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Cellulose

, Volume 13, Issue 2, pp 157–170 | Cite as

Determination of Surface Functional Groups in Lignocellulosic Materials by Chemical Derivatization and ESCA Analysis

  • M. Östenson
  • H. Järund
  • G. Toriz
  • P. Gatenholm
Article

Abstract

Simple and convenient methods for determining surface chemical composition of lignocellulosic materials are described. The methods are based on vapor phase fluorine surface derivatization with either trifluoro acetic anhydride (TFAA), tri-fluoro ethanol (TFE) or pentafluorophenyl hydrazine (PFPH) and subsequent Electron Spectroscopy for Chemical Analysis (ESCA). Model cellulosic surfaces with well defined functionalities were used to optimize the derivatization reaction conditions. Detection and accessibility of surface hydroxyl functional groups were investigated in cotton and regenerated cellulose as models. Carboxymethyl cellulose (CMC) was used as a model surface for detection and quantification of carboxylic acid groups. Theoretical conversion curves for derivatization reactions were calculated and used to evaluate the extent of the reactions on the model surfaces. It was found that the conversion was higher for the regenerated cellulose and CMC than for cotton. The protocols developed using the model surfaces were applied to a case study on wood fibers with different degrees of complexity, namely dissolving and chemithermomechanical (CTMP) pulp. Untreated and oxygen-plasma modified pulps were compared with respect to the surface composition of functional groups. According to the derivatization reactions, functionalities containing oxygen were significantly increased on the plasma-treated samples. The effect of the treatment was found to be dependent on the type of pulp. Fluorine derivatization is shown to be an unambiguous method for clear assessment of the chemical functionalities of cellulosic surfaces.

Keywords

Lignin Regenerate Cellulose Chemical Derivatization Oxygen Plasma Treatment TFAA 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2006

Authors and Affiliations

  • M. Östenson
    • 1
  • H. Järund
    • 1
  • G. Toriz
    • 1
    • 2
  • P. Gatenholm
    • 1
  1. 1.Biopolymer Technology, Department of Materials and Surface ChemistryChalmers University of TechnologyGöteborgSweden
  2. 2.Department of Wood, Cellulose, and Paper ResearchUniversity of GuadalajaraGuadalajaraMexico

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