Abstract
In recent years, advancements in air quality monitoring have been driven by the development of various sensor technologies, each with distinct advantages and limitations. Among these, polymer-based Distributed Bragg Reflectors (DBRs) have garnered significant interest for use in cost-effective, portable colorimetric sensors for detecting volatile organic compounds (VOCs). However, a key challenge in the fabrication of polymer-based DBRs lies in achieving an adequate refractive index contrast between the individual polymer layers. In this work, we fabricate plasmonic DBR sensors by a combination of low-temperature plasma-based techniques with reduced environmental footprint, investigate their potential as VOC sensors, and propose an optical model that links the sensors' optical properties and microstructure. Plasmonic nanoparticles of silver (Ag) are synthesized by gas aggregation and embedded into thermally evaporated poly(lactic acid) (PLA) layers to create nanocomposites with an enhanced refractive index (∼2.0). A 6-bilayer plasmonic DBR sensor is then produced by alternating depositions of plain PLA and nanocomposite layers as low and high refractive index materials, respectively. The resulting DBR achieves a 77% reflectance at 570 nm. The potential use-case of such a DBR as a VOC sensor is highlighted by its optical response upon exposure to ethanol (a model VOC) vapors as well as other VOCs (water, propanol, acetone, hexane). In an ethanol atmosphere, swelling of the polymer layers occurs, resulting in a red-shift of the reflection peak to 640 nm and a change in the DBR color. We take advantage of a generalized Maxwell-Garnett approach to create an advanced model that accurately reproduces the DBR spectra and captures swelling and degradation by accounting for structural changes and the behavior of isolated and coalesced Ag NPs within individual layers. Despite a decrease in the sensing performance with the number of swelling cycles, these plasmonic DBRs offer a promising solution for low-cost real-time VOC sensing.