Pt@In₂O₃ core-shell nanowire sensor exhibited high sensitivity to low concentration of acetone with the detection limit of 10 ppb. Fig. 2(i) shows portable sensing device with Pt@In₂O₃ core-shell nanowire (NW) sensor, which successfully distinguished between healthy and diabetic volunteers in real-time.

Fig. 2.

Nanosensors for diabetes diagnosis. (a) Schematic of synthesis of ZnO-CuO core-hollow cube nanostructures. (b) Dynamic responses of ZnO-CuO sensors to acetone. (c) Response comparison to previously reported sensors. Reprinted with permission from Ref [9]. Copyrights (2020) American Chemical Society. (d) SEM image of CdS/Co3O4 nanocomposite. (e) Dynamic responses of the CdS/Co3O4 gas sensor to acetone in the presence or absence of green light. (f) Response results of the sensor tested to simulated diabetes breath, acetone, and healthy breath Reprinted with permission from Ref [13]. Copyrights (2022) Elsevier. (g) Schematic of the electrospinning fabrication of Pt@In2O3 core-shell nanowires. (h) Dynamic responses of Pt@In2O3 core-shell NW to acetone gas. (i) Schematic of the portable sensing device and response to the exhaled breath from healthy volunteers. Reprinted with permission from Ref [14]. Copyrights (2018) Springer Nature.

3.2 Nanosensors and sensor systems for lung cancer

Selective gas sensors, for detection of specific VOCs related to lung cancer, have also been investigated. Unlike diabetes diagnosis, gas sensors targeting various biomarker candidates have been studied.

Nam et al. improved gas sensors for the toluene detection using highly porous WO₃ nanofibers (NFs) by combining Pd catalysts [21]. Fig. 3(a) illustrates fabrication process of the Pd@ZnO-WO₃ heterogeneous nanofibers for detection of toluene [22]. Electrospun Pd@ZIF-8 was oxidized to obtain ZnO and the Pd@ZnO particles were embedded onto WO₃ NFs during calcination process. Fig. 3(b) shows the sensor responses to the toluene exposure. The Pd@ZnO-WO₃ NFs exhibited higher responses than other sensing materials. The response time was much shorter (< 20 s) and the LOD was estimated as 100 ppb. Selectivity of the Pd@ZnO-WO₃ was investigated with interfering analyte gases, which showed higher sensitivity to toluene rather than other gases. The Pd-embedded/WO₃ NFs were fabricated by electrospinning and calcination. The Pd functionalized WO₃ nanofibers-based gas sensor exhibited 5.5 response to 1 ppm toluene gas and remarkable selectivity against H₂S gas. Lee et al. synthesized HKUST-1-based CuO decorated with Pt nanoparticles (NPs) using sonochemical method [23]. The CuO/Pt NPs gas sensors showed high response over 82% to formaldehyde in the concentration range of 100–1500 ppb. Majidi and Nadafan analyzed the possibility of detecting biomarkers with γ-graphyne and twin-graphene sheets [24]. Based on density functional theory, they analyzed the effect of adsorption of benzene, styrene, aniline, and o-toluidine. γ-graphyne and twin-graphene sheets can be used as aniline and o-toluidine by detecting the changes in the electric dipole moment and energy band gap.

Fig. 3.

Nanosensors for lung cancer diagnosis. (a) Schematic of fabrication for the Pd@ZnO-WO3 nanofibers. (b) Response results of pristine WO3, ZnO-WO3, Pd(polyol)-WO3, Pd(polyol)-ZnO-WO3, and Pd@ZnO-WO3 NFs for toluene detection. Reprinted with permission from Ref [22]. Copyrights (2016) American Chemical Society. (c) Images of the micro-analytical units with a single SnO2 gas sensor for detection of VOCs biomarkers. (d) Normalized conductance results of biomarkers in different mixtures. Reprinted with permission from Ref [25]. Copyrights (2018) Elsevier.

