Responsive Surfactant-Driven Morphology Transformation of Block Copolymer Microparticles.
Chemistry (Weinheim an der Bergstrasse, Germany)(2025)
Huazhong University of Science and Technology
Abstract
Block copolymer (BCP) microparticles, which exhibit rapid change of morphology and physicochemical property in response to external stimuli, represent a promising avenue for the development of programmable smart materials. Among the methods available for generating BCP microparticles with adjustable morphologies, the confined assembly of BCPs within emulsions has emerged as a particularly facile and versatile approach. This review provides a comprehensive overview of the role of responsive surfactants in modulating interfacial interactions at the oil-water interface, which facilitates controlled BCP microparticle morphology. We elucidate how variations in the properties of responsive surfactants, activated by external stimuli, influence BCP chain arrangement and interfacial selectivity. Additionally, this review explores the applications of shape-switchable microparticles in advanced technologies such as smart display, fluorescence modulation, magnetic resonance imaging, drug delivery, and photonic crystal. Finally, the challenges and prospective future directions in this rapidly evolving field are discussed.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper