Sputtering deposition under limited adatom mobility: an effective method to prepare a SERS substrate based on Ag@ZnO composite deposited onto electrospun cellulose acetate fibers
Source abstract
Abstract
Sputtering is a well-known physical vapor deposition method for the synthesis of coatings and thin films. When combined with other techniques, such as electrospinning, it becomes a straightforward yet effective method to prepare substrates that can render various morphological structures at the nano and micrometer scale. The resulting substrates possess high surface area, making them suitable for applications such as gas sensing and surface-enhanced Raman scattering (SERS). The present work reports the successful magnetron sputtering deposition of silver/zinc oxide (Ag@ZnO) onto cellulose acetate fibers. By adjusting the sputtering process parameters, we limited the deposition of ZnO and Ag to be under limited adatom mobility conditions. Secondary electron imaging revealed that the resulting ZnO morphology on the fibers presented a columnar grain morphology at the nanometer scale. The ZnO crystallites of the said grains possessed a wurtzite structure, as shown by Raman spectroscopy. Ag sputtering deposition onto the previously deposited ZnO-covered fibers, produced under limited adatom mobility and short deposition time, rendered nanoparticles of various shapes and sizes, as revealed by electron microscopy. Then, using benzenethiol (BZT) and rhodamine 6G (R6G) as the probe molecules, the Ag@ZnO/CA fibers were tested as SERS substrates. All the prepared composite materials showed good SERS response towards the BZT and R6G. In summary, the novel approach is a practical and straightforward route for preparing Ag/ZnO-based SERS substrates.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: ZnO doping and properties · Gold and Silver Nanoparticles Synthesis and Applications · Copper-based nanomaterials and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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