Combining tensile test results with atomistic predictions of elastic modulus of graphene/polyamide-6,6 nanocomposites

Name: Combining tensile test results with atomistic predictions of elastic modulus of graphene/polyamide-6,6 nanocomposites
Author: Batyrov, Merdan, Dericiler, K., Palabıyık, Büşra Akkoca, Okan, B. S., Öztürk, Hande, Fındıkçı, İlknur Eruçar

İsim	Combining tensile test results with atomistic predictions of elastic modulus of graphene/polyamide-6,6 nanocomposites
Yazar	Batyrov, Merdan, Dericiler, K., Palabıyık, Büşra Akkoca, Okan, B. S., Öztürk, Hande, Fındıkçı, İlknur Eruçar
Basım Tarihi:	2023-06
Basım Yeri	- Elsevier
Konu	6, Elastic modulus, Graphene, MD simulation, Polyamide-6, Polymer nanocomposite
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2352-4928
Kayıt Numarası	2c33f74a-6a4c-4f47-bf2a-2c8a85c24660
Lokasyon	Natural and Mathematical Sciences
Tarih	2023-06
Notlar	High-Performance Computing Laboratory of Ozyegin University ; TÜBİTAK
Örnek Metin	In this work, we combined tensile test results with atomistic simulations to investigate the effect of filler parameters including distribution, stacking, loading and lateral graphene size on elastic moduli of graphene/PA-6,6 nanocomposites. Stacked and randomly distributed atomistic models were adapted in Molecular Dynamics (MD) simulations to establish the limits of stiffness enhancement in graphene reinforced PA-6,6 nanocomposites with loading ratios changing from 0 to 1 wt%. Experimental results showed that incorporating of 0.3–0.4 wt% graphene loading improved the elastic modulus of the neat polymer by 41.7%−43.5%. While the test sample behaved close to the computational results of the stacked atomistic model at low graphene loadings up to 0.4 wt%, it overshot the predictions of the randomly distributed model at all considered loadings up to 1 wt%. Elastic moduli of graphene-based PA-6,6 nanocomposites increased linearly with graphene loading in the stacked model, however, no such relation was detected in the randomly distributed model. The lower stiffness enhancement provided by the randomly distributed model compared to the stacked model was revealed as the small lateral size of graphene plates in PA-6,6 matrix. As the graphene size increased, the elastic modulus of the graphene dramatically increased, directly improving the elastic modulus of the nanocomposite. The developed computational approach is highly useful to estimate the boundaries of stiffness enhancement provided by graphene dispersions in macroscale nanocomposite samples.
DOI	10.1016/j.mtcomm.2023.105636
Cilt	35

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Combining tensile test results with atomistic predictions of elastic modulus of graphene/polyamide-6,6 nanocomposites

Yazar Batyrov, Merdan, Dericiler, K., Palabıyık, Büşra Akkoca, Okan, B. S., Öztürk, Hande, Fındıkçı, İlknur Eruçar

Basım Tarihi 2023-06

Basım Yeri - Elsevier

Konu 6, Elastic modulus, Graphene, MD simulation, Polyamide-6, Polymer nanocomposite

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2352-4928

Kayıt Numarası 2c33f74a-6a4c-4f47-bf2a-2c8a85c24660

Lokasyon Natural and Mathematical Sciences

Tarih 2023-06

Notlar High-Performance Computing Laboratory of Ozyegin University ; TÜBİTAK

Örnek Metin In this work, we combined tensile test results with atomistic simulations to investigate the effect of filler parameters including distribution, stacking, loading and lateral graphene size on elastic moduli of graphene/PA-6,6 nanocomposites. Stacked and randomly distributed atomistic models were adapted in Molecular Dynamics (MD) simulations to establish the limits of stiffness enhancement in graphene reinforced PA-6,6 nanocomposites with loading ratios changing from 0 to 1 wt%. Experimental results showed that incorporating of 0.3–0.4 wt% graphene loading improved the elastic modulus of the neat polymer by 41.7%−43.5%. While the test sample behaved close to the computational results of the stacked atomistic model at low graphene loadings up to 0.4 wt%, it overshot the predictions of the randomly distributed model at all considered loadings up to 1 wt%. Elastic moduli of graphene-based PA-6,6 nanocomposites increased linearly with graphene loading in the stacked model, however, no such relation was detected in the randomly distributed model. The lower stiffness enhancement provided by the randomly distributed model compared to the stacked model was revealed as the small lateral size of graphene plates in PA-6,6 matrix. As the graphene size increased, the elastic modulus of the graphene dramatically increased, directly improving the elastic modulus of the nanocomposite. The developed computational approach is highly useful to estimate the boundaries of stiffness enhancement provided by graphene dispersions in macroscale nanocomposite samples.

DOI 10.1016/j.mtcomm.2023.105636

Cilt 35