Bulk characterization of highly structured tissue paper based on 2D and 3D evaluation methods

July 19, 2023

Jurgen Reitbauer (1), Eduardo Machado Charry (2), Rene Eckhart (1), Cemile Sozeri (1), Wolfgang Bauer (1)
Cellulose. (19 July 2023). DOI: https://doi.org/10.1007/s10570-023-05314-5


𝜇, μ-CT, microtome, 3D tissue bulk, intrinsic properties, fibre network structure


The structure of the fibre network in tissue paper can be complex and difficult to analyze, due to the presence of superimposed structures such as creping and patterns that occur, for example, in through-air-dried (TAD) tissue. Properties like high absorbency, a pleasant handfeel, and strength-related characteristics are closely related to the fibre network structure. Therefore, in addition to standard tissue testing methods, techniques that provide insights into the intrinsic properties of the tissue fibre network are essential for a deeper understanding and potential for further optimization. In this study, we utilized 2D cross-sectional images and 3D X-ray microtomography (𝜇μ-CT) to evaluate and quantify the intrinsic properties of highly structured TAD tissue. We compared the results obtained from these two methods, focusing on intrinsic thickness, porosity, and the fibre volume to fibre surface area (Fv/Fs) ratio. The open structure of the fibre network, fabric patterns, creping, and protruding fibres make it challenging to define bulk boundaries. Therefore, we examined the effect of different bulk expansion diameters on intrinsic properties. This procedure allows to quantify the effects of under- and overestimation of bulk boundaries, and to determine which regions within the fibre network are affected by bulk expansion. In terms of intrinsic thickness, both 2D and 3D evaluations show similar trends, which facilitates direct comparison of 2D and 3D data. Porosity, on the other hand, does not show any correlation between 2D and 3D-based data. Together with the Fv/Fs parameter, this leads to the conclusion that the depiction of 2D data does not represent the whole fibrous material but predominantly fibres perpendicular or close to perpendicular to the cut plane, whereas 3D data represents all fibres, fibre bonds and network connectivity. This work aims at presenting modern approaches and novel procedures to quantify intrinsic properties of open fibre structures such as tissue, but could also be applied to fibrous networks in general. The introduced methods could provide the basis for future research on the interrelations between intrinsic properties and key tissue properties such as absorbency and handfeel.

How Our Software Was Used

Dragonfly’s deep learning segmentation approach was used to separate the raw data into the available fractions of fibres, air, and Kapton. After inference with the trained deep model, the fibre network of all four datasets is further optimized by removing outlier data and single fibres which are not connected to the main fibre network.

Author Affiliation

(1) Institute of Bioproducts and Paper Technology, Graz University of Technology, Inffeldgasse 23, 8010, Graz, Austria
(2) Institute of Solid State Physics, Graz University of Technology, Petersgasse 16/II, 8010, Graz, Austria