Metal oxide semiconductor gas sensing materials for early lung cancer diagnosis

Xiaoxi He; Hongfeng Chai; Yifan Luo; Lingfeng Min; Marc Debliquy; Chao Zhang

doi:10.26599/JAC.2023.9220694

| Sign up

PDF (1.6 MB)

Cite

EndNote(RIS) BibTeX

Collect

Submit Manuscript

Show Outline

Figures (8)

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Tables (3)

Table 1

Table 2

Table 3

Review | Open Access

Metal oxide semiconductor gas sensing materials for early lung cancer diagnosis

Xiaoxi He^{^a}, Hongfeng Chai^{^a}, Yifan Luo^{^a^,^c}, Lingfeng Min^{^b}, Marc Debliquy^{^c}, Chao Zhang^{^a}()

a School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China

b Department of Respiratory and Critical Care Medicine, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou 225001, China

c Service de Science des Matériaux, Faculté Polytechnique, Université de Mons, Mons 7000, Belgium

Show Author Information

Graphical Abstract

View original image Download original image

Abstract

The urgency of early lung cancer (LC) diagnosis and treatment has been more and more significant. Exhaled breath analysis using gas sensors is a promising way to find out if someone has LC due to its low-cost, non-invasive, and real-time monitoring compared with traditional invasive diagnostic techniques. Among sensor-based gas detection techniques, metal oxide semiconductor’s gas sensors are one of the most important types. This review presents the-state-of-art in metal oxide gas sensors for the diagnosis of early LC. First, the exhaled breath biomarkers are described with emphasis on the concentration of abnormal volatile organic compounds (VOCs) caused by the metabolic process of LC cells. Then, the research status of metal oxide gas sensors in LC diagnosis is summarized. The sensing performance and enhancement strategy of biomarkers provided by metal oxide semiconductor materials are reviewed. Another effective way to improve VOC detection performance is to build a gas sensor array. At the same time, various gas sensors combined with self-powered techniques are mentioned to display a broad development prospect in breath diagnosis. Finally, metal oxide gas sensor-based LC diagnosis is prospected.

Keywords

gas sensors metal oxides exhaled breath analysis lung cancer (LC) diagnosis

References

[1]

Torre

, Bray

, Siegel

, et al. Global cancer statistics, 2012. CA-Cancer J Clin 2015, 65: 87–108.

Biomarker	n-propanol [28]	Toluene [29]	Isoprene [30,31]	Cyclohexane [28]	Acetaldehyde [32]	Formaldehyde [33,34]	2-butanone [35]
Concentration for healthy subjects (ppb)	—	< 30	< 12	25	5	48	0.45–2.34^a
Concentration for sick patients (ppb)	450	> 30	12–580	200	100	83	0.79–4.25^a

Material	Structure	Target gas	Operating temperature (℃)	Detection limit	Response	Response/recovery time (s)	Ref.
Fe-doped ZnO	Nanoneedles	Isopropanol	275	250 ppb	4.7–250 ppb	51/762	[13]
ZnO	Nanoparticle	n-propanol	125	—	6.6–40 ppm	190/200	[75]
Cu₂O	Double-shell nano hollow microspheres	n-propanol	187	10 ppm	11–100 ppm	—	[76]
ZnSnO₃	Nanospheres	n-propanol	200	500 ppb	1.7–500 ppb	—	[77]
NiO	Porous nanoparticles	n-propanol	75	20 ppb	1.59–20 ppb	—	[78]
ZnO/NiO	Bi-metal organic framework (MOF)	n-propanol	275	200 ppb	2.51–200 ppb	—	[79]
SnO₂	Egg yolk–shell	Toluene	250	2 ppm	20–28.6 ppm	1.8/4.1	[87]
SnO₂	Three-layer hollow cubic	Toluene	250	—	20–38.7 ppm	0.8/6.1	[88]
Pd-loaded SnO₂	Single-layer hollow cubic	Toluene	230	100 ppb	20–41.4 ppm	0.4/16.5	[89]
NiFe₂O₄	Ordered mesoporous structure	Toluene	230	2 ppb	1–77.3 ppm or 0.44–2 ppb	704/1523	[29]
Ce-doped SnO₂	Cuboid	Butanone	175	500 ppb	20–23.9 ppm	20/—	[94]
Ce-doped SnO₂	Thin film	Butanone	210	—	100–181 ppm	—	[95]
WO₃	Urchin	Butanone	240	100 ppb	50–188.5 ppm	7/13	[96]
Ag-modified NiO	Porous spherical	Butanone	320	50 ppb	3.2–100 ppb	5.5/8	[98]
In-doped LaFeO₃	Multilayer porous	Formaldehyde	125	1 ppb	100–122 ppm	36/40	[102]
3D rGO–SnO₂	Honeycomb	Formaldehyde	125	10 ppb	1–28 ppm	30/60	[34]
SnO₂ and α-Fe₂O₃ with loaded Pt	Hollow nanospheres	Styrene	206	50 ppb	10–10.56 ppm	3/15	[61]
NiTiO₃	Nanoparticles	Acetaldehyde	575	200 ppb	43–50 ppm	10/26	[109]
In₂O₃	Nanosheets	Isoprene	200	5 ppb	5–103.5 ppm	124/204	[110]

