Next Article in Journal
Review of the Recent Advances in Nano-Biosensors and Technologies for Healthcare Applications
Previous Article in Journal
Development of Graphene-Doped TiO2-Nanotube Array-Based MIM-Structured Sensors and Its Application for Methanol Sensing at Room Temperature
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Proceeding Paper

Morphological Effects in SnO2 Chemiresistors for Ethanol Detection: A Systematic Statistical Analysis of Results Published in the Last 5 Years †

National Institute of Optics of the National Research Council (CNR-INO), Unit of Brescia, 25123 Brescia, Italy
Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.
Chem. Proc. 2021, 5(1), 75; https://doi.org/10.3390/CSAC2021-10474
Published: 30 June 2021

Abstract

:
SnO2 is one of the most studied materials in gas sensing. Among the many strategies adopted to optimize its sensing properties, the fine tuning of the morphology in nanoparticles, nanowires, and nanosheets, as well as their eventual hierarchical organization, has become an active field of research. In this work, results published in the literature over the last five years are systematically analyzed focusing on response intensities recorded with chemiresistors based on pure SnO2 for ethanol detection in dry air. Results indicate that no morphology clearly outperforms others, while a few individual sensors emerge as remarkable outliers with respect to the whole dataset.

1. Introduction

Chemiresistors based on semiconducting metal oxides are among the most popular gas sensing devices. Their success comes from their high sensitivity to a broad range of chemicals, their reduced size and power consumption, and their suitability for mass production at relatively reduced costs. To optimize the sensing layer, the fine control of the morphology, both at the level of individual nanostructures and at the level of their hierarchical assembly, has been reported as very effective [1,2].
In this work, with the aim to have a more general and reliable picture of the state of the art, results published in the literature in the last five years are systematically analyzed, focusing on response intensities recorded with chemiresistors based on pure SnO2 for ethanol detection in dry air, as the case example. In particular, we chose to focus on SnO2 because it is the most studied material among semiconducting metal oxides. Similarly, we chose ethanol as target gas because it is widely used as a test gas for the development of innovative materials (morphologies) and it is a key component in many applications [3].

2. Materials and Methods

This work considers the responses to ethanol reported for chemiresistors based on pure SnO2 in the period from January 2015 to July 2020. In order to have a common background between all the considered responses, only dry air tests have been taken into account.
The morphology of the SnO2 layer is described at two different levels: at the level of individual nanostructures and the level of their eventual hierarchical assembly.
Concerning the shape of individual crystallites composing the sensing layer, it has been categorized as follows:
  • Nanorods: elongated nanostructures with a high aspect-ratio, and surfaces identified by well-defined crystalline planes;
  • Nanoparticles: spherical nanostructures, such as those used in thick films;
  • Nanosheets: thin nanostructures extending in two dimensions.

3. Results

As an example of the shape of elementary nanostructures widely investigated in the literature, Figure 1 reports the SEM images for two SnO2 layers composed by a disordered network of nanowires (Figure 1a), and by a disordered network of nanoparticles (Figure 1b) [1]. Therefore, some nanoparticles are distributed over the substrate individually, while others are distributed in μm-sized grains as a consequence of aggregation often observed in nanoparticle-based layers [1].
Boxplots resuming the responses to 10 ppm and to 300 ppm of ethanol reported in literature are shown in Figure 2a,b, respectively, grouping the results by nanostructure morphologies, namely nanorods, nanoparticles, and nanosheets.
The statistical parameters describing these distributions are reported in Table 1 and Table 2 for data shown in Figure 2a,b, respectively.
Statistical parameters reported in these tables are: the number of samples considered in each category (morphology of elementary nanostructures); the number of outliers identified for each category; the values of the 1st, 2nd, and 3rd quartiles (Q1, Q2, and Q3) of the response amplitude Ggas/Gair; and the values of the upper and lower whiskers. The p-value of the median test comparing the median response of morphologies two by two are also reported in order to have a statistical check about the similarity and dissimilarity between median responses of the different morphologies.

