Randomized trial reveals how processing conditions tailor properties of lignocellulosic nanofibrils, indicating a scalable engineering approach.
Lignocellulosic nanofibrils (LCNFs) are promising sustainable biomaterials for optical, structural and barrier uses, but most production methods remain costly and unsustainable, and few studies have explored how processing conditions can tailor their properties. In this work, lignocellulosic nanofibers were produced from wheat straw under systematically controlled conditions using alkaline peroxide fractionation followed by peracetic acid oxidation, a process that has shown strong potential for commercial application. By varying temperature and chemical charge, LCNFs with a wide range of physical and chemical properties were obtained. More mild fractionation conditions, characterized by lower temperature and reduced chemical charge, promoted greater retention of hemicellulose and lignin and produced nanofibrils with distinct features, including wider fiber distributions, lower UV transmittance, higher haze, moderate thermal and mechanical properties, and decreased surface wettability. The residual lignin content had a decisive influence on material performance: higher lignin levels led to less uniform, more hydrophobic fibrils with reduced strength, transparency and thermal stability. Importantly, the stage at which lignin was removed, either during fractionation or oxidation, had a significant impact on the final properties, even when the overall lignin content remained similar. When the peracetic acid charge increased from 7.5% to 30%, the charge demand increased whereas the ultimate tensile strength decreased significantly. To our knowledge, this is the first systematic demonstration that the delignification sequence and chemical conditions, rather than overall lignin removal extent, are key determinants of LCNF characteristics. These results demonstrate that controlled adjustment of fractionation and oxidation parameters offers a flexible and scalable strategy for engineering LCNFs with tailored, application-specific properties.
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Sidhu et al. (2026) studied this question.
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