Not only high-performance gas sensors, but also detection devices have been researched [25]. Fig. 3(c) shows the micro-analytical units for the detection of volatile biomarkers. The detecting device includes micro-preconcentrator with a zeolite adsorbent, GC micro-column, and SnO₂ gas sensor. Target gas passes through micro-preconcentrator and GC micro-column, the gas sensor can selectively detect biomarkers in a short time. Fig. 3(d) shows electrical responses to various biomarkers, and the LODs were estimated to be 24±3, 5±1, 21±3, and 2515±5 ppb for toluene, o-xylene, propanol, and cyclohexane, respectively.

3.3 Electronic nose systems for diabetes

Diagnosing diabetes and lung cancer with a single sensor is difficult because of a variety of VOCs related to the diseases and interference of disruptive factors, like temperature and humidity, with sensor signals. Therefore, the VOC patterns in EB have been studied using gas sensor arrays, called as an electronic nose (E-nose). The E-nose system includes gas sensors with different sensing materials and generates response patterns. Various gas sensors have been utilized for the E-nose systems.

Lee et al. fabricated MEMS microheater integrated gas sensor arrays with four different sensing materials based on CuO [26]. The gas sensor arrays detected acetone with high sensitivity and distinguished it from other VOCs using principal component analysis (PCA). Orbe et al. fabricated breath analyzer for monitoring metabolic fat burning as shown in Fig. 4(a) [27]. The acetone in EB and beta-hydroxybutyric acid (BOHB) are correlated biomarkers of the metabolic fat burning by lipolysis in the liver. Fig. 4(b) and (c) show schematic of the breath intake operation. The EB automatically flows to metal oxide gas sensor arrays, and responses to breath samples are collected. Recurrent neural network (RNN) algorithm was used to estimate the values of BOHB using collected response data. Fig. 4(d) shows feature coefficients of the variables provided by the sensors and the The result shows feature impact and feature impact of each variables indicating contributions of the trained model. Fig. 4(e) shows the predicted and real values of BOHB. The result shows some scattering due to small sample size, but verifies correlation between estimated and actual values. Fig. 4(f) shows an E-nose system using commercial chemical gas sensors for non-invasive diagnosis of diseases including diabetes [28]. Saidi et al collected breath samples from 44 volunteers and analyzed them with machine learning algorithms. Fig. 4(g) shows the schematic of 22 VOCs of breath samples, including subjects with chronic kidney disease (CKD) and diabetes mellitus (DM), healthy subjects with high creatinine (HSHS), and healthy subjects with low creatinine (HSLS). The dataset was distinguished by PCA. Fig. 4(h) shows PCA plot of breath VOC samples collected from the E-nose system and other samples for model verification. The results show that E-nose system can identify diseases without overlap using the PCA model.

Fig. 4.

E-nose systems for diabetes diagnosis. (a) Schematic of the breath analyzer, (b) operation process, and (c) strategy using RNN for BOHB estimation. (d) Feature coefficients of each of variables contributing to regression output. (e) Comparison between predicted BOHB values of the model and truth values. Reprinted with permission from Ref [27]. Copyrights (2022) Elsevier. (f) E-nose measurement setup for diagnosis of CKD, DM, and HS. (g) VOC patterns detected in exhaled breath of CKD, DM, HSHC, and HSLC. (h) Three-dimensional PCA plot of PCA model and other measurement set. Reprinted with permission from Ref [28]. Copyrights (2018) Elsevier.

Bahos et al. fabricated zeolitic imidazolate frameworks (ZIFs) nanocrystal-based surface acoustic wave (SAW) E-nose system [29], which includes four SAW sensors; ZIF-8, ZIF-67, ZIF-8/AuNP, and ZIF-67/AuNP. SAW/ZIF E-nose exhibited high sensitivity, selectivity, and reproducibility to acetone, ethanol, and ammonia.

Gupta et al. presented a simulation study of breath analysis based on MEMS cantilever sensor array [30]. The MEMS cantilever sensors are bent by vapor sorption in the volatile-selective polymer and detect a change in the cantilever resonance frequency. By adopting data mining methods, they successfully detected acetone, isoprene, and ethane with various compounds in virtual breaths.