4. Discussion

The distributions of the response intensities shown in Figure 2 depend on the gas concentration. This is partially due to the fact that different authors often tested their sensors against different ethanol concentration so there is no a complete overlap between concentration used in different articles. In other words, the sensors whose response is shown in Figure 2a are not exactly the same sensors whose response is shown in Figure 2b. Nonetheless, despite these differences, a common feature is that no morphology clearly performs better than other morphologies. Median tests reported in Table 1 and Table 2 feature a p-value that is larger than 0.05 in all situations. This means that there is no clear evidence to reject the null hypothesis, i.e., there is no clear evidence to reject the hypothesis that the couple of morphologies under the test are not distinguishable. The same is observed for other concentrations and also considers the eventual hierarchical organization of the individual nanostructures into assemblies, such as hollow spheres, fibers, hollow fibers, etc. [46]. On the other hand, some materials emerge as outliers with respect to all morphologies. In Figure 2a, there are five outliers: four are the responses from layers composed by nanoparticles, namely [4,5,6,7] with response intensities of about 236, 50, 49, and 50 (to 10 ppm of ethanol), and one composed by nanosheets [33] featuring a response Ggas/Gair ≈ 50. As a reference, the median responses to this ethanol concentration are around 4.55, 2.3, and 10 for nanoparticles, nanorods, and nanosheets, respectively. Concerning the concentration of 300 ppm, four outliers emerges: the nanoparticles synthesized by [45], and two types of nanorods and the nanosheets developed by [38]. These materials feature responses of about 2000, 4070, 1609, and 495, compared with the median responses of 71, 52, and 38 for nanosheets, nanorods, and nanoparticles, respectively.
These results are arguably due to the longer tradition of the synthesis of nanoparticles with respect to those of nanowires and nanosheets. Such a longer experience may reasonably imply a more developed capability to effectively combine the many parameters underlying the sensing mechanism, which may counterbalance the advantages arising from the fine morphological tuning inherent in the more recent nanostructures.

