journal article·2013-08

All-cellulose multilayers: long nanofibrils assembled with short nanocrystals

Anna Maria Olszewska, Eero Kontturi, Janne Laine, Monika Österberg

Cellulose· Volume 20· Issue 4
Publisher page

Abstract

Self-organized multilayer films were formed by sequential addition of oppositely charged cellulose I nanoparticles. The all-cellulosic multilayers were prepared via adsorption of cationicially modified cellulose nanofibrils (cat NFC) and anionic short crystalline cellulose (CNC) at pH 4.5 and pH 8.3. The properties and build-up behavior of layer-by-layer-constructed films were studied with microgravimetry (QCM-D) and the direct surface forces in these systems were explored with colloidal probe microscopy to gain information about the fundamental interplay between cat NFC and anionic CNC. The importance of the first layer on the adsorption of the consecutive layers was demonstrated by comparing pure in situ adsorption in the QCM-D with multilayer films made by spin coating the first cationic NFC layer and then subsequently adsorbing the following layers in situ in the QCM-D chamber. Differences in adsorbed amount and viscoelastic behavior were observed between those two systems. In addition, a significant pH dependence of cat NFC charge was found for both direct surface interactions and layer properties. Moreover the underlying cellulose layer in multilayer film was established to influence the surface forces especially at lower pH, where the cat NFC chains extensions were facilitated and overall charge was affected by the cationic counterpart within the layers. This enhanced understanding the effect of charge and structure on the interaction between these renewable nanoparticles is valuable when designing novel materials based on nanocellulose.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced Cellulose Research Studies · Polysaccharides and Plant Cell Walls · Nanocomposite Films for Food Packaging

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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