3.4 Electronic nose systems for lung cancer

Chen et al. fabricated a flexible E-nose by synthesizing graphene oxide (GO) using different types of metal ions (M^x+) with different ratio [31]. Fig. 5(a) shows SEM images of reduced graphene oxide-metal (rGO-M) hybrid sensing materials. The rGO-M formed rough, undulating, and porous films, which enhanced sensing performances. The E-nose system including eight rGO-M sensors was tested with exhaled breath collected from 108 volunteers. Fig. 5(b) shows Langmuir-Hill model of the responses toward acetone, isoprene, ammonia, and hydrothion at sub-ppm levels. Using PCA, the rGO-M arrays successfully discriminated four biomarkers as shown in Fig. 5(c). Fig. 5(d) shows result of clinical EB analysis by linear discrimination analysis (LDA). The control group was accurately distinguished from lung cancer patients. Li et al. designed E-nose system using four types of 14 gas sensors for lung cancer diagnosis [32]. They collected gas pattern data using 52 breath samples in various diseased and healthy volunteers. The E-nose system performed classification using LDA with fuzzy 5-NN classifiers. The sensitivity, specificity, and accuracy of discrimination were 91.58, 91.72, and 91.59%, respectively. Kononov et al. fabricated E-nose system consisting of six metal oxide gas sensors [33]. They operated the E-nose system with three different temperature regimes, which can generate different responses patterns. In this study, 118 breath samples from volunteers were used and analyzed using various models, such as k-nearest neighbors (kNN), logistic regression, random forest, LDA, and support vector machine (SVM). The sensitivity, specificity, and accuracy of the classification were 95.0, 100, and 97.2, respectively.

Fig. 5.

E-nose systems for lung cancer diagnosis. (a) SEM images of GO using different types of metal ions with different ratio of GO to Mx+. (b) Langmuir-Hill fitting curves of rGO-M array for acetone, isoprene, NH3, and H2S, respectively. (c) PCA plots of rGO-M array of normalized responses to target gases. (d) LDA result and dynamic responses of EB samples. Reprinted with permission from Ref [31]. Copyrights (2020) American Chemical Society.

Zhong et al constructed a colorimetric sensor array using diverse chemo-responsive dyes [34]. Thirty-six colorimetric sensors generated 108 dimensional vectors with RGB color difference values, which represented responses to different VOCs. The arrays successfully discriminated between 20 VOCs with an accuracy of at least 90%.

Jang et al showed the E-nose that could be a platform for early screening and prognosis monitoring of lung cancer patients through performing the clinical test, according to a procedure delicately prepared by thoracic surgeons [35]. The E-nose system successfully distinguished lung cancer patients from healthy people with 75.0% accuracy by using a multi-layer perceptron (MLP).

4. CONCLUSION

We reviewed single gas sensor and E-nose system for diagnosis of diabetes and lung cancer. High sensitivity and selectivity are essential for detecting minute changes in the concentration of biomarkers in EB containing abundant VOCs. Researchers have made improvements in the nanomaterial-based single sensors and various types of E-nose systems. However, some challenges need to be overcome for their utilization in clinical fields. Most of sensor systems are in early stages of development, and the experiments were performed in controlled environment. Especially, the difficulties in collecting clinical data from patients, numerous bodily factors, intervention of humidity, and miniaturization of systems are the biggest obstacles in further advancements. E-nose systems are sufficiently capable of detecting diseases, but their collaborations with electronics, data processing, and machine learning technologies are necessary for realization of the non-invasive diagnosis systems.

Acknowledgments

This work was supported by the National Research Foundation of Korea under research projects number NRF-2017M3A9F1033056 and NRF-2021M3H4A4079271.