Funding

His research was funded by Regione Lombardia and Fondazione Cariplo through the project EMPATIA@LECCO.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data are in the reported figures and in the cited references.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ponzoni, A.; Comini, E.; Concina, I.; Ferroni, M.; Falasconi, M.; Gobbi, E.; Sberveglieri, V.; Sberveglieri, G. Nanostructured Metal Oxide Gas Sensors, a Survey of Applications Carried out at SENSOR Lab, Brescia (Italy) in the Security and Food Quality Fields. Sensors 2012, 12, 17023–17045. [Google Scholar] [CrossRef] [PubMed]
  2. Lee, J.-H. Gas Sensors Using Hierarchical and Hollow Oxide Nanostructures: Overview. Sens. Actuators B Chem. 2009, 140, 319–336. [Google Scholar] [CrossRef]
  3. Palma, S.I.C.J.; Traguedo, A.P.; Porteira, A.R.; Frias, M.J.; Gamboa, H.; Roque, A.C.A. Machine Learning for the Meta-Analyses of Microbial Pathogens’ Volatile Signatures. Sci. Rep. 2018, 8, 3360. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Lee, S.-H.; Galstyan, V.; Ponzoni, A.; Gonzalo-Juan, I.; Riedel, R.; Dourges, M.-A.; Nicolas, Y.; Toupance, T. Finely Tuned SnO2 Nanoparticles for Efficient Detection of Reducing and Oxidizing Gases: The Influence of Alkali Metal Cation on Gas-Sensing Properties. ACS Appl. Mater. Interfaces 2018, 10, 10173–10184. [Google Scholar] [CrossRef] [PubMed]
  5. Tricoli, A.; Pratsinis, S.E. Dispersed Nanoelectrode Devices. Nat. Nanotechnol. 2010, 5, 54–60. [Google Scholar] [CrossRef] [PubMed]
  6. Li, H.; Chu, S.; Ma, Q.; Li, H.; Che, Q.; Wang, J.; Wang, G.; Yang, P. Multilevel Effective Heterojunctions Based on SnO2/ZnO 1D Fibrous Hierarchical Structure with Unique Interface Electronic Effects. ACS Appl. Mater. Interfaces 2019, 11, 31551–31561. [Google Scholar] [CrossRef] [PubMed]
  7. Tofighi, G.; Degler, D.; Junker, B.; Müller, S.; Lichtenberg, H.; Wang, W.; Weimar, U.; Barsan, N.; Grunwaldt, J.-D. Microfluidically Synthesized Au, Pd and AuPd Nanoparticles Supported on SnO2 for Gas Sensing Applications. Sens. Actuators B Chem. 2019, 292, 48–56. [Google Scholar] [CrossRef] [Green Version]
  8. Wang, T.; Jiang, B.; Yu, Q.; Kou, X.; Sun, P.; Liu, F.; Lu, H.; Yan, X.; Lu, G. Realizing the Control of Electronic Energy Level Structure and Gas-Sensing Selectivity over Heteroatom-Doped In2O3 Spheres with an Inverse Opal Microstructure. ACS Appl. Mater. Interfaces 2019, 11, 9600–9611. [Google Scholar] [CrossRef] [PubMed]
  9. Zhang, Y.; He, X.; Li, J.; Miao, Z.; Huang, F. Fabrication and Ethanol-Sensing Properties of Micro Gas Sensor Based on Electrospun SnO2 Nanofibers. Sens. Actuators B Chem. 2008, 132, 67–73. [Google Scholar] [CrossRef]
  10. Tricoli, A.; Righettoni, M.; Pratsinis, S.E. Minimal Cross-Sensitivity to Humidity during Ethanol Detection by SnO2–TiO2 Solid Solutions. Nanotechnology 2009, 20, 315502. [Google Scholar] [CrossRef]
  11. Kassem, O.; Saadaoui, M.; Rieu, M.; Viricelle, J.-P. A Novel Approach to a Fully Inkjet Printed SnO2-Based Gas Sensor on a Flexible Foil. J. Mater. Chem. C 2019, 7, 12343–12353. [Google Scholar] [CrossRef]
  12. Kotchasak, N.; Wisitsoraat, A.; Tuantranont, A.; Phanichphant, S.; Yordsri, V.; Liewhiran, C. Highly Sensitive and Selective Detection of Ethanol Vapor Using Flame-Spray-Made CeOx-Doped SnO2 Nanoparticulate Thick Films. Sens. Actuators B Chem. 2018, 255, 8–21. [Google Scholar] [CrossRef]
  13. Li, X.; Peng, K.; Dou, Y.; Chen, J.; Zhang, Y.; An, G. Facile Synthesis of Wormhole-Like Mesoporous Tin Oxide via Evaporation-Induced Self-Assembly and the Enhanced Gas-Sensing Properties. Nanoscale Res. Lett. 2018, 13, 14. [Google Scholar] [CrossRef] [Green Version]
  14. Li, R.; Chen, S.; Lou, Z.; Li, L.; Huang, T.; Song, Y.; Chen, D.; Shen, G. Fabrication of Porous SnO2 Nanowires Gas Sensors with Enhanced Sensitivity. Sens. Actuators B Chem. 2017, 252, 79–85. [Google Scholar] [CrossRef]
  15. Xue, N.; Zhang, Q.; Zhang, S.; Zong, P.; Yang, F. Highly Sensitive and Selective Hydrogen Gas Sensor Using the Mesoporous SnO2 Modified Layers. Sensors 2017, 17, 2351. [Google Scholar] [CrossRef] [Green Version]
  16. Zito, C.A.; Perfecto, T.M.; Volanti, D.P. Impact of Reduced Graphene Oxide on the Ethanol Sensing Performance of Hollow SnO2 Nanoparticles under Humid Atmosphere. Sens. Actuators B Chem. 2017, 244, 466–474. [Google Scholar] [CrossRef] [Green Version]
  17. Li, S.-H.; Meng, F.-F.; Chu, Z.; Luo, T.; Peng, F.-M.; Jin, Z. Mesoporous SnO2 Nanowires: Synthesis and Ethanol Sensing Properties. Adv. Condens. Matter Phys. 2017, 2017, 1–6. [Google Scholar] [CrossRef] [Green Version]
  18. Palla Papavlu, A.; Mattle, T.; Temmel, S.; Lehmann, U.; Hintennach, A.; Grisel, A.; Wokaun, A.; Lippert, T. Highly Sensitive SnO2 Sensor via Reactive Laser-Induced Transfer. Sci. Rep. 2016, 6, 25144. [Google Scholar] [CrossRef] [Green Version]
  19. Liu, J.; Dai, M.; Wang, T.; Sun, P.; Liang, X.; Lu, G.; Shimanoe, K.; Yamazoe, N. Enhanced Gas Sensing Properties of SnO2 Hollow Spheres Decorated with CeO2 Nanoparticles Heterostructure Composite Materials. ACS Appl. Mater. Interfaces 2016, 8, 6669–6677. [Google Scholar] [CrossRef]
  20. Naik, A.; Parkin, I.; Binions, R. Gas Sensing Studies of an N-n Hetero-Junction Array Based on SnO2 and ZnO Composites. Chemosensors 2016, 4, 3. [Google Scholar] [CrossRef] [Green Version]
  21. Tan, W.; Yu, Q.; Ruan, X.; Huang, X. Design of SnO2-Based Highly Sensitive Ethanol Gas Sensor Based on Quasi Molecular-Cluster Imprinting Mechanism. Sens. Actuators B Chem. 2015, 212, 47–54. [Google Scholar] [CrossRef]
  22. Lou, Z.; Wang, L.; Wang, R.; Fei, T.; Zhang, T. Synthesis and Ethanol Sensing Properties of SnO2 Nanosheets via a Simple Hydrothermal Route. Solid-State Electron. 2012, 76, 91–94. [Google Scholar] [CrossRef]
  23. Francioso, L.; De Pascali, C.; Creti, P.; Radogna, A.V.; Capone, S.; Taurino, A.; Epifani, M.; Baldacchini, C.; Bizzarri, A.R.; Siciliano, P.A. Nanogap Sensors Decorated with SnO2 Nanoparticles Enable Low-Temperature Detection of Volatile Organic Compounds. ACS Appl. Nano Mater. 2020, 3, 3337–3346. [Google Scholar] [CrossRef]
  24. Zhang, L.; Tong, R.; Ge, W.; Guo, R.; Shirsath, S.E.; Zhu, J. Facile One-Step Hydrothermal Synthesis of SnO2 Microspheres with Oxygen Vacancies for Superior Ethanol Sensor. J. Alloy Compd. 2020, 814, 152266. [Google Scholar] [CrossRef]
  25. Van Hieu, N.; Kim, H.-R.; Ju, B.-K.; Lee, J.-H. Enhanced Performance of SnO2 Nanowires Ethanol Sensor by Functionalizing with La2O3. Sens. Actuators B Chem. 2008, 133, 228–234. [Google Scholar] [CrossRef]
  26. Nguyen, K.; Hung, C.M.; Ngoc, T.M.; Thanh Le, D.T.; Nguyen, D.H.; Nguyen Van, D.; Nguyen Van, H. Low-Temperature Prototype Hydrogen Sensors Using Pd-Decorated SnO2 Nanowires for Exhaled Breath Applications. Sens. Actuators B Chem. 2017, 253, 156–163. [Google Scholar] [CrossRef]
  27. Choi, K.S.; Park, S.; Chang, S.-P. Enhanced Ethanol Sensing Properties Based on SnO2 Nanowires Coated with Fe2O3 Nanoparti-cles. Sens. Actuators B Chem. 2017, 238, 871–879. [Google Scholar] [CrossRef]
  28. Wang, Q.; Yao, N.; An, D.; Li, Y.; Zou, Y.; Lian, X.; Tong, X. Enhanced Gas Sensing Properties of Hierarchical SnO2 Nanoflower Assembled from Nanorods via a One-Pot Template-Free Hydrothermal Method. Ceram. Int. 2016, 42, 15889–15896. [Google Scholar] [CrossRef]
  29. Cai, Z.; Park, S. Enhancement Mechanisms of Ethanol-Sensing Properties Based on Cr2O3 Nanoparticle-Anchored SnO2 Nanowires. J. Mater. Res. Technol. 2020, 9, 271–281. [Google Scholar] [CrossRef]
