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A mechanistic study of the radical copolymerisation of p ‐chlorostyrene with citraconic anhydride via 13 C NMR spectroscopy
Author(s) -
Brown Paul G.,
Fujimori Kiyohisa
Publication year - 1994
Publication title -
macromolecular chemistry and physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.57
H-Index - 112
eISSN - 1521-3935
pISSN - 1022-1352
DOI - 10.1002/macp.1994.021950309
Subject(s) - comonomer , monomer , copolymer , polymer chemistry , chemistry , dissociation (chemistry) , mole fraction , nuclear magnetic resonance spectroscopy , equilibrium constant , organic chemistry , polymer
The composition and monomer unit sequence distribution of copolymers of p ‐chlorostyrene ( p ‐CST) with citraconic (α‐methylmaleic) anhydride (CA) prepared in methyl ethyl ketone (MEK) at 50,0 ± 0,1°C were determined over a range of comonomer feed mole fractions using 13 C NMR spectroscopy. The monomeric units in these copolymers were found to display a mild tendency to alternate, which increased as the mole fraction of CA in the feed increased. The existence of a 1:1 charge‐transfer complex formed between p ‐CST and CA in the feed was confirmed via UV spectroscopy and the equilibrium constant for complex formation determined to be 0,10 ± 0,04 L/mol in MEK at ambient temperature. The terminal, penultimate, complex‐participation and complex‐dissociation models were each tested for applicability to the mechanism of copolymerisation for this comonomer system. On the basis of copolymer composition data, the penultimate, complex‐participation and complex‐dissociation models were each found to provide an adequate description of the mechanism of copolymerisation in the comonomer system via non‐linear least squares methods. An analysis of the comonomer unit sequence distributions for these semi‐alternating copolymers and the application of reactivity ratio related test functions to this data allowed applicability of the four models under investigation to be separated more explicitly, with the complex‐dissociation model providing the best fit to the data.