Deciphering Evolution, Function, and Observation of Crystallization-Driven Self-Assembly

Crystallization-driven self-assembly (CDSA) offers precise control over the size, shape, and hierarchical organization of polymeric nanostructures by harnessing the crystallization of a core-forming block. Unlike conventional self-assembly, CDSA favors the formation of low-curvature morphologies, such as fibers and platelets, with exceptional uniformity. This review highlights key CDSA strategies, including seeded growth, self-seeding, and polymerization-induced CDSA, along with factors influencing assembly, such as polymer composition, solvent, temperature, and additives. We summarize advanced characterization techniques─spanning light scattering, microscopy, spectroscopy and fluorescence imaging─and computational approaches, including Monte Carlo and Brownian dynamics simulations, for understanding assembly mechanisms and predicting morphologies. Finally, we discuss emerging applications in biomedicine, catalysis, optoelectronics, and functional materials, and outline future challenges in precision control, multitechnique characterization, and scalable synthesis. By integrating mechanistic insights, advanced characterization, and application-driven design, this review establishes a comprehensive foundation for future development of CDSA-based functional materials.

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Publication Details

Journal
Chemical Reviews
Published
2025-10-13
DOI
https://doi.org/10.1021/acs.chemrev.5c00298
Citations
7
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
3.62
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article

Deciphering Evolution, Function, and Observation of Crystallization-Driven Self-Assembly

Andrew P. Dove, Tianlai Xia, Rachel K. O’Reilly, Yujie Xie et al.
7 citations
Chemical Reviews
Machine Learning in Materials Science
3.62
article

Deciphering Evolution, Function, and Observation of Crystallization-Driven Self-Assembly

Andrew P. Dove, Tianlai Xia, Rachel K. O’Reilly, Yujie Xie, Laihui Xiao
article en
7 citations

Abstract

Crystallization-driven self-assembly (CDSA) offers precise control over the size, shape, and hierarchical organization of polymeric nanostructures by harnessing the crystallization of a core-forming block. Unlike conventional self-assembly, CDSA favors the formation of low-curvature morphologies, such as fibers and platelets, with exceptional uniformity. This review highlights key CDSA strategies, including seeded growth, self-seeding, and polymerization-induced CDSA, along with factors influencing assembly, such as polymer composition, solvent, temperature, and additives. We summarize advanced characterization techniques─spanning light scattering, microscopy, spectroscopy and fluorescence imaging─and computational approaches, including Monte Carlo and Brownian dynamics simulations, for understanding assembly mechanisms and predicting morphologies. Finally, we discuss emerging applications in biomedicine, catalysis, optoelectronics, and functional materials, and outline future challenges in precision control, multitechnique characterization, and scalable synthesis. By integrating mechanistic insights, advanced characterization, and application-driven design, this review establishes a comprehensive foundation for future development of CDSA-based functional materials.

Chemical ReviewsVol. 125(21)
Shanghai University (CN), Indiana University School of Medicine, University of Birmingham (GB)
Openalex Percentile: Top 6%
Machine Learning in Materials Science
3.62
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Deciphering Evolution, Function, and Observation of Crystallization-Driven Self-Assembly — Andrew P. Dove, Tianlai Xia, et al. · Chemical Reviews (2025) | TGRS Research Map | TGRS