  30. Cui, Y.; Zhang, M.; Li, X.; Wang, B.; Wang, R. Investigation on Synthesis and Excellent Gas-Sensing Properties of Hierarchical Au-Loaded SnO2 Nanoflowers. J. Mater. Res. 2019, 34, 2944–2954. [Google Scholar] [CrossRef] [Green Version]
  31. Wang, B.; Sun, L.; Wang, Y. Template-Free Synthesis of Nanosheets-Assembled SnO2 Hollow Spheres for Enhanced Ethanol Gas Sensing. Mater. Lett. 2018, 218, 290–294. [Google Scholar] [CrossRef]
  32. Zito, C.A.; Perfecto, T.M.; Volanti, D.P. Palladium-Loaded Hierarchical Flower-like Tin Dioxide Structure as Chemosensor Exhibiting High Ethanol Response in Humid Conditions. Adv. Mater. Interfaces 2017, 4, 1700847. [Google Scholar] [CrossRef]
  33. Zhou, Q.; Chen, W.; Li, J.; Tang, C.; Zhang, H. Nanosheet-Assembled Flower-like SnO2 Hierarchical Structures with Enhanced Gas-Sensing Performance. Mater. Lett. 2015, 161, 499–502. [Google Scholar] [CrossRef]
  34. Xu, M.-H.; Cai, F.-S.; Yin, J.; Yuan, Z.-H.; Bie, L.-J. Facile Synthesis of Highly Ethanol-Sensitive SnO2 Nanosheets Using Homogeneous Precipitation Method. Sens. Actuators B Chem. 2010, 145, 875–878. [Google Scholar] [CrossRef]
  35. Cao, S.; Zeng, W.; Zhu, Z.; Peng, X. Synthesis of SnO2 Nanostructures from 1D to 3D via a Facile Hydrothermal Method and Their Gas Sensing Properties. J. Mater. Sci. Mater. Electron. 2015, 26, 1820–1826. [Google Scholar] [CrossRef]
  36. Zeng, W.; Zhang, H.; Li, Y.; Chen, W.; Wang, Z. Hydrothermal Synthesis of Hierarchical Flower-like SnO2 Nanostructures with Enhanced Ethanol Gas Sensing Properties. Mater. Res. Bull. 2014, 57, 91–96. [Google Scholar] [CrossRef]
  37. Zeng, W.; He, Q.; Pan, K.; Wang, Y. Synthesis of Multifarious Hierarchical Flower-like SnO2 and Their Gas-Sensing Properties. Phys. E Low-Dimens. Syst. Nanostructures 2013, 54, 313–318. [Google Scholar] [CrossRef]
  38. Firooz, A.A.; Mahjoub, A.R.; Khodadadi, A.A. Highly Sensitive CO and Ethanol Nanoflower-like SnO2 Sensor among Various Morphologies Obtained by Using Single and Mixed Ionic Surfactant Templates. Sens. Actuators B Chem. 2009, 141, 89–96. [Google Scholar] [CrossRef]
  39. Zhang, B.; Fu, W.; Meng, X.; Ruan, A.; Su, P.; Yang, H. Enhanced Ethanol Sensing Properties Based on Spherical-Coral-like SnO2 Nanorods Decorated with α-Fe2O3 Nanocrystallites. Sens. Actuators B Chem. 2018, 261, 505–514. [Google Scholar] [CrossRef]
  40. Hoa, L.T.; Cuong, N.D.; Hoa, T.T.; Khieu, D.Q.; Long, H.T.; Quang, D.T.; Hoa, N.D.; Hieu, N.V. Synthesis, Characterization, and Comparative Gas Sensing Properties of Tin Dioxide Nanoflowers and Porous Nanospheres. Ceram. Int. 2015, 41, 14819–14825. [Google Scholar] [CrossRef]
  41. Wang, X.; Han, X.; Xie, S.; Kuang, Q.; Jiang, Y.; Zhang, S.; Mu, X.; Chen, G.; Xie, Z.; Zheng, L. Controlled Synthesis and Enhanced Catalytic and Gas-Sensing Properties of Tin Dioxide Nanoparticles with Exposed High-Energy Facets. Chem. Eur. J. 2012, 18, 2283–2289. [Google Scholar] [CrossRef]
  42. Punginsang, M.; Wisitsoraat, A.; Sriprachuabwong, C.; Phokharatkul, D.; Tuantranont, A.; Phanichphant, S.; Liewhiran, C. Roles of Cobalt Doping on Ethanol-Sensing Mechanisms of Flame-Spray-Made SnO2 Nanoparticles−electrolytically Exfoliated Graphene Interfaces. Appl. Surf. Sci. 2017, 425, 351–366. [Google Scholar] [CrossRef]
  43. Asgari, M.; Saboor, F.H.; Mortazavi, Y.; Khodadadi, A.A. SnO2 Decorated SiO2 Chemical Sensors: Enhanced Sensing Performance toward Ethanol and Acetone. Mater. Sci. Semicond. Process. 2017, 68, 87–96. [Google Scholar] [CrossRef]
  44. Li, M.; Zhu, H.; Cheng, J.; Zhao, M.; Yan, W. Synthesis and Improved Ethanol Sensing Performance of CuO/SnO2 Based Hollow Microspheres. J. Porous Mater. 2017, 24, 507–518. [Google Scholar] [CrossRef]
