Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning

Özkan, Merve; Borghei, Maryam; Karakoç, Alp; Rojas, Orlando J.; Paltakari, Jouni

doi:10.1038/s41598-018-23114-x

Cited by 26 publications

(17 citation statements)

References 27 publications

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“…Reported studies on cellulose nanofibrils (CNF), derived from different cellulose sources including bleached wood fiber, cotton, and agricultural residues show a yet untapped opportunity for uses in diverse applications such as in reinforcement of nanocomposites with thermoplastic and thermoset polymers, membranes, coatings, packaging, and film materials . Large scale production of CNF is possible by the combined effects of chemical, enzymatic, and mechanical treatments such as grinding, high‐pressure homogenization, and microfluidization …”

Section: Introductionmentioning

confidence: 99%

Coupling Nanofibril Lateral Size and Residual Lignin to Tailor the Properties of Lignocellulose Films

Imani

Ghasemian

Dehghani-Firouzabadi

et al. 2019

Adv Materials Inter

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Lignocellulosic nanofibrils (LCNF) are produced from a single source of unbleached, oxidized wood fibers by serial disintegration, high‐pressure microfluidization, and homogenization. Sequential centrifugation enables fractionation by fibril width (≈5, ≈9, and ≈18 nm). LCNF residual lignin of high molecular mass reports together with the finest fraction (LCNF‐fine), whereas the more strongly cellulose‐bound lignin, of relatively lower molecular mass, associates with the coarsest fraction (LCNF‐coarse). Hot pressing softens the amorphous lignin, which fills the interstices between fibrils and acts as an in‐built interfacial cross‐linker. Thus, going from the LCNF‐fine to the LCNF‐course films, it is possible to obtain a range of values for the structural consolidation (density from 0.9 to 1.2 g cm−3 and porosity from 19% to 40%), surface roughness (RMS from ≈6 to 13 nm), and strength (elastic modulus from 8 to ≈12 GPa). The concentration of free hydroxyl groups controls effectively the direct surface interactions with liquids. The apparent surface energy dispersive component tracks with the total surface free energy and appears to be strongly influenced by the higher porosity as the fibril lateral size increases. The results demonstrate the possibility to tailor nanofibril cross‐linking and associated optical and thermo‐mechanical performance of LCNF films.

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Section: Introductionmentioning

confidence: 99%

Coupling Nanofibril Lateral Size and Residual Lignin to Tailor the Properties of Lignocellulose Films

Imani

Ghasemian

Dehghani-Firouzabadi

et al. 2019

Adv Materials Inter

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“…In addition, there are also reports of simulations on mixed materials of NC and CNTs [136,137]. In recent years, as a result of remarkable improvements in computer performance, many researches on NC or CNTs using machine learning have been reported [138][139][140][141][142][143][144]. In the future, research using these machine learning methods will accelerate the development of further mixed materials of cellulose and CNTs research, such as by reducing the time and cost of experiments.…”

Section: Prospects For Mixed Materialsmentioning

confidence: 99%

A Review of Applications Using Mixed Materials of Cellulose, Nanocellulose and Carbon Nanotubes

2020

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Carbon nanotubes (CNTs) have been extensively studied as one of the most interesting nanomaterials for over 25 years because they exhibit excellent mechanical, electrical, thermal, optical, and electrical properties. In the past decade, the number of publications and patents on cellulose and nanocellulose (NC) increased tenfold. Research on NC with excellent mechanical properties, flexibility, and transparency is accelerating due to the growing environmental problems surrounding us such as CO2 emissions, the accumulation of large amounts of plastic, and the depletion of energy resources such as oil. Research on mixed materials of cellulose, NC, and CNTs has been expanding because these materials exhibit various characteristics that can be controlled by varying the combination of cellulose, NC to CNTs while also being biodegradable and recyclable. An understanding of these mixed materials is required because these characteristics are diverse and are expected to solve various environmental problems. Thus far, many review papers on cellulose, NC or CNTs have been published. Although guidance for the suitable application of these mixed materials is necessary, there are few reviews summarizing them. Therefore, this review introduces the application and feature on mixed materials of cellulose, NC and CNTs.

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“…Recently, we reported the transparency and surface properties of films composed of TOCNFs, PVA, Gx, and AZC, and additionally, integrated a machine learning method (RF) to predict the possible properties from different component combinations . Interestingly, all the crosslinkers increased the WCA and decreased the surface roughness of the films while slightly <90% transmission of the incident light was observed in all samples according to the UV–Vis spectroscopy analyses.…”

Section: Introductionmentioning

confidence: 98%

“…Alternatively, in Ref. , glyoxal (Gx)‐crosslinked TOCNF substrates were studied in terms of their transparency, surface roughness, and water contact angle (WCA). Interestingly, Gx‐crosslinked TOCNF substrates exhibited remarkable increase in WCA and surface smoothness.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning assisted design of tailor‐made nanocellulose films: A combination of experimental and computational studies

et al. 2019

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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 threecomponent 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. COM-POS., 40:4013-4022, 2019.

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Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning

Cited by 26 publications

References 27 publications

Coupling Nanofibril Lateral Size and Residual Lignin to Tailor the Properties of Lignocellulose Films

Coupling Nanofibril Lateral Size and Residual Lignin to Tailor the Properties of Lignocellulose Films

A Review of Applications Using Mixed Materials of Cellulose, Nanocellulose and Carbon Nanotubes

Machine Learning assisted design of tailor‐made nanocellulose films: A combination of experimental and computational studies

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