REFERENCES

• Global Industry Analysts, Inc., Digital Health – Global Market Trajectory & Analytics, Research and Markets, Dublin, 2022.
• World Economic Forum, Top 10 Emerging Technologies of 2021, World Economic Forum, Geneva, 2021.
• A. Vasilescu, B. Hrioczenko, G. M. Swain, and S. F. Peteu, “Exhaled breath biomarker sensing”, Biosens. Bioelectron., Vol. 182, p. 113193, 2021. [https://doi.org/10.1016/j.bios.2021.113193]
• D. Marzorati, L. Mainardi, G. Sedda, R. Gasparri, L. Spaggiari, and P. Cerveri, “A review of exhaled breath: a key role in lung cancer diagnosis”, J. Breath Res., Vol. 13, No. 3, p. 034001, 2019. [https://doi.org/10.1088/1752-7163/ab0684]
• M. Y. Chuang, C. C. Chen, H. W. Zan, H. F. Meng, and C. J. Lu, “Organic Gas Sensor with an Improved Lifetime for Detecting Breath Ammonia in Hemodialysis Patients”, ACS Sens., Vol. 2, No. 12, pp. 1788-1795, 2017. [https://doi.org/10.1021/acssensors.7b00564]
• D. Hashoul and H. Haick, “Sensors for detecting pulmonary diseases from exhaled breath”, Eur. Respir. Rev., Vol. 28, No. 152, pp. 190011(1)-190011(13), 2019. [https://doi.org/10.1183/16000617.0011-2019]
• R. Vishinkin and H. Haick, “Nanoscale Sensor Technologies, for Disease Detection via Volatolomics”, Small, Vol. 11, No. 46, pp. 6142-6164, 2015. [https://doi.org/10.1002/smll.201501904]
• Z. Wang and C. Wang, “Is breath acetone a biomarker of diabetes? A historical review on breath acetone measurements”, J. Breath Res., Vol. 7, No. 3, p. 037109, 2013. [https://doi.org/10.1088/1752-7155/7/3/037109]
• J. Lee, C. Lim, H. Park, H. Song, S. Choi, and D. Lee, “ZnO-CuO Core Hollow Cube Nanostructures for Highly Sensitive Acetone Gas Sensors at the ppb Level”, ACS Appl. Mater. Interfaces, Vol. 12, No. 31, pp. 35688-35697, 2020. [https://doi.org/10.1021/acsami.0c08593]
• N. H. Hanh, L. V. Duy, C. M, Hung, C. T, Xuan, N. V. Duy, and N. D. Hoa, “High-performance acetone gas sensor based on Pt-Zn₂SnO₄ hollow octahedra for diabetic diagnosis”, J. Alloys. Compd., Vol. 886, p. 161284, 2021. [https://doi.org/10.1016/j.jallcom.2021.161284]
• A. Ma, H. Park, J. Seo, K. Jang, H. Lee, D. Kim, J. Lee, K. Nam, and D. Lee, “Phase transition of non-equilibrium wurtzite CoO: Spontaneous deposition of sensing material for ultrasensitive detection of acetone”, Sens. Actuators. B. Chem., Vol. 308, p. 127698, 2020. [https://doi.org/10.1016/j.snb.2020.127698]
• G. Li, Z. Cheng, Q. Xiang, L. Yan, X. Wang, and J. Xu, “Bimetal PdAu decorated SnO₂ nanosheets based gas sensor with temperature-dependent dual selectivity for detecting formaldehyde and acetone”, Sens. Actuators. B. Chem., Vol. 283, pp. 590-601, 2019. [https://doi.org/10.1016/j.snb.2018.09.117]
• J. Guo, D. Zhang, T. Li, J. Zhang, and L. Yu, “Green light-driven acetone gas sensor based on electrospinned CdS nanospheres/Co₃O₄ nanofibers hybrid for the detection of exhaled diabetes biomarker”, J. Colloid. Interface. Sci., Vol. 606, pp. 261-271, 2022. [https://doi.org/10.1016/j.jcis.2021.08.022]
