Sorghum as a novel biomass for the sustainable production of cellulose nanofibers

Pennells, Jordan; Cruickshank, Alan; Chaleat, Celine; Godwin, I. D.; Martin, Darren J.

doi:10.1016/j.indcrop.2021.113917

Cited by 30 publications

(23 citation statements)

References 83 publications

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“…Nanopaper was prepared from CNF pulp according to our previous work (Pennells et al 2021), as described in brief below. CNF pulp was dewatered and dried into a nanopaper handsheet using an automatic Rapid Ko ¨then handsheet former (Xell, Austria) according to the ISO 5269-2:2004 standard operating procedure (International Organization for Standardization 2001).…”

Section: Nanopaper Mechanical Propertiesmentioning

confidence: 99%

“…A hypothesis to explain this is the impact of non-fibre related factors on the strain at break value. A number of microfractures are expected to be imparted to the edges of some nanopaper strips during the sample cutting procedure, which could be a random process or associated with the biochemical composition and rigidity of the nanopaper handsheet itself (Pennells et al 2021). As such, the number and size of microfractures imparted to the nanopaper strip would disproportionately impact the strain at break result for the tested nanopaper strip.…”

Section: Influence Of Random Variationmentioning

confidence: 99%

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Assessing cellulose micro/nanofibre morphology using a high throughput fibre analysis device to predict nanopaper performance

et al. 2022

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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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Section: Nanopaper Mechanical Propertiesmentioning

confidence: 99%

Section: Influence Of Random Variationmentioning

confidence: 99%

Assessing cellulose micro/nanofibre morphology using a high throughput fibre analysis device to predict nanopaper performance

et al. 2022

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“…Sorghum morphology is that of a cane-like grass, comprising a slender to stocky stem ranging from 0.5 to 6 meters in height. The stem is either dry or contains sweet or insipid juice, with broad and coarse leaves distributed along the stem and connected via a leaf sheath that encircles the stem [91]. 9.…”

Section: Electrospinning Of Extracts and Essential Oils For Wound Hea...mentioning

confidence: 99%

Electro spun polymeric membranes for wound healing

Kadham

Braihi

Kadham

2022

ijhs

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Wound dressing materials which are capable of meeting the demands of accelerating wound closure and promoting wound healing process have been highly desired. Electro spun nanofbrous materials show great application potentials for wound healing owing to relatively large surface area, better mimicry of native extracellular matrix, adjustable waterproofness and breathability, and programmable drug delivery process.In this review article, the historical perspective of using electrospun polymeric materials for wound healing is outlined. This review includes, also, the electrospining parameters, wound measurement methods, phases of wound healing, characteristics of membrane required for wound healing and the required tests. The electrospun extract and the essential oils also included.

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“…A hypothesis to explain this is the impact of non-bre related factors on the strain at break value. A number of microfractures are expected to be imparted to the edges of some nanopaper strips during the sample cutting procedure, which could be a random process or associated with the biochemical composition and rigidity of the nanopaper handsheet itself (Pennells et al 2021). As such, the number and size of microfractures imparted to the nanopaper strip would disproportionately impact the strain at break result for the tested nanopaper strip.…”

Section: In Uence Of Random Variationmentioning

confidence: 99%

Assessing Cellulose Micro/Nanofibre Morphology Using a High Throughput Fibre Analysis Device to Predict Nanopaper Performance

Pennells¹,

Heuberger²,

Chaleat³

et al. 2021

Preprint

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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.

show abstract

Sorghum as a novel biomass for the sustainable production of cellulose nanofibers

Cited by 30 publications

References 83 publications

Assessing cellulose micro/nanofibre morphology using a high throughput fibre analysis device to predict nanopaper performance

Assessing cellulose micro/nanofibre morphology using a high throughput fibre analysis device to predict nanopaper performance

Electro spun polymeric membranes for wound healing

Assessing Cellulose Micro/Nanofibre Morphology Using a High Throughput Fibre Analysis Device to Predict Nanopaper Performance

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