New insights into X-ray scattering data from wood and cellulose through modeling and statistical analysis

Paavo Penttilä (Corresponding author), Aleksi Zitting, Patrik Ahvenainen, Enriqueta Noriega Benitez, Antti Paajanen

Research output: Contribution to conferenceConference AbstractScientific


X-ray scattering provides powerful tools to study the nanoscale structure of cellulosic materials. Small-angle X-ray scattering (SAXS) is sensitive to the density contrast between crystalline cellulose microfibrils and the less-ordered matrix around them, giving information on the cross-sectional size and shape of the microfibrils and their packing distance. Wide-angle X-ray scattering, on the other hand, can be used to analyze the crystalline portion of the microfibrils, that is, to determine lattice spacings and coherence length of the crystals (crystal size). However, multiple aspects regarding the information that can be extracted with these methods from experimental data of real cellulosic samples have remained poorly defined. In this contribution, we address the resolvability of X-ray scattering methods in the characterization of cellulosic materials, especially in terms of the accuracy of parameters determined from scattering data by fitting and the variation of such parameters in heterogeneous wood samples. We discuss for instance factors affecting the size of cellulose crystallites as determined from diffraction peak broadening in WAXS data (Scherrer equation) or by simplified analytical models fitted to SAXS data, based on scattering intensities calculated from molecular models of microfibrils. These factors include the shape of the microfibril cross-section and the presence of hemicelluloses at the crystallite surfaces, as well as the effects of moisture. We also present a statistical analysis of WAXS and SAXS results obtained from a large set of experimental data from wood samples, which helps us to study the natural variation of nanostructural parameters across different wood species and tissue types. This investigation will serve as a basis for future implementations of machine learning strategies in data-driven analysis of scattering data from cellulosic samples.
Original languageEnglish
Publication statusPublished - 2023
MoE publication typeNot Eligible
EventACS Spring 2023 National Meeting & Exhibition - Indianapolis, United States
Duration: 26 Mar 202330 Mar 2023


ConferenceACS Spring 2023 National Meeting & Exhibition
Country/TerritoryUnited States


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