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Published on: March 13, 2013
Sensing of Organic Vapors with Plasmonic Distributed Bragg Reflectors
Zdeněk Krtouš1,2, Oleksandr Polonskyi3, Pavel Pleskunov2
1Department of Macromolecular Physics, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, 180 00 Prague, Czech Republic.
This study introduces novel plasmonic Distributed Bragg Reflector sensors for detecting volatile organic compounds (VOCs). These sensors utilize silver nanoparticles in polymer layers, offering a cost-effective and portable solution for real-time air quality monitoring.
Area of Science:
- Materials Science
- Nanotechnology
- Chemical Sensing
Background:
- Advancements in air quality monitoring rely on diverse sensor technologies.
- Polymer-based Distributed Bragg Reflectors (DBRs) show promise for cost-effective, portable volatile organic compound (VOC) sensors.
- A key challenge is achieving sufficient refractive index contrast in polymer DBR fabrication.
Purpose of the Study:
- To fabricate plasmonic DBR sensors using low-temperature plasma techniques.
- To investigate their potential for detecting VOCs.
- To develop an optical model linking sensor properties, microstructure, and performance.
Main Methods:
- Synthesized silver (Ag) nanoparticles via gas aggregation and embedded them into poly(lactic acid) (PLA) layers to form nanocomposites.
- Fabricated a 6-bilayer plasmonic DBR by alternating plain PLA and Ag-PLA nanocomposite layers.
- Utilized a generalized Maxwell-Garnett approach for optical modeling.
Main Results:
- Achieved a plasmonic DBR with 77% reflectance at 570 nm.
- Demonstrated sensor response to ethanol vapors, causing a red-shift in reflection peak to 640 nm and color change due to polymer swelling.
- The developed model accurately reproduced DBR spectra, accounting for structural changes and nanoparticle behavior.
Conclusions:
- Plasmonic DBRs offer a promising route for low-cost, real-time VOC sensing.
- The fabricated sensors exhibit optical changes upon exposure to VOCs, indicating sensing capability.
- The optical model provides accurate prediction of sensor performance and degradation effects.
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