Production and investigation of mechanical properties of graphene/polystyrene nano composites

Raza, Yasir; Raza, Hassan; Ahmad, Arslan; Quazi, M. M.; Abid, Мuhammad; Kazmi, Monis; Rahman, Md. Saifur; Zulfattah, Z. M.; Fattah, I.M. Rizwanul

doi:10.1007/s10965-021-02560-8

Cited by 14 publications

(7 citation statements)

References 34 publications

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“…The target network was used to predict the mechanical properties of Gr/Epoxy nanocomposite. The dataset of the source DNN consists of 136 samples (gathered from literature, [30][31][32][33][34][35][36][37][38][39][40][41] ) which were randomly partitioned into three subgroups; 70% of the dataset was used for training, 15% for validation and 15% for testing. Due to the different scale values of the inputs, they were first normalized between −1 and + 1, followed by cleaning and removing the data with constant values.…”

Section: Resultsmentioning

confidence: 99%

Mechanical properties prediction of various graphene reinforced nanocomposites using transfer learning-based deep neural network

Pashmforoush

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Nowadays, various machine learning (ML) approaches are widely used in different research areas. However, the need for a large training dataset has restricted the attractiveness of ML techniques for industrial applications, since the preparation of a large dataset is very costly and inefficient. To deal with this limitation, an efficient method is required to fill the gap between industry and research. For this purpose, in this study a transfer learning-based deep neural network (TL-DNN) model was developed to predict the mechanical properties of various graphene reinforced nanocomposites. In this respect, a hybrid multi-layer feedforward DNN was designed, containing one source network and one target network. The source DNN was trained to predict the mechanical properties of graphene/graphene oxide nanocomposites with various matrix types including Al, Cu, PMMA, Si3N4, Al2O3, etc. By transferring the acquired knowledge of the source DNN to the target DNN, the mechanical properties of another material (graphene/epoxy nanocomposite) were estimated with high accuracy level, even with limited number of data samples. It should be mentioned that the optimal values of the network hyperparameters were determined using genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO) algorithms.

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Section: Resultsmentioning

confidence: 99%

Mechanical properties prediction of various graphene reinforced nanocomposites using transfer learning-based deep neural network

Pashmforoush

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…The π-π stacking between the π-bond of GNPG and the π-bond of benzene rings of the polystyrene lead to a decrease in the mobility of the polymer chain and free volume. 27,35 However, an excess of PG powders fails to exfoliate more GNs at the oil-water interface, and it suggests that the addition of more PG powders cannot improve the material properties effectively. 22,24 The TGA curves of PNGL X are presented in Figure 3b,c.…”

Section: Optical Micrographs Of Pickering Emulsionmentioning

confidence: 99%

Graphene nanosheet stabilized Pickering emulsion and its polystyrene nanocomposites

Wang

et al. 2023

J of Applied Polymer Sci

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High thermal conductivity and electrical insulation materials are necessary for packaging materials of miniaturized electronic equipment at present. Graphene nanosheets (GNs) have great potential in polymer composites due to its excellent thermal conductivity. However, the ultra‐high conductivity of GNs will reduce the insulation of polymer composites, so that the GN composites have good thermal conductivity and electrical insulation has become the focus of research. Herein, the closed cell polystyrene nanocomposites are polymerized by using a Pickering emulsion route. Based on the exfoliating effect of pristine graphite at the oil–water interface and the amphiphilic properties of GNs during the preparation of emulsion, the resulting graphite can achieve good dispersion in the polystyrene resin. Furthermore, the high heat transfer rate of the resulting graphite makes the temperature of polystyrene resin increase by 60.2°C at room temperature within 1 min. Meanwhile, the high volume electrical resistivity (3.63 × 109 Ω·m) and breakdown strength (33.8 kV·mm−1) confirms the PNGL0.610%/PS composite keep excellent electrical insulation at 10.0 wt% filler loading.

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“…The chances of agglomeration increase with the increase of filler content in the polymer matrix. Raza et al 14 reported that a decrease in the sonication time from a certain level after mixing polystyrene and graphene in solution forms cause agglomeration. In order to tackle this extra challenge, polystyrene and graphene solution is kept in a vial and sonicated for 25 min for better dispersion.…”

Section: Creep Testingmentioning

confidence: 99%

“…[4][5][6][7][8][9][10] Researchers working in polymeric nanocomposites have already made tremendous advances by introducing functional binders in nanocomposites to effectively utilise them for the benefit of society. [11][12][13][14][15] However, creep, a time-dependent fracture phenomenon that occurs due to molecular movement in the backbone of the base polymer structure, adversely affects the strength and other structural properties, hampering polymers' practical applications 16,17 in various engineering sectors. When the constant load is applied to the polymer, it experiences a time-dependent increase in strain, inclusively at room temperature; as a consequence, elastic/plastic deformation occurs, leading structure towards failure.…”

Section: Introductionmentioning

confidence: 99%

Enhancement in creep resistance of pristine polystyrene with incorporation of exfoliated 2D graphene nanosheets at low filler loading

Malik

Raza

Ahmad

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Determination of the reliability and durability of polymers in structural applications is highly dependent on the resistance to time-dependent plastic deformation. In this study, creep behaviour and enhancement in creep resistance of polystyrene-graphene nanocomposites is investigated at low filler loading. Herein, 2D material (graphene) is synthesised by the liquid-phase exfoliation method ( LPE) using the tetrahydrofuran ( THF) solvent via batch sonication. Samples are characterised by Scanning Electron Microscopy ( SEM) for morphology analysis and X-rays Diffraction ( XRD) for identification of polystyrene-graphene (PS-G) peaks in nanocomposites. Additionally, Atomic Force Microscopy ( AFM) is employed to quantify sheet’s thickness and Optical Microscopy ( OM) to corroborate the dispersion of 2D sheets. The creep resistance of PS-G nanocomposites is measured at room temperature (25°C) by incorporating 2D sheets of flake length ∼359 μm with 0.1, 0.3, 0.5, 0.7 and 0.9 wt.%. A significant enhancement in the time to withstand constant creep load (1 N) is observed. The creep resistance of the samples exhibits a maximum increase of 79%, 258.24%, 647.25%, 417.58% and 760.44%, respectively, compared to pristine polystyrene. This increase in creep resistance resulted from intense interfacial contact between polymer chains and filler accompanied by adequate scattering/dispersion in the polymer matrix.

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Production and investigation of mechanical properties of graphene/polystyrene nano composites

Cited by 14 publications

References 34 publications

Mechanical properties prediction of various graphene reinforced nanocomposites using transfer learning-based deep neural network

Mechanical properties prediction of various graphene reinforced nanocomposites using transfer learning-based deep neural network

Graphene nanosheet stabilized Pickering emulsion and its polystyrene nanocomposites

Enhancement in creep resistance of pristine polystyrene with incorporation of exfoliated 2D graphene nanosheets at low filler loading

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