  45. NaderiNasrabadi, M.; Mortazavi, Y.; Khodadadi, A.A. Highly Sensitive and Selective Gd2O3-Doped SnO2 Ethanol Sensors Synthesized by a High Temperature and Pressure Solvothermal Method in a Microreactor. Sens. Actuators B Chem. 2016, 230, 130–139. [Google Scholar] [CrossRef]
  46. Ponzoni, A. Morphological Effects in SnO2 Chemiresistors for Ethanol Detection: A Review in Terms of Central Performances and Outliers. Sensors 2021, 21, 29. [Google Scholar] [CrossRef]
Figure 1. Examples of two different morphologies investigated in the literature for SnO2-based chemiresistors. (a) Film composed by a disordered network of SnO2 nanowires; (b) film composed by a disordered network of SnO2 nanoparticles, which are distributed either individually or in μm-sized aggregates. Reprinted from [1].
Figure 1. Examples of two different morphologies investigated in the literature for SnO2-based chemiresistors. (a) Film composed by a disordered network of SnO2 nanowires; (b) film composed by a disordered network of SnO2 nanoparticles, which are distributed either individually or in μm-sized aggregates. Reprinted from [1].
Chemproc 05 00075 g001
Figure 2. Boxplots resuming the statistics of the response intensities of SnO2 chemiresistors grouped by crystallite shape. (a) Statistics recorded vs. 10 ppm of ethanol; (b) statistics recorded vs. 300 ppm of ethanol.
Figure 2. Boxplots resuming the statistics of the response intensities of SnO2 chemiresistors grouped by crystallite shape. (a) Statistics recorded vs. 10 ppm of ethanol; (b) statistics recorded vs. 300 ppm of ethanol.
Chemproc 05 00075 g002
Table 1. Statistics of data shown in Figure 1a (responses to 10 ppm of ethanol).
Table 1. Statistics of data shown in Figure 1a (responses to 10 ppm of ethanol).
NanoparticlesNanorodsNanosheets
Number of samples [Refs.]30 [4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]5 [25,26,27,28,29]7 [22,30,31,32,33,34]
Number of outliers401
Ggas/Gair, Q132.0754.175
Ggas/Gair, Q2 (median)4.552.310
Ggas/Gair, Q3145.82516.75
Ggas/Gair, whisker low1.822.4
Ggas/Gair, whisker up30818
p-value median test, nanorodsNaN0.680.18
p-value median test, nanoparticles0.68NaN0.08
p-value median test, nanosheets0.180.08NaN
Table 2. Statistics of data shown in Figure 1b (responses to 300 ppm of ethanol).
Table 2. Statistics of data shown in Figure 1b (responses to 300 ppm of ethanol).
NanosheetsNanorodsNanoparticles
Number of samples [Refs.]5 [30,35,36,37,38]12 [35,36,37,38,39,40,41]7 [35,37,40,42,43,44,45]
Number of outliers121
Ggas/Gair, Q158.2523.514.375
Ggas/Gair, Q2 (median)715238
Ggas/Gair, Q319311585
Ggas/Gair, whisker low293.42.9
Ggas/Gair, whisker up93135100
p-value median test, nanorodsNaN0.50.08
p-value median test, nanoparticles0.5NaN0.21
p-value median test, nanosheets0.080.21NaN
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ponzoni, A. Morphological Effects in SnO2 Chemiresistors for Ethanol Detection: A Systematic Statistical Analysis of Results Published in the Last 5 Years. Chem. Proc. 2021, 5, 75. https://doi.org/10.3390/CSAC2021-10474

AMA Style

Ponzoni A. Morphological Effects in SnO2 Chemiresistors for Ethanol Detection: A Systematic Statistical Analysis of Results Published in the Last 5 Years. Chemistry Proceedings. 2021; 5(1):75. https://doi.org/10.3390/CSAC2021-10474

Chicago/Turabian Style

Ponzoni, Andrea. 2021. "Morphological Effects in SnO2 Chemiresistors for Ethanol Detection: A Systematic Statistical Analysis of Results Published in the Last 5 Years" Chemistry Proceedings 5, no. 1: 75. https://doi.org/10.3390/CSAC2021-10474

Article Metrics

Back to TopTop