Microstructure of graphene oxide–silica-reinforced OPC composites: Image-based characterization and nano-identification through deep learning

Lin, Junlin; Liu, Yanming; Sui, Hao; Sagoe‐Crentsil, Kwesi; Duan, Wenhui

doi:10.1016/j.cemconres.2022.106737

Cited by 34 publications

(7 citation statements)

References 48 publications

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“…As a result, graphene-based polymer nanocomposites have piqued researchers' curiosity all over the world. Graphene polymer nanocomposites have been made using a variety of polymers as matrices, including epoxy (Gervasoni, 2016;Wu et al, 2020a), PMMA (Ramanathan et al, 2007;Jang et al, 2009), HDPE (Zheng et al, 2004), polystyrene (Yuan et al, 2009), and nylon (Frontini and Pouzada, 2011;Goodfellow et al, 2016;Moore et al, 2018;Pierson et al, 2019;Qi et al, 2019;Schwarzer et al, 2019;Lin et al, 2022). The use of graphenesupported nanocomposites for the development of novel materials is becoming more common.…”

Section: Graphene-based Nanocomposites/ Nanopolymersmentioning

confidence: 99%

“…In this regard, modern artificial intelligence (AI) approaches are now providing a new view regarding the evaluation of nanocomposites. Lin et al (2022) has used convolutional neural networks (CNN), an advanced AI approach, to evaluate the mechanism of nanomaterial reinforcements using data image processing.…”

Section: Ai-based Modeling and Preparation Of Graphenementioning

confidence: 99%

“…At a micro level, the AI extracts higher-dimensional information such as porosity, solid material shape, and the arrangement of numerous phases, providing a better understanding of its structural dynamics. This enables a better interpretation of the potential mechanism behind nano-reinforcement of composite materials, aiding the design of such nanocomposites (Lin et al, 2022).…”

Section: Ai-based Modeling and Preparation Of Graphenementioning

confidence: 99%

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Synthesis and applications of graphene and graphene-based nanocomposites: Conventional to artificial intelligence approaches

Tariq

Ali

Arslan

et al. 2022

Front. Environ. Chem.

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Recent advances in graphene research have enabled the utilization of its nanocomposites for numerous energy-based and environmental applications. Recently, the advancement in graphene-based polymer nanocomposites has received much attention with special emphasis on synthesis and application. Graphene-based nanocomposites show astonishing electrical, mechanical, chemical, and thermal characteristics. Graphene nanocomposites (GNCs) are synthesized using a variety of methods, including covalent and non-covalent methods, a chemical-based deposition approach, hydrothermal growth, electrophoresis deposition, and physical deposition. Chemical methods are the most viable route for producing graphene in small quantities at low temperatures. The technique can also produce graphene films on a variety of substrate materials. The use of artificial intelligence (AI) for the synthesis of AI-created nanoparticles has recently received a lot of attention. These nanocomposite materials have excellent applications in the environmental, energy, and agricultural sectors. Due to high carrier mobility, graphene-based materials enhance the photocatalytic performance of semiconductor materials. Similarly, these materials have high potential for pollutant removal, especially heavy metals, due to their high surface area. This article highlights the synthesis of graphene-based nanocomposites with special reference to harnessing the power of modern AI tools to better understand GNC material properties and the way this knowledge can be used for its better applications in the development of a sustainable future.

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Section: Graphene-based Nanocomposites/ Nanopolymersmentioning

confidence: 99%

Section: Ai-based Modeling and Preparation Of Graphenementioning

confidence: 99%

Section: Ai-based Modeling and Preparation Of Graphenementioning

confidence: 99%

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Synthesis and applications of graphene and graphene-based nanocomposites: Conventional to artificial intelligence approaches

Tariq

Ali

Arslan

et al. 2022

Front. Environ. Chem.

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“…[22][23][24][25][26][27][28] Finally, in the last couple of years, we are witnessing a fast-growing interest in the application of (iv) artificial intelligence, in the form of machine and deep learning algorithms. [29][30][31][32][33][34][35][36][37] Each of these classes of computational approaches has its own advantages and drawbacks. Machine learning approaches are powerful predictive tools that can be trained on experimental or theoretical data.…”

Section: Introductionmentioning

confidence: 99%

Mechanical and dynamical stability of major oxide constituents of Portland cement clinker: a density functional theory study

Maul,

Mitoli,

Erba

et al. 2024

J Am Ceram Soc.

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Quantum‐mechanical calculations based on the density functional theory (DFT) enable an effective characterization of a variety of properties of materials at the atomistic level, although at a high computational cost. Here, we fully exploit parallel computing to apply such methods to the study of lattice dynamics and mechanical response of the major oxide constituents of Portland cement clinker: , , , and . Raman spectra and the evolution of the elastic tensor (and associated mechanical properties) with pressure are predicted for all oxide systems. We devote much attention to the assessment of dynamical and mechanical stability of the many previously proposed polymorphic forms of the most abundant oxide: . Specifically, five different crystalline models of are analyzed: only two turn out to be dynamically stable. The mechanical response of is further analyzed as a function of temperature through a quasi‐harmonic description of its lattice dynamics.

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“…With the help of adaptive momentum-based optimization technique, the developed deep learning model could accurately predict the non-dimensional natural frequency of the nanocomposite plates. Lin et al 16 utilized convolutional neural network (CNN) to study microstructure of graphene oxide reinforced cement composites. Using backscattered electron images to train the network, they could accurately extract microstructural features of the composites, and accordingly investigated the mechanisms of graphene oxide reinforcement.…”

Section: Introductionmentioning

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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Microstructure of graphene oxide–silica-reinforced OPC composites: Image-based characterization and nano-identification through deep learning

Cited by 34 publications

References 48 publications

Synthesis and applications of graphene and graphene-based nanocomposites: Conventional to artificial intelligence approaches

Synthesis and applications of graphene and graphene-based nanocomposites: Conventional to artificial intelligence approaches

Mechanical and dynamical stability of major oxide constituents of Portland cement clinker: a density functional theory study

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

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