• W. Lei, L. Xu, K. Sheng, X. Zhou, B. Dong, G. Lu, and H. Song, “A highly sensitive and moisture-resistant gas sensor for diabetes diagnosis with Pt@In₂O₃ nanowires and a molecular sieve for protection”, NPG Asia. Mater., Vol. 10, No. 4, pp. 293-308, 2018. [https://doi.org/10.1038/s41427-018-0029-2]
• B. Behera, R. Joshi, G. K. A. Vishnu, S. Bhalerao, and H. J. Pandya, “Electronic nose: a non-invasive technology for breath breath analysis of diabetes and lung cancer patients”, J. Breath Res., Vol. 13, No. 2, p. 024001, 2019. [https://doi.org/10.1088/1752-7163/aafc77]
• A. M. Diskin, P. Spanel, and D. Smith, “Time variation of ammonia, acetone, isoprene and ethanol in breath: a quantitative SIFT-MS study over 30 days”, Physiol. Meas., Vol. 24, No. 1, pp. 107-119, 2003. [https://doi.org/10.1088/0967-3334/24/1/308]
• C. Deng, J. Zhang, X. Yu, W. Zhang, and X. Zhang, “Determination of acetone in human breath by gas chromatography-mass spectrometrt and solid-phase microextraction with on-fiber derivatization”, J. Chromatogr. B., Vol. 810, No. 2, pp. 269-275, 2004. [https://doi.org/10.1016/S1570-0232(04)00657-9]
• T. Ligor, L. Pater, and B. Buszewski, “Application of an artificial neural network model for selection of potential lung cancer biomarkers”, J. Breath Res., Vol. 9, No. 2, p. 027106, 2015. [https://doi.org/10.1088/1752-7155/9/2/027106]
• Y. Saalberg and M. Wolff, “VOC breath biomarkers in lung cancer”, Clin. Chim. Acta., Vol. 459, pp. 5-9, 2016. [https://doi.org/10.1016/j.cca.2016.05.013]
• G. Rocco, G. Pennazza, M. Santonico, F. Longo, R. Rocco, P. Crucitti, and R. A. Incalzi, “Breathprinting and Early Diagnosis of Lung Cancer”, J. Thorac. Oncol., Vol. 13, No. 7, pp. 883-894, 2018. [https://doi.org/10.1016/j.jtho.2018.02.026]
• N. Kim, S. Choi, D. Yang, J. Bae, J. Park, and I. Kim, “Highly sensitive and selective hydrogen sulfide and toluene sensors using Pd functionalized WO₃ nanofibers for potential diagnosis of halitosis and lung cancer”, Sens. Actuators. B. Chem., Vol. 193, pp. 574-581, 2014. [https://doi.org/10.1016/j.snb.2013.12.011]
• W. Koo, S. Choi, S. Kim, J. Jang, H. L. Tuller, and I. Kim, “Heterogeneous Sensitization of Metal-Organic Framework Driven Metal@Metal Oxide Complex Catalysts on an Oxide Nanofiber Scaffold Toward Superior Gas Sensors”, J. Am. Chem. Soc., Vol. 138, No. 40, pp. 13431-13437, 2016. [https://doi.org/10.1021/jacs.6b09167]
• J. Lee, D. Kim, H. Lee, H. Park, A. Ma, S. Choi, and D. Lee, “Sonochemcial synthesis of HKUST-1-based CuO decorated with Pt nanoparticles for formaldehyde gas-sensor applications”, Sens. Actuators. B. Chem., Vol. 292, pp. 289-296, 2019 [https://doi.org/10.1016/j.snb.2019.04.062]
• R. Majidi and M. Nadafan, “Detection of exhaled gas by γ-graphyne and twin-graphene for early diagnosis of lung cancer: A density functional theory study”, Phys. Lett. A., Vol. 384, No. 1, p. 126036, 2020. [https://doi.org/10.1016/j.physleta.2019.126036]
• G. Gregis, J. B. Sanchez, I. Bezverkhyy, G. Weber, F. Berger, V. Fierro, J. P. Bellat, and A. Celzard, “Detection and quantification of lung cancer biomarkers by a micro-analytical device using a single metal oxide-based gas sensor”, Sens. Actuators. B. Chem., Vol. 255, pp. 391-400, 2018. [https://doi.org/10.1016/j.snb.2017.08.056]
