An equation driven quality classification of (a)symmetric gradient, gradient-block, block-gradient-block and block copolymers

The control of the A/B comonomer distribution over individual chains targeting block and/or gradient monomer distributions is essential to macroscopic properties resulting copolymers. Matrix-based kinetic Monte Carlo simulations allow compare each chain a representative copolymer sample with desired (mathematical) composition, as defined by inclusion probabilities (PA/B) taking B-functionalization degree (B-Func) maximally 50%. A so-called average deviation (SD∗ value) results per chain, close 0 (normalized) structural 〈SD〉 corresponding an almost perfect structure and 1 representing worst case scenario B-homopolymer. previously assigned transitions from excellent good poor are, however, somewhat arbitrary, e.g. for both symmetric copolymers threshold 〈SDGood/Poor〉 0.3 currently utilized only specific asymmetric cases (30% B-Func block, block-gradient, gradient) 〈SDExc/Good〉 values have been reported. present work puts forward equation driven method obtain values, minimizing arbitrary nature quality classification given type more importantly aligning assessment any containing elements. Emphasis on complete SD (instead its average) (a)symmetric AB gradient, A-gradient AB, AB-block B, introducing overall fractions (fGr/Bl) novel parameter alongside targeted polymerization (target DP) B-Func. Ideal theoretical structures equal length implementation PA/B profiles are dealt with, they represent best actual synthesis recipe could deliver in limit. It shown that log-normal can be reliably used approximate distribution, coefficients determination (R2) very one follow . further showcased dominant must higher than those block-like well-defined difficult achieve ones. also recommended report together standard σSD.