Decoupling Sensitivity and Recovery Kinetics of MOF/MWCNT-Based Gas Sensors Through Aerodynamic Chamber Design
Source abstract
Abstract
This study investigates the aerodynamic trade-off between adsorption-driven sensitivity and desorption-driven recovery in MOF/MWCNT-based gas sensors. We compared two chamber designs: a Stable Flow Chamber (SFC) with streamlined laminar flow and a Disturbed Flow Chamber (DFC) featuring baffle-induced perturbations. Experimental results demonstrated that the SFC significantly enhanced sensitivity, showing increases of 16.3% (QCM), 21.1% (Resistive), and 53.8% (EIS) compared to the DFC, by promoting stable diffusion into the sensing layer. Conversely, the DFC markedly accelerated recovery kinetics, reducing recovery times by 10.3% (QCM), 12.3% (Resistive), and 55.9% (EIS), respectively, due to baffle-induced aerodynamic scrubbing. Computational fluid dynamics (CFD) confirmed that the DFC generates a 3.3 times higher wall shear stress even under a low Reynolds number regime (Re ≈ 2.3). These findings establish that aerodynamic design can effectively decouple sensitivity and recovery kinetics, offering a route to optimize sensor performance without modifying materials.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Gas Sensing Nanomaterials and Sensors · Metal-Organic Frameworks: Synthesis and Applications · Advanced Sensor and Energy Harvesting Materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Environmental Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Funding
MSIT (Ministry of Science, ICT), Korea · National Research Foundation of Korea
References (37)
Show source-provided references
1. Cincinelli, A., and Martellini, T. (2017). Indoor air quality and health. Int. J. Environ. Res. Public Health, 14.10.3390/ijerph14111286 ↗
2. The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants10.1038/sj.jea.7500165 ↗
3. Indoor air quality10.1177/1559827616653343 ↗
4. Tran, V.V., Park, D., and Lee, Y.-C. (2020). Indoor air pollution, related human diseases, and recent trends in the control and improvement of indoor air quality. Int. J. Environ. Res. Public Health, 17.10.3390/ijerph17082927 ↗
5. Nie, T., Zhang, G., Sun, Y., Wang, W., Wang, T., and Duan, H. (2025). Effects of Indoor Air Quality on Human Physiological Impact: A Review. Buildings, 15.10.3390/buildings15081296 ↗
6. Indoor air pollution and health: Bridging perspectives from developing and developed countries10.1146/annurev-environ-012220-010602 ↗
7. The sick building syndrome10.4103/0019-5278.43262 ↗
8. Wang, J., and Wang, R. (2025). Development of gas sensors and their applications in health safety, medical detection, and diagnosis. Chemosensors, 13.10.3390/chemosensors13050190 ↗
9. A review of standards and guidelines set by international bodies for the parameters of indoor air quality10.5094/APR.2015.084 ↗
10. Saini, J., Dutta, M., and Marques, G. (2020). Indoor air quality monitoring systems based on internet of things: A systematic review. Int. J. Environ. Res. Public Health, 17.10.3390/ijerph17144942 ↗
11. Spinelle, L., Gerboles, M., Kok, G., Persijn, S., and Sauerwald, T. (2017). Review of portable and low-cost sensors for the ambient air monitoring of benzene and other volatile organic compounds. Sensors, 17.10.3390/s17071520 ↗
12. Measurement of interfacial processes at electrode surfaces with the electrochemical quartz crystal microbalance10.1021/cr00014a006 ↗
13. Verwendung von Schwingquarzen zur Wägung dünner Schichten und zur Mikrowägung10.1007/BF01337937 ↗
14. A review of quartz crystal microbalance for chemical and biological sensing applications10.1007/s11220-023-00413-w ↗
15. A survey of the 2006–2009 quartz crystal microbalance biosensor literature10.1002/jmr.1117 ↗
16. Tulliani, J.-M., Inserra, B., and Ziegler, D. (2019). Carbon-based materials for humidity sensing: A short review. Micromachines, 10.10.3390/mi10040232 ↗
17. Metal oxide semi-conductor gas sensors in environmental monitoring10.3390/s100605469 ↗
18. Zhao, D., Yu, S., Jiang, W.-J., Cai, Z.-H., Li, D.-L., Liu, Y.-L., and Chen, Z.-Z. (2022). Recent progress in metal-organic framework based fluorescent sensors for hazardous materials detection. Molecules, 27.10.3390/molecules27072226 ↗
19. Carbon nanotube-based chemical sensors10.1002/smll.201502555 ↗
20. Metal–organic framework-derived metal oxides for resistive gas sensing: A review10.1039/D3CP04777F ↗
21. Hierarchical structuring of metal–organic framework thin-films on quartz crystal microbalance (QCM) substrates for selective adsorption applications10.1039/C5TA05620A ↗
22. Study of the factors affecting mass transport in electrochemical gas sensors10.1039/a703644b ↗
23. Stabilization of the Eulerian model for incompressible multiphase flow by artificial diffusion10.1016/j.jcp.2010.11.025 ↗
24. Fluid dynamic simulation of a measurement chamber for electronic noses10.1016/S0925-4005(02)00071-0 ↗
25. Effect of various flow rate on current fluctuations of amperometric gas sensors10.1016/j.snb.2018.12.006 ↗
26. Schlichting, H., and Gersten, K. (2016). Boundary-Layer Theory, Springer.10.1007/978-3-662-52919-5 ↗
27. Cussler, E.L. (2009). Diffusion: Mass Transfer in Fluid Systems, Cambridge University Press.10.1017/CBO9780511805134 ↗
28. Bergman, T.L. (2011). Fundamentals of Heat and Mass Transfer, John Wiley & Sons.
29. Ottino, J.M. (1989). The Kinematics of Mixing: Stretching, Chaos, and Transport, Cambridge University Press.
30. Chaotic mixer for microchannels10.1126/science.1066238 ↗
31. A benzene vapor sensor based on a metal-organic framework-modified quartz crystal microbalance10.1016/j.snb.2019.127365 ↗
32. Optimization of gas detectors placement in complex industrial layouts based on CFD simulations10.1016/j.jlp.2022.104859 ↗
33. Annanouch, F.-E., Bouchet, G., Perrier, P., Morati, N., Reynard-Carette, C., Aguir, K., and Bendahan, M. (2018, January 9–12). How the chamber design can affect gas sensor responses. Proceedings of the Eurosensors 2018 Conference, Graz, Austria.10.3390/proceedings2130820 ↗
34. Choi, Y. (2024). Machine Learning-Enhanced Detection and Identification of Urban and Indoor Gaseous Pollutants. [Ph.D. Dissertation, Seoul National University of Science and Technology].
35. Chemical dynamics at the gas−surface interface10.1021/jp9536007 ↗
36. Study on the response and recovery properties of semiconductor gas sensors using a high-speed gas-switching system10.1016/j.snb.2008.06.044 ↗
37. Ryu, J., Shim, S., Song, J., Park, J., Kim, H.S., Lee, S.-K., Shin, J.C., Mun, J., and Kang, S.-W. (2023). Effect of measurement system configuration and operating conditions on 2D material-based gas sensor sensitivity. Nanomaterials, 13.10.3390/nano13030573 ↗