Author (year)	Method and technique used	Advantage	Disadvantage	Ref.
Salimi and Hosseini (2021)	• Sensor array system with 19 commercial metal oxide gas sensors	• 87 subjects were included in the experiment• The classification of data was more detailed	• Large amount of data processing• The test process took a long time	[31]
Di Gilio et al. (2020)	• e-nose with 10 gas sensors for breath detection• Feature extraction algorithms used PCA classifier	• Large number of LC patients (115) and healthy controls (153) were selected for the study with good accuracy	• Sensitivity and specificity are not mentioned	[26]
Chen et al. (2020)	• e-nose was constructed by metal-ion-induced assembly of graphene oxide• Feature extraction algorithms used PCA classifier	• Better sensitivity and specificity of 95.8% and 96%, respectively• Economical and low power consumption sensor array	• Only four VOCs were mainly analyzed, namely acetone, isoprene, hydrothion, and ammonia	[111]
Kononov et al. (2019)	• Sensor array system with six metal oxide chemoresistance gas sensors• Classifier-used k-nearest neighbor (k-NN), logistic regression, and linear discriminant analysis (LDA)	• The solid-state sensors keep the sensors stable for long time• Sensitivity, specificity, and accuracy of 95%, 100%, and 97.2%, respectively	• The details about current smokers, past smokers, and nonsmokers are not given• Unreasonable control group selection	[112]
Chang et al. (2018)	• e-nose with seven metal oxide semiconductor gas sensors for breath detection• Used LDA	• Low cost• 85 subject samples were selected	• Accuracy, sensitivity, and specificity are only 75%, 79%, and 72%, respectively• Smokers were not selected	[113]
Masuda et al. (2015)	• LC diagnosis system based on self-made SnO₂ nanosheet combined with SnO₂ nanoparticles and noble metal catalysts	• The functional sensor system showed an excellent detection limit of 1-nonanal, an LC biomarker	• Only 1-nonanal is tested• Still in the sensor development stage	[47]
Zhang et al. (2017)	• e-nose with 14 gas sensors for breath detection (including metal oxide gas sensors)• Feature extraction algorithms used LDA classifier and fuzzy k-NN	• Sensitivity, specificity, and accuracy of 91.58%, 91.72%, and 91.59%, respectively	• Only 37 subjects were included for the study	[40]
Itoh et al. (2016)	• Combined SnO₂-based metal oxide metal gas sensor with GC	• Gas sensor only needs to detect the concentration of a certain VOC• Good analytical capability• Enough testing samples	• Cumbersome and high cost	[46]
Güntner et al. (2016)	• Sensor array with four nanostructured and highly porous Pt-, Si-, Pd-, and Ti-doped SnO₂ sensing films by FSP	• Stability, high selectivity, low detection limit, and average error	• No further development and testing results	[33]
Blatt et al. (2007)	• Sensor array system with six highly sensitive metal oxide gas sensors (fabricated by SACMI S.C.) and fuzzy k-NN and genetic algorithm	• Low cost, small size, and short response• Accuracy, sensitivity, and specificity of over 90%	• No external factors such as smoking were considered	[18]