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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Borghei, Maryam
Aalto University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (16/16 displayed)
- 2024Wood flour and Kraft lignin enable air-drying of the nanocellulose-based 3D-printed structurescitations
- 2023Immobilized cellulose nanospheres enable rapid antigen detection in lateral flow immunoassayscitations
- 2021Systematic analysis on the effect of sintering temperature for optimized performance of li0.15ni0.45zn0.4o2-gd0.2ce0.8o2-li2co3-na2co3-k2co3 based 3d printed single-layer ceramic fuel cellcitations
- 2020Mesoporous Carbon Microfibers for Electroactive Materials Derived from Lignocellulose Nanofibrilscitations
- 2019Solvent Welding and Imprinting Cellulose Nanofiber Films Using Ionic Liquidscitations
- 2019Nanocellulose and Nanochitin Cryogels Improve the Efficiency of Dye Solar Cellscitations
- 2019Nanocellulose and Nanochitin Cryogels Improve the Efficiency of Dye Solar Cellscitations
- 2019Coupling Nanofibril Lateral Size and Residual Lignin to Tailor the Properties of Lignocellulose Filmscitations
- 2019Conductive Carbon Microfibers Derived from Wet-Spun Lignin/Nanocellulose Hydrogelscitations
- 2019Machine Learning assisted design of tailor-made nanocellulose filmscitations
- 2018Biobased aerogels with different surface charge as electrolyte carrier membranes in quantum dot-sensitized solar cellcitations
- 2018Experimental and Computational Investigation of Hydrogen Evolution Reaction Mechanism on Nitrogen Functionalized Carbon Nanotubescitations
- 2016Mesoporous carbon soft-templated from lignin nanofiber networks: Microphase separation boosts supercapacitance in conductive electrodescitations
- 2014Influence of different carbon nanostructures on the electrocatalytic activity and stability of Pt supported electrocatalystscitations
- 2014Influence of different carbon nanostructures on the electrocatalytic activity and stability of Pt supported electrocatalystscitations
- 2013Durability of Carbon Nanofiber (CNF) & Carbon Nanotube (CNT) as Catalyst Support for Proton Exchange Membrane Fuel Cellscitations
Places of action
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article
Machine Learning assisted design of tailor-made nanocellulose films
Abstract
<p>Nowadays, modern nanomaterial research is complemented by machine learning methods to reduce experimental costs and process time. With this motivation, here, we implemented artificial neural network (ANN), random forest (RF), and multiple linear regression (MLR) methods to predict the mechanical properties of three-component nanocomposite films consisting of polyvinyl alcohol (PVA) crosslinked 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) oxidized cellulose nanofibers (TOCNFs) and either ammonium zirconium carbonate (AZC) or glyoxal (Gx) using the mechanical properties of mono-component TOCNF films and two-component nanocomposites containing PVA, AZC, or Gx-crosslinked TOCNF as the input of prediction system. Prediction methods were evaluated with performance indicators and experimental data. Overall, MLR performed with least accuracy, whereas ANN prediction displayed the lowest error followed closely by RF. Additionally, the physically or/and chemically crosslinked hybrid films with optimized amount of crosslinkers resulted in structures with a strength to rupture that was significantly higher than that of the pure nanocellulose films (increases of up to ~90% in tensile strength and ~70% in Young's modulus). POLYM. COMPOS., 2019.</p>