Selective Detection of Functionalized Carbon Particles based on Polymer Semiconducting and Conducting Devices as Potential Particulate Matter Sensors
Yunjia Song
Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218 USA
Search for more papers by this authorNan Chen
Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218 USA
Search for more papers by this authorQifeng Jiang
Department of Chemistry, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218 USA
Search for more papers by this authorTushita Mukhopadhyay
Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218 USA
Search for more papers by this authorWudyalew Wondmagegn
Department of Electrical and Computer Engineering, The College of New Jersey, Ewing, NJ, 08628 USA
Search for more papers by this authorRebekka S. Klausen
Department of Chemistry, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218 USA
Search for more papers by this authorCorresponding Author
Howard E. Katz
Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218 USA
Department of Chemistry, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218 USA
E-mail: [email protected]
Search for more papers by this authorYunjia Song
Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218 USA
Search for more papers by this authorNan Chen
Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218 USA
Search for more papers by this authorQifeng Jiang
Department of Chemistry, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218 USA
Search for more papers by this authorTushita Mukhopadhyay
Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218 USA
Search for more papers by this authorWudyalew Wondmagegn
Department of Electrical and Computer Engineering, The College of New Jersey, Ewing, NJ, 08628 USA
Search for more papers by this authorRebekka S. Klausen
Department of Chemistry, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218 USA
Search for more papers by this authorCorresponding Author
Howard E. Katz
Department of Materials Science and Engineering, Johns Hopkins University, 206 Maryland Hall, 3400 North Charles Street, Baltimore, MD, 21218 USA
Department of Chemistry, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD, 21218 USA
E-mail: [email protected]
Search for more papers by this authorAbstract
This paper reports a new mechanism for particulate matter detection and identification. Three types of carbon particles are synthesized with different functional groups to mimic the real particulates in atmospheric aerosol. After exposing polymer-based organic devices in organic field effect transistor (OFET) architectures to the particle mist, the sensitivity and selectivity of the detection of different types of particles are shown by the current changes extracted from the transfer curves. The results indicate that the sensitivity of the devices is related to the structure and functional groups of the organic semiconducting layers, as well as the morphology. The predominant response is simulated by a model that yielded values of charge carrier density increase and charge carriers delivered per unit mass of particles. The research points out that polymer semiconductor devices have the ability to selectively detect particles with multiple functional groups, which reveals a future direction for selective detection of particulate matter.
Conflict of Interest
A provisional patent application was filed based on this work.
Open Research
Data Availability Statement
The data that support the findings of this study are openly available in Johns Hopkins Research Data Repository at [DOI], reference number 45.
Supporting Information
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| smll202310527-sup-0001-SuppMat.pdf6 MB | Supporting Information |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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