• J. Lee, C. Lim, H. Song, S. Choi, and D. Lee, “A highly smart MEMS acetone gas sensors in array for diet-monitoring applications”, Micro. Nano. Sys. Lett., Vol. 9, No. 1, pp. 10(1)-10(6), 2021. [https://doi.org/10.1186/s40486-021-00136-1]
• D. D. Orbe, H. Park, M. Kwack, H. Lee, D. Kim, J. Lim, I, Park, M. Sohn, S. Lim, and D. Lee, “Breath analyzer for personalized monitoring of exercise-induced metabolic fat burning”, Sens. Actuators. B. Chem., Vol. 369, p. 132192, 2022. [https://doi.org/10.1016/j.snb.2022.132192]
• T. Saidi, O. Zaim, M. Moufid, N. E. Bari, R. Ionescu, and B. Bouchikhi, “Exhaled breath analysis using electronic nose and gas chromatography-mass spectrometry for non-invasive diagnosis of chronic kidney disease, diabetes mellitus and heathy subjects”, Sens. Actuators. B. Chem., Vol. 257, pp. 178-188, 2018. [https://doi.org/10.1016/j.snb.2017.10.178]
• F. A. Bahos, A. S. Vidal, C. S. Perez, J. M. Saniger, I. Gracia, M. M. Saniger-Alba, and D. Matatagui, “ZIF Nanocrystal-Based Surface Acoustic Wave (SAW) Electronic Nose to Detect Diabetes in Human Breath”, Biosensors, Vol. 9, No. 1, pp. 4(1)-4(13), 2019. [https://doi.org/10.3390/bios9010004]
• A. Gupta, T. S. Singh, and R. D. S. Yadava, “MEMS sensor array-based electronic nose for breath analysis-a simulation study”, J. Breath Res., Vol. 13, No. 1, p. 016003, 2019. [https://doi.org/10.1088/1752-7163/aad5f1]
• Q. Chen, Z. Chen, D. Liu, Z. He, and J. Wu, “Constructing E-nose Using Metal-Ion-Induced Assembly of Graphene Oxide for Diagnosis of Lung Cancer via Exhaled Breath”, ACS Appl. Mater. Interfaces, Vol. 12, No. 15, pp. 17713-17724, 2020. [https://doi.org/10.1021/acsami.0c00720]
• W. Li, H. Liu, D. Xie, Z. He, and X. Pi, “Lung Cancer Screening Based on Type-different Sensor Arrays”, Sci. Rep., Vol. 7, No. 1, pp. 1969(1)-1969(12), 2017. [https://doi.org/10.1038/s41598-017-02154-9]
• A. Kononov, B. Korotetsky, I. Jahatspanian, A. Gubal, A. Vasiliev, A. Arsenjev, A. Nefedov, A. Barchuk, I. Gorbunov, K. Kozyrev, A. Rassadina, E. Iakovleva, M. Sillanpaa, Z. Safaei, N. Ivanenko, N. Stolyarova, V. Chuchina, and A. Ganeev, “Online breath analysis using metal oxide semiconductor sensors (electronic nose) for diagnosis of lung cancer”. J. Breath Res., Vol. 14, No. 1, p. 016004, 2020. [https://doi.org/10.1088/1752-7163/ab433d]
• X. Zhong, D. Li, W. Du, M. Yan, Y. Wang, D. Huo, and C. Hou, “Rapid recognition of volatile organic compounds with colorimetric sensor arrays for lung cancer screening”, Anal. Bioanal. Chem., Vol. 410, No. 16, pp. 3671-3681, 2018. [https://doi.org/10.1007/s00216-018-0948-3]
• J. Chang, D. Lee, S. Ban, J. Oh, M. Jung, S. Kim, S. Park, K. Persaud, and S. Jheon, “Analysis of volatile organic compounds in exhaled breath for lung cancer diagnosis using a sensor system”, Sens. Actuators. B. Chem., Vol. 255, pp. 800-807, 2018. [https://doi.org/10.1016/j.snb.2017.08.057]