journal article·2023-07Open access

New driving forces and recent advances in polymerization-induced self-assembly

Qianxi Gu, Haolan Li, Erik Jan Cornel, Jianzhong Du

Cell Reports Physical Science· Volume 4· Issue 7

Abstract

Polymerization-induced self-assembly (PISA) is a powerful tool to prepare dispersed block copolymer nanoparticles with various morphologies in a range of solvents. This method is based on the chain extension of a soluble homopolymer with a monomer that forms an insoluble second block. Once a critical chain length of this second block is achieved, polymer aggregation into nanoparticles occurs in situ. Various nanoparticle morphologies can be accurately targeted by adjusting the degree of polymerization (DP) of either the stabilizer block or core-forming block and the total solids content (or final polymer concentration). In this Perspective, we give a brief but comprehensive introduction to this nanoparticle preparation method. Afterward, we discuss emerging PISA methods that push the boundaries of this technology. Some of these new methods utilize polymer insolubility (polymer-solvent interactions) as a self-assembly driving force, and these are discussed first; afterward, recently discovered PISA formulations where in situ self-assembly relies on polymer-polymer interactions are discussed. Examples of such polymer-polymer interactions include hydrogen bonds, electrostatic effects, chirality, and crystallization. The current state and limitations of PISA are discussed in the final section of this Perspective. Additionally, we provide insights on how the current limitations of PISA can be overcome with the aim of making PISA nanoparticle synthesis more applicable for a broad range of audiences with higher nanoparticle demands.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Advanced Polymer Synthesis and Characterization · Polymer Surface Interaction Studies · Luminescence and Fluorescent Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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