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.
One of the key challenges for the industrial translation of cellulose nanofiber (CNF) materials is appropriate characterization and evaluation of product quality. Characterization of CNF properties is difficult because direct nanofiber assessment is largely unreliable and unscalable, while indirect characterization is often inaccurate and unable to be generalized across different biomass sources, processing routes, and final product or component formats. In addition, quality is an ambiguous term that is difficult to define, encompassing material performance, processing sustainability, and any aspects impacting economic viability of industrial production, dependent on the application in question. Using existing data on CNF produced from sorghum biomass, we explored the development of versatile statistical methodologies as a framework to investigate quality and sustainability, including: (a) a novel visualization tool for the evaluationof biomass processing sustainability (Processing Sustainability Triangle); (b) correlation analysis of biomass chemical composition with metrics relating to processing sustainability and nanopaper performance; and (c) an application-tunable Quality Ranking methodology based on a user-defined definition, as built through structural equation modeling. Versatility of the framework allows researchers and technologists to map the statistical methodology onto their experimental system of interest, enhancing data analysis through visualization. Ultimately,more sophisticated techniques for evaluation of product quality and processing sustainability will assist researchers to elucidate relationships in biomass-derived materialperformance and advance the industrial translation of CNF products.
Characterising cellulose nanofibre (CNF) morphology has been identified as a grand challenge for the nanocellulose research field. Direct techniques for CNF morphology characterisation exhibit various difficulties related to the material network structure and equipment cost, while indirect techniques that investigate fibre-light interaction, fibre-solvent interaction, fibre-fibre interaction, or specific fibre surface area involve relatively facile methods but may be more unreliable. Nanopaper mechanical testing is a prevalent metric for assessing fibre-fibre interaction, but is an off-line, time-consuming, and destructive methodology. In this study, an optical fibre morphology analyser (MorFi, Techpap) was employed as an on-line, high throughput, fast turnaround tool to assess micro/nanofibre pulp morphology and predict the properties of nanopaper material. Correlation analysis identified fibre content and fibre kink properties as most correlated with nanopaper strength and toughness, while fibre width and coarseness were most inversely correlated with nanopaper performance. Principal component analysis (PCA) was employed to visualise interdependent morphological and mechanical data. Subsequently, two data driven statistical models—multiple linear regression (MLR) and machine learning based support vector regression (SVR)—were established to predict nanopaper properties from fibre morphology data, with SVR generating a more accurate prediction across all nanopaper properties (NRMSE = 0.13–0.33) compared to the MLR model (NRMSE = 0.33–0.51). This study highlights that statistical methods are useful to disentangle and visualise interdependent morphological data from an on-line fibre analysis device, while regression models are also capable of predicting paper mechanical properties from CNF samples even though these devices do not operate at nanoscale resolution. Graphical abstract
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