Presentation + Paper
7 March 2019 Analysis of microorganisms, chlorinated hydrocarbons and hyaluronic acid gel using Raman based optofluidic techniques and SERS
Ota Samek, Zdenek Pilat, Silvie Bernatova, Jan Jezek, Tereza Klementova, Martin Kizovsky, Pavel Zemanek, Katarina Rebrosova, Filip Ruzicka
Author Affiliations +
Proceedings Volume 10894, Plasmonics in Biology and Medicine XVI; 108940D (2019) https://doi.org/10.1117/12.2508425
Event: SPIE BiOS, 2019, San Francisco, California, United States
Abstract
We report on the development of a set of Raman based techniques to monitor a large variety of biological and chemical analytes, such as various microorganisms, gels of hyaluronic acid and selected halogenated hydrocarbons using Raman spectroscopy, Raman tweezers and surface-enhanced Raman spectroscopy (SERS). We analyzed individual microbial cells with Raman tweezers to provide solutions for fast and label-free identification of specific bacterial or yeast species. We designed an optofluidic SERS platform for quantification of sub-millimolar concentrations of halogenated environmental pollutants such as 1,2,3-trichloropropane and chloroform. We also examined the gel structure of hyaluronic acid by Raman spectroscopy.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ota Samek, Zdenek Pilat, Silvie Bernatova, Jan Jezek, Tereza Klementova, Martin Kizovsky, Pavel Zemanek, Katarina Rebrosova, and Filip Ruzicka "Analysis of microorganisms, chlorinated hydrocarbons and hyaluronic acid gel using Raman based optofluidic techniques and SERS ", Proc. SPIE 10894, Plasmonics in Biology and Medicine XVI, 108940D (7 March 2019); https://doi.org/10.1117/12.2508425
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Raman spectroscopy

Bacteria

Surface enhanced Raman spectroscopy

Statistical analysis

Optofluidics

Microorganisms

Principal component analysis

Back to Top