Enhanced Colorimetric Detection of Volatile Organic Compounds Using a Dye-Incorporated Photonic Crystal-Based Sensor Array
© 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH.
| Veröffentlicht in: | Advanced materials (Deerfield Beach, Fla.). - 1998. - (2024) vom: 10. Sept., Seite e2409297 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2024
|
| Zugriff auf das übergeordnete Werk: | Advanced materials (Deerfield Beach, Fla.) |
| Schlagworte: | Journal Article colorimetric arrays dyes photonic crystals sensors volatile organic compounds |
| Zusammenfassung: | © 2024 The Author(s). Advanced Materials published by Wiley‐VCH GmbH. Chemoresponsive dyes offer the potential to selectively detect volatile organic compounds (VOCs) unique to certain disease states. Among different VOC sensing techniques, colorimetric sensing offers the advantage of facile recognition. However, it is often challenging to discern the color changes by the naked eye. Here, highly sensitive colorimetric VOC sensor arrays from dye-incorporated colloidal photonic crystals (dye-cPhCs) are reported. cPhCs are scalably fabricated on a 4-inch wafer by spin-coating of silica nanoparticles (NPs) dispersed in a photo-cross-linkable monomer, where the gradient shear flow along the film thickness creates densely-packed square arrays of NPs in the top layers, whereas the bulk is quasi-amorphous with larger periodicities. The broadened reflection peak allows for augmented dye absorption originating from the overlap between the photonic bandgap edge of the cPhC and the dye absorption peak, leading to a more noticeable color change upon exposure to VOCs. The sensor array generates distinct color difference maps for acetaldehyde, acetone, and acetic acid, respectively, without any data amplification. The limit of detection for acetaldehyde, acetone, and acetic acid is 1, 0.1, and 0.02 ppm, respectively. Moreover, VOC can be diagonalized by visually intuitive pattern recognition, and principal component analysis at reduced dimensionality is demonstrated |
|---|---|
| Beschreibung: | Date Revised 10.09.2024 published: Print-Electronic Citation Status Publisher |
| ISSN: | 1521-4095 |
| DOI: | 10.1002/adma.202409297 |