The triumvirate of effective and rapid synthesis, analysis, and artificial intelligence to explore the structure-property relationship of copolymers

Abstract

Understanding the structure-property relationship is of paramount importance for tailoring copolymers for specific applications. Poly(N-acryloylmorpholine)-block-poly(N-isopropylacrylamide) (PNAM-b-PNIPAM) diblock copolymers were synthesized by reversible addition–fragmentation chain transfer (RAFT) polymerization with varying Mn and composition, providing the basis for deducing structure-property relationships. The chemical structure of the copolymers was analyzed by mass spectrometry (MS). A novel and efficient mass spectrum processing methodology was developed for the detailed analysis of polymers/copolymers that greatly expands the upper mass limit of the time-of-flight (TOF) analyzers in the linear mode up to 20,000 Da. Our method “makes visible” the mass peaks of the individual copolymer species and their isotopologues providing effective and fast automatized analysis. The self-assembly property of the thermoresponsive PNAM-b-PNIPAM diblocks in aqueous solutions was investigated by dynamic light scattering (DLS) experiments, and quantified by determining the incipient temperature of the phase transition. For rapid evaluation, an artificial neural network (ANN) was created to explore the hidden relationships between the structural information obtained by our novel mass analysis method and the properties as well as to predict the self-assembly behavior of the copolymers.

Keywords

• Copolymers
• Structure-property relation
• MALDI-TOF MS
• artificial intelligence