| People | Locations | Statistics |
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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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Paltakari, Jouni
Aalto University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (10/10 displayed)
- 2021Effect of single-fiber properties and fiber volume fraction on the mechanical properties of Ioncell fiber compositescitations
- 2021Exploring the possibilities of FDM filaments comprising natural fiber-reinforced biocomposites for additive manufacturingcitations
- 2020Data-Driven Computational Homogenization Method Based on Euclidean Bipartite Matchingcitations
- 2020Mechanical and thermal behavior of natural fiber-polymer composites without compatibilizerscitations
- 2020Comparative screening of the structural and thermomechanical properties of FDM filaments comprising thermoplastics loaded with cellulose, carbon and glass fiberscitations
- 2020Comparative screening of the structural and thermomechanical properties of FDM filaments comprising thermoplastics loaded with cellulose, carbon and glass fiberscitations
- 2019Machine Learning assisted design of tailor-made nanocellulose filmscitations
- 2016Modeling of wood-like cellular materials with a geometrical data extraction algorithmcitations
- 2013The influence of shear on the dewatering of high consistency nanofibrillated cellulose furnishescitations
- 2012Interactions between inorganic nanoparticles and cellulose nanofibrilscitations
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>