| 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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Karakoç, Alp
Aalto University
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (18/18 displayed)
- 2024Design, Fabrication, and Characterization of 3D-Printed Multiphase Scaffolds Based on Triply Periodic Minimal Surfacescitations
- 2023Effects of leaflet curvature and thickness on the crimping stresses in transcatheter heart valvecitations
- 2023Low-cost thin film patch antennas and antenna arrays with various background wall materials for indoor wireless communicationscitations
- 2022Predicting the upper-bound of interlaminar impact damage in structural composites through a combined nanoindentation and computational mechanics techniquecitations
- 2022Simplified indentation mechanics to connect nanoindentation and low-energy impact of structural composites and polymers
- 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
- 2021Mild alkaline separation of fiber bundles from eucalyptus bark and their composites with cellulose acetate butyratecitations
- 2020Data-Driven Computational Homogenization Method Based on Euclidean Bipartite Matchingcitations
- 2020Mechanical and thermal behavior of natural fiber-polymer composites without compatibilizerscitations
- 2020A predictive failure framework for brittle porous materials via machine learning and geometric matching methodscitations
- 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
- 2018Stochastic fracture of additively manufactured porous compositescitations
- 2016Shape and cell wall slenderness effects on the stiffness of wood cell aggregates in the transverse planecitations
- 2016Modeling of wood-like cellular materials with a geometrical data extraction algorithmcitations
- 2013Effective stiffness and strength properties of cellular materials in the transverse planecitations
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>