Chaperone solvent-assisted assembly of polymers at the interface of two immiscible liquids

Sai Zhao, Yongkang Jiang, Yuchen Fu, Wei Chen, Qinrong Zhang, Liulin He, Changxiong Huang*, Yao Liu*, Xiao Cheng Zeng*, Yu Chai*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

4 Citations (Scopus)
22 Downloads (CityUHK Scholars)

Abstract

The assembly of polymers at liquid-liquid interfaces offers a promising strategy for fabricating two-dimensional polymer films. However, a significant challenge arises when the polymers lack inherent interfacial traction. In response, we introduce an approach termed chaperone solvent-assisted assembly. This approach utilizes a target polymer, X, along with three solvents: α, β, and γ. α and β are poor solvents for X and immiscible with each other, while γ is a good solvent for X and miscible with both α and β, thus serving as the chaperone solvent. The cross-interface diffusion of γ induces the assembly of interfacially nonactive X at the α-β interface, and this mechanism is verified through systematic in situ and ex situ studies. We show that chaperone solvent-assisted assembly is versatile and reliable for the interfacial assembly of polymers, including those that are interfacially nonactive. Several practical applications based on chaperone solvent-assisted assembly are also demonstrated. © The Author(s) 2024.
Original languageEnglish
Article number7423
JournalNature Communications
Volume15
Online published28 Aug 2024
DOIs
Publication statusPublished - 2024

Funding

The authors acknowledge financial support from the Research Grants Council of Hong Kong (Project No.21304421, Y.C.), the National Natural Science Foundation of China (Project No. 22003053, Y.C.), the Natural Science Foundation of Guangdong Province, China (Project No. 2023A1515011457, Y.C.), the Natural Science Foundation of Sichuan Province, China (Project No. 2023NSFSC0312, Y.C.), CityU Strategic Interdisciplinary Research Grant (Project No. 2020SIRG035, Y.C.), and Hong Kong Institute for Advanced Study. The authors also thank OpenAI for editing.

Publisher's Copyright Statement

  • This full text is made available under CC-BY-NC-ND 4.0. https://creativecommons.org/licenses/by-nc-nd/4.0/

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