Double network hydrogel-based phantoms for single and multiphoton imaging Fizza Haseeb 1 Konstantinos Bourdakos 4 , Ewan Forsyth 1 , Kerry Setchfield 3 , Alistair Gorman 2 , Seshasailam Venkateswaran 1 , Amanda J. Wright 3 , Sumeet Mahajan 4 , Mark Bradley 1 1 School of Chemistry, University of Edinburgh, UK, 2 School of Engineering, University of Edinburgh, UK, 3 3FF Optics and Photonics Research Group, Faculty of Engineering, University of Nottingham UK, 4 School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, UK Optical imaging technology has become a critical research tool in biomedicine, because of its ability to track diseases in real time and in a non-invasive manner within living subjects. 1 Currently much effort is focused on designing and fabricating new NIR laser sources and detectors, devising novel label free imaging methodologies and application of machine learning to help optimize imaging at greater depths. Stable, reliable, and reproducible standards are required for the initial verification, optimization, and calibration of these systems. 2 Human tissue, although serves as a realistic model for this purpose, possess heterogeneity among samples and raises ethical concerns 3 . Alternatively, phantoms are constructed using materials ranging from solid, semi solid and liquid, 3 incorporating molecules that respond to imaging modality under consideration. Herein we introduce new material/ construct to be used in the fabrication of tissue phantoms. The material comprises of a double network hydrogel matrix made using two interpenetrating polymer networks: agarose and polyacrylamide. The material was observed to be robust and stable over long periods of time (weeks-months). Moreover, the material proved useful for testing in the near infrared range and with a range of single and multi-photon imaging modalities (conventional one photon excited fluorescence imaging, coherent anti-Stokes Raman scattering, second harmonic generation imaging and two photon fluorescence imaging). References 1. H. Kasban, M. El-Bendary and D. Salama, International Journal of Information Science and Intelligent System , 2015, 4 , 37- 58. 2. L. Hacker, H. Wabnitz, A. Pifferi, T. J. Pfefer, B. W. Pogue and S. E. Bohndiek, Nature Biomedical Engineering , 2022, 6 , 541- 558. 3. F. W. Esmonde-White, K. A. Esmonde-White, M. R. Kole, S. A. Goldstein, B. J. Roessler and M. D. Morris, Analyst , 2011, 136 , 4437-4446.
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