Neutron reflectivity (NR) measurements have been performed on stimulus-responsive polymer brushes containing N-isopropylacrylamide (NIPAAM), grafted on silicon blocks, at different temperatures and solvent contrasts.The NR data were analyzed using a novel method employing polymer density profiles predicted from lattice mean-field theory augmented with a polymer model to describe polymer solubility that decreases with increasing temperature.

The predicted density profiles at the different temperatures were self-consistent with the experimentally observed profiles: We found that the brush thickness decreased from 220 to 160 nm and the polymer volume fraction in solution increased from 55 to 75% when increasing temperature from 293 to 328 K. This new evaluation approach involved significantly fewer independent fitting parameters than methods involving layers of uniform densities.

Furthermore, the approach can straightforwardly be extended to analyze neutron reflectivity data of grafted, weakly charged polymers that display pH-sensitive behavior and also to block copolymers and to surfaces with adsorbed polymers.

We propose that such accurate model calculations provide a tool to interpret results from NR experiments more effectively and design neutron reflectivity experiments for optimal outcome.