Abstract
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
Highlights
Characterising cellulose nanofibre (CNF) morphology has been identified as a grand challenge for nanocellulose research (Moon et al 2011)
While the integration of fibre analysis tools with pulp and paper production would greatly benefit CNF commercialisation, it has only been addressed in a handful of previous studies (Pande and Roy 1998; Oluwafemi and Sotannde 2007; Lin et al 2014; Nasser et al 2015; Aguado et al 2016). To address this gap in the literature, this study investigates the relationships between fibre morphology and nanopaper properties for a broad sample population consisting of different varieties and sections of sorghum biomass–a globally important agricultural crop (Borrell et al 2021) – processed under 3 different energy levels
Relationships of interest included the positive correlation between fibre width, fibre coarseness, fibre curl index, macrofibrillation index (MF.index), and broken fibre content, the inverse correlation between these parameters and fibre content, and the inverse correlation between kinked fibre content and mean fine area and length
Summary
Characterising CNF morphology has been identified as a grand challenge for nanocellulose research (Moon et al 2011). Fibre morphology encompasses the average fibre dimensions (length, width), the relative size distribution of fibre dimensions throughout the sample, fibre aspect ratio, fibre surface area, the degree of fibrillation and branching, fibre hydrodynamic volume (rigidity), and fibre shape (kink, curl, curvature). Nanofibre morphology influences the effective nanofibre surface area, fibre-fibre and fibre-solvent interaction, net surface charge, gel point or networking concentration, and the product quality in terms of its mechanical properties, rheological and colloidal behaviour, hydrophilicity, optical properties, electrical conductivity, film permeability, and material reactivity (Li et al 2021). Fibre morphology has a well-established impact on the mechanical properties of paper, such that fibre length strongly affects paper strength, whereas fibre width decreases fibre flexibility and conformability, which has a negative impact on paper strength (Seth 1995; Larsson et al 2018). Fine content improves the strength, smoothness, and optical properties of the final paper (Moral et al 2010; Motamedian et al 2019), while decreasing the freeness of the pulp (Dienes et al 2005)
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