English

3D Imaging of directional multi-scale cellulose nanostructures through multi-directional dark-field neutron tomography

Mesoscale and Nanoscale Physics 2024-07-10 v1 Materials Science Soft Condensed Matter

Abstract

Hierarchical biomaterials embody nature's intricate design principles, offering advanced functionalities through the complex, multi-level organization of their molecular and nanosized building blocks. However, the comprehensive characterization of their 3D structure remains a challenge, particularly due to radiation damage caused by conventional X-ray- and electron-based imaging techniques, as well as due to the length scale limitations of scattering-based investigation methods. Here, we present a study utilizing multi-directional dark-field neutron imaging in tomographic mode to visualize the 3D nanoarchitecture of nanocellulose solid foams, a class of sustainable materials possessing complex and highly tunable hierarchical structures. By exploiting the unique properties of neutrons as a probe, this non-destructive method circumvents the inherent limitations of damage-inducing ionizing radiation, preserving the structural and chemical integrity of the biomaterials, and allowing for truly multiscale characterization of the spatial orientation and distribution of cellulose nano fibrils within large-volume samples. In particular, the study showcases the 3-dimensional anisotropic orientation and degree of alignment of nanofibrils with different crystallinity, across various length scales, from nanometers to centimeters. This approach offers a valuable and generally applicable tool for multi-scale characterisation of biobased materials where complex nanoscale arrangements inform macroscopic properties.

Keywords

Cite

@article{arxiv.2407.06728,
  title  = {3D Imaging of directional multi-scale cellulose nanostructures through multi-directional dark-field neutron tomography},
  author = {Matteo Busi and Elisabetta Nocerino and Agnes Åhl and Lennart Bergström and Markus Strobl},
  journal= {arXiv preprint arXiv:2407.06728},
  year   = {2024}
}