Characterization of Sequence Distributions in Random and Semi-Random Copolymers

We report a strategy for analyzing and distinguishing the sequence distributions of random and semirandom poly(lactic-co-glycolic acid) (PLGA) analogs using selective digestion at cleavable olefin-containing monomer units. Semirandom copolymers were synthesized via a parallel-successive (P-S) approach that enables coarse-grained sequence control by coupling telechelic oligomers of varied composition and length. Following cross-metathesis digestion, the resulting fragment distributions were fractionated and analyzed via NMR, SEC, and MALDI-MS. These postdigestion data directly reflect the microstructural arrangement of the cleavable units in the predigestion copolymers. Monte Carlo simulations were employed to model both random and P-S copolymerizations, offering in silico digestion data that elucidate the influence of oligomer feed ratios and dispersity on the resulting block-length distributions. Experimental and simulated results demonstrate that P-S copolymers exhibit broader and sometimes bimodal fragment distributions compared to their random analogs, validating the method’s capacity to encode and detect distinct microstructural features. This approach provides a scalable, analytically tractable platform for tuning and characterizing sequence distributions in degradable polyesters and potentially other polymer systems where sequence plays a critical role in material properties.