Breaking the nanoparticle loading-dispersion dichotomy in polymer nanocomposites with the art of croissant-making

Giovanni Santagiuliana, Olivier T. Picot, Maria Crespo, Harshit Porwal, Han Zhang, Yan Li, Luca Rubini, Samuele Colonna, Alberto Fina, Ettore Barbieri, Anne B. Spoelstra, Giulia Mirabello, Joseph P. Patterson, Lorenzo Botto, Nicola M. Pugno, Ton Peijs, Emiliano Bilotti

Research output: Contribution to journalArticleAcademicpeer-review

26 Citations (Scopus)
137 Downloads (Pure)


The intrinsic properties of nanomaterials offer promise for technological revolutions in many fields, including transportation, soft robotics, and energy. Unfortunately, the exploitation of such properties in polymer nanocomposites is extremely challenging due to the lack of viable dispersion routes when the filler content is high. We usually face a dichotomy between the degree of nanofiller loading and the degree of dispersion (and, thus, performance) because dispersion quality decreases with loading. Here, we demonstrate a potentially scalable pressing-and-folding method (P & F), inspired by the art of croissant-making, to efficiently disperse ultrahigh loadings of nanofillers in polymer matrices. A desired nanofiller dispersion can be achieved simply by selecting a sufficient number of P & F cycles. Because of the fine microstructural control enabled by P & F, mechanical reinforcements close to the theoretical maximum and independent of nanofiller loading (up to 74 vol %) were obtained. We propose a universal model for the P & F dispersion process that is parametrized on an experimentally quantifiable "D factor". The model represents a general guideline for the optimization of nanocomposites with enhanced functionalities including sensing, heat management, and energy storage.

Original languageEnglish
Pages (from-to)9040-9050
Number of pages11
JournalACS Nano
Issue number9
Publication statusPublished - 25 Sept 2018


  • graphene
  • multifunctional materials
  • nanoclay
  • nanoparticle dispersion
  • polymer nanocomposites
  • predictive model


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