Abstract
The effects of bulk and surface chemical composition on the mechanical properties (MP) of eucalyptus kraft pulp were investigated using Liquid Chromatography (HPLC), Scanning Electron Microscopy (SEM), X-ray Photoelectron Spectroscopy (XPS) and Principal Component Analysis (PCA). SEM images of fractured regions during tear (TrID), tensile (TsID) and burst (BuID) tests of refined pulps showed fibre deformation and low proportion of broken fibres, indicating an inter-fibre failure in the sheet network. TrID of unrefined samples was affected by lignin surface coverage (SLig) while TsID and BuID were affected by surface coverage of extractives (SExt) and carbohydrates (SCar). After refining, TrID was affected by uronic acids (UA) in xylan and the influence of bulk composition was more pronounced. Our results also indicated that the fibre-to-fibre bonding rather than the individual fibre strength was the limit factor for MP. A strategy combining surface modification and preservation of polysaccharides in pulping was suggested to improve the MP of this material.
Highlights
Surface chemistry of pulp fibres has attracted remarkable research interest, especially in the latest years
Hemicelluloses and extractives were removed and macromolecular degradation of cellulose was observed according to intrinsic viscosity (IV) and estimation of Degree of polymerization (DP)
The cellulose/hemicellulose ratio (CHr) varied extensively due to removal of xylan
Summary
Surface chemistry of pulp fibres has attracted remarkable research interest, especially in the latest years. SEM was used to investigate the hand sheet fractured regions and PCA was applied in an exploratory data analysis using load plots containing surface composition from XPS, bulk chemical composition and mechanical properties. In the second matrix aiming at studying refined samples, SB was added to the bulk and surface chemical composition and mechanical properties forming a 4 x 16 data matrix. Both matrices were autoscaled, i.e., adjusted to zero mean by subtracting the original mean of each column and adjusted to unit variance by dividing each column by its standard deviation. The latter operations and PCA were performed using a computer program (MATLAB 6.0)
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