Sniffing Bacteria with a Carbon-Dot Artificial Nose
Corresponding Author: Raz Jelinek
Nano-Micro Letters,
Vol. 13 (2021), Article Number: 112
Abstract
Continuous, real-time monitoring and identification of bacteria through detection of microbially emitted volatile molecules are highly sought albeit elusive goals. We introduce an artificial nose for sensing and distinguishing vapor molecules, based upon recording the capacitance of interdigitated electrodes (IDEs) coated with carbon dots (C-dots) exhibiting different polarities. Exposure of the C-dot-IDEs to volatile molecules induced rapid capacitance changes that were intimately dependent upon the polarities of both gas molecules and the electrode-deposited C-dots. We deciphered the mechanism of capacitance transformations, specifically substitution of electrode-adsorbed water by gas molecules, with concomitant changes in capacitance related to both the polarity and dielectric constants of the vapor molecules tested. The C-dot-IDE gas sensor exhibited excellent selectivity, aided by application of machine learning algorithms. The capacitive C-dot-IDE sensor was employed to continuously monitor microbial proliferation, discriminating among bacteria through detection of distinctive “volatile compound fingerprint” for each bacterial species. The C-dot-IDE platform is robust, reusable, readily assembled from inexpensive building blocks and constitutes a versatile and powerful vehicle for gas sensing in general, bacterial monitoring in particular.
Highlights:
1 Novel artificial nose based upon electrode-deposited carbon dots (C-dots). Significant selectivity and sensitivity determined by “polarity matching” between the C-dots and gas molecules.
2 The C-dot artificial nose facilitates, for the first time, real-time, continuous monitoring of bacterial proliferation and discrimination among bacterial species, both between Gram-positive and Gram-negative bacteria and between specific strains.
3 Machine learning algorithm furnishes excellent predictability both in the case of individual gases and for complex gas mixtures.
Keywords
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References
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H. Ding, S.B. Yu, J.S. Wei, H.M. Xiong, Full-color light-emitting carbon dots with a surface-state-controlled luminescence mechanism. ACS Nano 10, 484–491 (2016). https://doi.org/10.1021/acsnano.5b05406
N. Shauloff, N.L. Teradal, R. Jelinek, Porous Graphene oxide-metal ion composite for selective sensing of organophosphate gases. ACS Sensor 5, 1573–1581 (2020). https://doi.org/10.1021/acssensors.9b02367
C. Beleites, R. Salzer, Assessing and improving the stability of chemometric models in small sample size situations. Anal. Bioanal. Chem. 390, 1261–1271 (2008). https://doi.org/10.1007/s00216-007-1818-6
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L. Rokach, A. Schclar, E. Itach, Ensemble methods for multi-label classification. Expert Syst. Appl. 41, 7507–7523 (2014). https://doi.org/10.1016/j.eswa.2014.06.015
L. Breiman, Random forests. Machine Learning 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324
G. Tsoumakas, I. Katakis, Multi-Label Classification: An Overview. International Journal of Data Warehousing and Mining (IJDWM) 3, 1–13 (2007). https://doi.org/10.4018/jdwm.2007070101
A. Gradišek, M. van Midden, M. Koterle, V. Prezelj, D. Strle et al., Improving the chemical selectivity of an electronic nose to TNT, DNT and RDX using machine learning. Sensors 19, 1–15 (2019). https://doi.org/10.3390/s19235207
H. Liu, Q. Li, B. Yan, L. Zhang, Y. Gu, Bionic electronic nose based on mos sensors array and machine learning algorithms used for wine properties detection. Sensors 19, 45 (2019). https://doi.org/10.3390/s19010045
S. Acharyya, B. Jana, S. Nag, G. Saha, P.K. Guha, Single resistive sensor for selective detection of multiple VOCs employing SnO2 hollowspheres and machine learning algorithm: A proof of concept. Sensor. Actuat. B Chem. 321, 128484 (2020). https://doi.org/10.1016/j.snb.2020.128484
H. Bi, K. Yin, X. Xie, J. Ji, S. Wan et al., Ultrahigh humidity sensitivity of graphene oxide. Sci. Rep. 3, 1–7 (2013). https://doi.org/10.1038/srep02714
M. Mohammadi, S. Fardindoost, A. Iraji Zad, M. Almasi-Kashi, Room temperature selective sensing of aligned Ni nanowires using impedance spectroscopy. Mater. Res. Express 7, 025044 (2020). https://doi.org/10.1088/2053-1591/ab66ac
Q. Li, M. Zhou, Q. Yang, M. Yang, Q. Wu et al., Flexible carbon dots composite paper for electricity generation from water vapor absorption. J. Mater. Chem. A 6, 10639–10643 (2018). https://doi.org/10.1039/c8ta02505c
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