Water Transport-Induced Liquid-Liquid Phase Separation Facilitates Gelation for Controllable and Facile Fabrication of Physically Crosslinked Microgels.
Michael W ChenDongdong FanXiangjian LiuDongbo HanYuhong JinYanxiao AoYuyang ChenZhiqiang LiuYiting FengSida LingKaini LiangWenyu KongJianhong XuYanan DuPublished in: Advanced materials (Deerfield Beach, Fla.) (2024)
Physically crosslinked microgels (PCMs) offer a biocompatible platform for various biomedical applications. However, current PCM fabrication methods suffer from their complexity and poor controllability, due to their reliance on altering physical conditions to initiate gelation and their dependence on specific materials. To address this issue, a novel PCM fabrication method is devised, which employs water transport-induced liquid-liquid phase separation (LLPS) to trigger the intermolecular interaction-supported sol-gel transition within aqueous emulsion droplets. This method enables the controllable and facile generation of PCMs through a single emulsification step, allowing for the facile production of PCMs with various materials and sizes, as well as controllable structures and mechanical properties. Moreover, this PCM fabrication method holds great promise for diverse biomedical applications. The interior of the PCM not only supports the encapsulation and proliferation of bacteria but also facilitates the encapsulation of eukaryotic cells after transforming the system into an all-aqueous emulsion. Furthermore, through appropriate surface functionalization, the PCMs effectively activate T cells in vitro upon coculturing. This work represents an advancement in PCM fabrication and offers new insights and perspectives for microgel engineering.
Keyphrases
- tissue engineering
- low cost
- high glucose
- diabetic rats
- ionic liquid
- quantum dots
- induced apoptosis
- reduced graphene oxide
- highly efficient
- signaling pathway
- mental health
- oxidative stress
- drug induced
- high resolution
- cell cycle arrest
- machine learning
- high throughput
- big data
- endothelial cells
- artificial intelligence
- deep learning