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USAXS analysis of concentration-dependent self-assembling of polymer-brush-modified nanoparticles in ionic liquid: [I] concentrated-brush regime.

Yohei NakanishiRyohei IshigeHiroki OgawaKeita SakakibaraKohji OhnoTakashi MorinagaTakaya SatoToshiji KanayaYoshinobu Tsujii
Published in: The Journal of chemical physics (2018)
Using ultra-small angle X-ray scattering (USAXS), we analyzed the higher-order structures of nanoparticles with a concentrated brush of an ionic liquid (IL)-type polymer (concentrated-polymer-brush-modified silica particle; PSiP) in an IL and the structure of the swollen shell layer of PSiP. Homogeneous mixtures of PSiP and IL were successfully prepared by the solvent-casting method involving the slow evaporation of a volatile solvent, which enabled a systematic study over an exceptionally wide range of compositions. Different diffraction patterns as a function of PSiP concentration were observed in the USAXS images of the mixtures. At suitably low PSiP concentrations, the USAXS intensity profile was analyzed using the Percus-Yevick model by matching the contrast between the shell layer and IL, and the swollen structure of the shell and "effective diameter" of the PSiP were evaluated. This result confirms that under sufficiently low pressures below and near the liquid/crystal-threshold concentration, the studied PSiP can be well described using the "hard sphere" model in colloidal science. Above the threshold concentration, the PSiP forms higher-order structures. The analysis of diffraction patterns revealed structural changes from disorder to random hexagonal-closed-packing and then face-centered-cubic as the PSiP concentration increased. These results are discussed in terms of thermodynamically stable "hard" and/or "semi-soft" colloidal crystals, wherein the swollen layer of the concentrated polymer brush and its structure play an important role.
Keyphrases
  • ionic liquid
  • room temperature
  • high resolution
  • public health
  • magnetic resonance
  • mass spectrometry
  • optical coherence tomography
  • convolutional neural network
  • liquid chromatography