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In situ assessment of cellular hydrogel swelling behaviors via microbubble‑induced ultrasonic resonance scattering.

Created on 31 Jul 2026

Authors

Wanglinhan Zhang, Jiangang Xu, Qi Yuan, Guojie Luo, Ming Ma, Qingqing Wang, Yi He, Shuchang Zhang, Jie Huang, Zhongqing Su

Published in

Ultrasonics. Volume 168. Pages 108243. Jul 25, 2026. Epub Jul 25, 2026.

Abstract

Hydrogels exhibit stimuli-responsive swelling behaviors that are beneficial for biomedical applications including disease diagnosis and therapeutic monitoring. However, the current prevailing methods of assessing the swelling behaviors are inherently ex situ, requiring the removal of hydrogels from their operating environment. This has hindered the use of hydrogels in continuous physiological monitoring contexts. Achieving reliable and in situ assessment of hydrogel swelling is essential yet remains a challenge. Here, we present a new monitoring strategy that exploits resonance scattering of ultrasonic elastic waves by microbubbles embedded in the cellular hydrogel, to enable quantitative and in situ assessment of the hydrogel swelling behaviors. Both experimental observations and simulation results reveal that the uniformly dispersed microbubbles (100-300 μm in diameter) in hydrogel induce characteristic ultrasonic resonance scattering, leading to pronounced attenuation of the transmitted elastic waves in the frequency domain. The characteristic attenuation peak shifts in accordance with the variation of microbubble dimensions, which are governed by the swelling states of the cellular hydrogel. To validate, a dedicated measurement platform incorporating ultrasonic transducers is configured, and experimental results reveal that the peak shifts quantitatively in response to the swelling and deswelling of the cellular hydrogel, providing a direct ultrasonic readout of hydrogel swelling dynamics. This work establishes a reliable, nondestructive, real-time, and in situ monitoring method for assessing hydrogel swelling/deswelling, providing a fabrication‑efficient platform to support hydrogel-driven diagnosis and therapy.

PMID:
42531650
Bibliographic data and abstract were imported from PubMed on 31 Jul 2026.

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