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Machine Learning assisted design of tailor‐made nanocellulose films: A combination of experimental and computational studies
Author(s) -
Özkan Merve,
Karakoç Alp,
Borghei Maryam,
Wiklund Jenny,
Rojas Orlando J.,
Paltakari Jouni
Publication year - 2019
Publication title -
polymer composites
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.577
H-Index - 82
eISSN - 1548-0569
pISSN - 0272-8397
DOI - 10.1002/pc.25262
Subject(s) - nanocellulose , materials science , nanocomposite , ultimate tensile strength , composite material , polyvinyl alcohol , cellulose , chemical engineering , engineering
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., 40:4013–4022, 2019. © 2019 Society of Plastics Engineers