Novel multi-angle light scattering (MALS)-based method for quantifying polymer conformation: Determining the ratio of random coils, spheres, and rods

Polymer conformation analysis is crucial in polymer science, as the spatial arrangement of macromolecules directly influences their physical properties and behavior. Light scattering techniques provide valuable insights, but each has limitations. Small-angle neutron scattering (SANS) and small-angle X-ray scattering (SAXS) offer detailed structural insights but are constrained by instrument availability, particularly for SANS. Static light scattering (SLS), dynamic light scattering (DLS), and viscometry estimate conformation via the radius of gyration (R_g)-to-hydrodynamic radius (R_h) ratio, though accuracy depends on model selection. Traditional MALS enables shape determination through the R_g vs. molecular weight (Mw) relationship but requires either a broad-distributed sample or multiple narrow-distributed samples, making synthesis time-consuming and complex.
Here, we introduce a novel MALS-based method to determine polymer conformation and quantify the ratio of three primary polymer shapes (random coils, spheres, and rods) by comparing their experimental and theoretical angular dependence across different conformations and sizes. The method was validated using ultra-high Mw (UHMW) bottlebrush polymers with varying side-chain densities, covering structures from random coils to rods. The results correlated well with R_g/R_h ratios, confirming the accuracy of this approach. Notably, for UHMW samples, the method performs optimally when R_g exceeds 60 nm.
This new MALS-based method provides an accessible and efficient alternative for polymer conformation analysis without requiring extensive sample synthesis or complex model fitting. By leveraging angular dependence, it enables a quantitative structural assessment, surpassing the traditional R_g/R_h method. Future work will expand its applicability to diverse polymer architectures and enhance precision for complex systems, supporting streamlined and scalable polymer characterization for both research and industry.