RESUMO
With the rise of biomass-based materials such as nanocellulose, there is a growing need to develop statistical methods capable of leveraging inter-dependent experimental data to improve material design, product development, and process optimisation. Statistical approaches are essential given the multifaceted nature of variability in lignocellulosic biomass, which includes a range of different biomass feedstock types, a combinative arrangement of different biomass processing routes, and an array of different product formats depending on the focal application. To account for this large degree of variability and to extract meaningful patterns from research studies, there is a requirement to generate larger datasets of biomass-derived material properties through well-designed experimental systems that enable statistical analysis. To drive this trend, this article proposes the cross-disciplinary utilisation of statistical modelling approaches commonly applied within the field of statistical genetics to evaluate data generated in the field of biomass-based material research and development. The concepts of variance partitioning, heritability, hierarchical clustering, and selection gradients have been explained in their native context of statistical genetics and subsequently applied across the disciplinary boundary to evaluate relationships within a model experimental study involving the production of sorghum-derived cellulose nanofibres and their subsequent fabrication into nanopaper material. Variance partitioning and heritability calculates the relative influence of biomass vs. processing factors on material performance, while hierarchical clustering highlights the obscured similarity between experimental samples or characterisation metrics, and selection gradients elucidate the relationships between characterisation metrics and material quality. Ultimately, these statistical modelling approaches provide more depth to the investigation of biomass-processing-structure-property-performance relationships through outlining a framework for product characterisation, quality evaluation, and data visualisation, not only applicable to nanocellulose production but for all biomass-based materials and products.
RESUMO
The isolation of nanocellulose from lignocellulosic biomass, with desirable surface chemistry and morphology, has gained extensive scientific attention for various applications including polymer nanocomposite reinforcement. Additionally, environmental and economic concerns have driven researchers to explore viable alternatives to current isolation approaches, employing chemicals with reduced environmental impact. To address these issues, in this study, we have tuned the amphiphilic behavior of cellulose nanofibers (CNFs) by employing controlled alkali treatment, instead of in combination with expensive, environmentally unsustainable conventional approaches. Microscopic and spectroscopic analysis demonstrated that this approach is capable of tuning composition and interfacial tension of CNFs through a careful control of the quantity of residual lignin and hemicellulose. To elucidate the performance of CNF as an efficient reinforcing nanofiller in hydrophobic polymer matrices, prevulcanized natural rubber (NR) latex was employed as a suitable host polymer. CNF/NR nanocomposites with different CNF loading levels (0.1-1 wt % CNF) were prepared by a casting method. It was found that the incorporation of 0.1 wt % CNF treated with a 0.5 w/v % sodium hydroxide solution led to the highest latex reinforcement efficiency, with an enhancement in tensile stress and toughness of 16% to 42 MPa and 9% to 197 MJ m-3, respectively. This property profile offers a potential application for the high-performance medical devices such as condoms and gloves.