Characterizing Acoustic Wave Propagation in Hydrogels Using Transient Grating Spectroscopy

Acoustic and thermal energy transport is critical for a wide range of applications, including biomedical diagnostics (e.g. ultrasound), renewable energy production, and building thermal management. Improving the performance of these technologies requires precise tailoring of acoustic and thermal energy transport, which remains a fundamental challenge. Understanding how to tune these materials and study them in real-time requires methods that can be used in varied environmental conditions. Hydrogels are of interest because they are easily fabricated, tunable, and have environment specific behavior. Our research investigates using transient grating spectroscopy, a method that employs acoustic wave signatures, to mechanically characterize hydrogels and other inhomogeneous materials. We provide a systematic approach across a range of polymer groups, to decompose the effects of crosslinking, swelling, and/or entanglements on Rayleigh wave speeds, which can then be related to a material’s elastic modulus. Transient grating also presents a method for dynamic response measurements of hydrogels. Traditional characterization methods, such as indentation, result in large discrepancies in measurements, which indicates that mechanical properties of hydrogels differ significantly in dynamic and static conditions. These differences are the first step to developing models for the direct contribution of poroelasticity and viscoelasticity on hydrogel behavior. This project will show how we can model and subsequently develop hydrogels with the mechanical properties necessary for our desired applications. This work was supported by the UCSB Materials Research Science and Engineering Center (MRSEC) of the National Science Foundation under award No. DMR-1720256 (IRG-3). The development of the transient grating spectroscopy at UCSB is supported by a DURIP grant from the U.S. Army Research Office under the award number W911NF-20-1-0161. Allison Chau acknowledges support from the NSF Graduate Research Fellowship Program under Grant No. 1650114. Angela Pitenis acknowledges funding support from the NSF CAREER award (CMMI-CAREER-2048043).