Research Data Leeds Repository

Citation

Victoria, Ashley and Hine, Peter J. and Ward, Keeran and Ries, Michael (2023) Data for Design of experiments in the optimization of all-cellulose composites. University of Leeds. [Dataset] https://doi.org/10.5518/1298

Dataset description

In this work, statistical design of experiments (DoE) was applied to the optimization of all cellulose composites (ACCs) using cotton textile and interleaf films under applied heat and pressure. The effects of dissolution temperature, pressure and time on ACC mechanical properties were explored through a full factorial design (23) and later optimized using Response Surface Methodology (RSM). It was found that the experimental design was effective at revealing the underlying relationship between Young’s modulus and processing conditions, identifying optimum temperature and time settings of 101 °C and 96.8 minutes respectively, to yield a predicted Young’s modulus of 3.3 GPa. This was subsequently validated through the preparation of in-lab test samples which were found to exhibit a very similar Young’s modulus of 3.4 ± 0.2 GPa, confirming the adequacy of the predictive model. Additionally, the optimized samples had an average tensile strength and peel strength of 72 ± 2 MPa and 811 ± 160 N/m respectively, as well as a favorable density resulting from excellent consolidation within the material microstructure. This work highlights the potential of DoE for future ACC process understanding and optimization, helping to bring ACCs to the marketplace as feasible material alternatives.

Keywords:	All cellulose composite, Cellulose textile, Design of experiment, Partial dissolution, Ionic liquid
Subjects:	F000 - Physical sciences > F200 - Materials science
Divisions:	Faculty of Engineering and Physical Sciences > School of Physics and Astronomy
Related resources:	Location Type https://doi.org/10.1007/s10570-023-05535-8 Publication https://eprints.whiterose.ac.uk/204681/ Publication
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	03 Oct 2023 11:41
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/1161
Additional details Additional details Creators or authors: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Victoria, Ashley https://orcid.org/0000-0002-9848-5493 University of Leeds https://ror.org/024mrxd33 ror Hine, Peter J. University of Leeds https://ror.org/024mrxd33 ror Ward, Keeran https://orcid.org/0000-0001-7119-4138 University of Leeds https://ror.org/024mrxd33 ror Ries, Michael https://orcid.org/0000-0002-8050-3200 University of Leeds https://ror.org/024mrxd33 ror Type of data: Dataset Research funders: EPSRC Grant numbers: EP/SO22473/1 Publication date: 2023 Resource language: English Metadata language: English Publisher: University of Leeds Contact email: m.e.ries@leeds.ac.uk Last Modified: 20 May 2024 10:16

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Supporting_Dataset.xlsx [50kB] Supporting_Dataset.xlsx [50kB] Supporting_Dataset.xlsx [50kB] Security: Anyone File name: Supporting_Dataset.xlsx Content type: Data Mime-Type: application/vnd.openxmlformats-officedocument.spreadsheetml.sheet License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 50kB