An Analytical Approach to Calculate the Charge Density of Biofunctionalized Graphene Layer Enhanced by Artificial Neural Networks

Karimi, Hamid; Rahmani, Rasoul; Othman, Mohd Fauzi; Zohoori, Bahareh; Mahrami, Mohsen; Kamyab, Hesam; Hosseini, Seyed Ebrahim

doi:10.1007/s11468-015-9998-y

Cited by 31 publications

(5 citation statements)

References 34 publications

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“…One of the most promising applications for graphene and FGNs-based 2D coating/thin films are in neural tissue regeneration due to their high electrical conductivity and bioactivity. ,− Since the neural cells are electroactive, and the electrical conductivity of graphene and FGNs can be easily changed to adapt to the required conductivity of neural interfaces. Furthermore, the chemical and mechanical characteristics of graphene and FGNs can also provide great benefits for long-term neural implants. , Meng has studied to apply the electrical stimulation for in situ modification of PC-12 cells behavior by using graphene-based substrate under different stimulation period, intensity, frequency, electrical pulse and interval change .…”

Section: Emerging Biological Applications Of Fgns-based Architecturesmentioning

confidence: 99%

Functional Graphene Nanomaterials Based Architectures: Biointeractions, Fabrications, and Emerging Biological Applications

et al. 2017

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Functional graphene nanomaterials (FGNs) are fast emerging materials with extremely unique physical and chemical properties and physiological ability to interfere and/or interact with bioorganisms; as a result, FGNs present manifold possibilities for diverse biological applications. Beyond their use in drug/gene delivery, phototherapy, and bioimaging, recent studies have revealed that FGNs can significantly promote interfacial biointeractions, in particular, with proteins, mammalian cells/stem cells, and microbials. FGNs can adsorb and concentrate nutrition factors including proteins from physiological media. This accelerates the formation of extracellular matrix, which eventually promotes cell colonization by providing a more beneficial microenvironment for cell adhesion and growth. Furthermore, FGNs can also interact with cocultured cells by physical or chemical stimulation, which significantly mediate their cellular signaling and biological performance. In this review, we elucidate FGNs-bioorganism interactions and summarize recent advancements on designing FGN-based two-dimensional and three-dimensional architectures as multifunctional biological platforms. We have also discussed the representative biological applications regarding these FGN-based bioactive architectures. Furthermore, the future perspectives and emerging challenges will also be highlighted. Due to the lack of comprehensive reviews in this emerging field, this review may catch great interest and inspire many new opportunities across a broad range of disciplines.

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Section: Emerging Biological Applications Of Fgns-based Architecturesmentioning

confidence: 99%

Functional Graphene Nanomaterials Based Architectures: Biointeractions, Fabrications, and Emerging Biological Applications

et al. 2017

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“…An artificial neural network (ANN) is a bioinspired computational model made up of hundreds of single units referred to as artificial neurons that are coupled with coefficients/weights to form the neural structure; ANNs also commonly known as processing elements (PE) since they are able to process data. [109][110][111][112][113][114] Every PE consists of weighted inputs, one output and transfer function. Essentially, PE is an equation that balances outputs and inputs.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

The metaheuristic optimization of the mechanical properties of sustainable energies using artificial neural networks and genetic algorithm: A case study by eggshell fine waste

Wang

AL‐Huqail

Salimimoghadam

et al. 2022

Intl J of Energy Research

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Summary Eggshell concrete is a novel green material that aids the recycling of eggshell powder (ESP) waste while decreasing the environmental damage due to higher manufacture to develop sustainable energies. Nevertheless, current investigations on eggshell concrete are limited, and the results might vary according to admixture design variations. Despite the fact that the design of experiments is utilized to simplify and optimize the research of sustainable energies, the studies employing eggshell concrete are still uncommon. The powdered egg shells were employed as fine concrete aggregate as a tool of sustainable energies. The flexural and compressive strength of concrete with (5%, 10%, and 15%) and without egg shell are examined, and the findings are predicted by artificial neural network (ANN) and genetic algorithm (GA) as a hybridized model of ANN‐GA. The contour plot research revealed that eggshell powder boosted the energy stability in an appropriate replacement proportion of 5% to 10%. Conversely, for mix designs with a larger water ratio, the partial substitution with eggshell powder is preferable. The findings demonstrate that with 5% ESP replacement, the strengths were greater than in control concrete, indicating that 5% ESP is an ideal content for maximal strength. Furthermore, in terms of transport qualities, the performance of ESP concretes was equivalent to control concrete up to 15% ESP substitution. The statistical regression indices as determination coefficient (R2) and root‐mean‐square error demonstrated that the ANN‐GA model is an effective tool for formulating and predicting the flexural and compressive strength of eggshell concrete to develop sustainable energies.

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“…The biological artificial neural network (ANN) for humans/animals 94-109 is derived by NN, a popular multilayer method. [110][111][112][113][114][115] By means of the layer mechanism, ANN can handle appropriate complexities in multidimensional issue space to estimate the objectives. [116][117][118][119][120][121][122][123][124][125][126][127] Multilayer perceptron (MLP) is a basic practical type of feed ANN.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The biological artificial neural network (ANN) for humans/animals 94–109 is derived by NN, a popular multilayer method 110–115 . By means of the layer mechanism, ANN can handle appropriate complexities in multidimensional issue space to estimate the objectives 116–127 .…”

Section: Materials and Mixture Proportionsmentioning

confidence: 99%

Compressive strength of efficient self‐compacting concrete with rice husk ash, fly ash, and calcium carbide waste additives using multiple artificial intelligence methods

Mohamed

Bashir

Dirar

et al. 2021

Structural Concrete

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The principal benefit of mixed concrete is to minimize the disadvantages of certain complementary cement ingredients by combining them with other better materials so that overall costs are managed and efficiencies of concrete production are increased. The structural and economic characteristics of concrete can also be improved. In this work, the compressive strength (CS) of selfcompacting concrete (SCC) was assessed and their interrelations were addressed using binary and ternary cementitious combinations of rice husk, fly ash (FA), and calcium carbide. Accordingly, various admixtures were prepared with diverse amounts of wastes by substituting ordinary Portland cement (5%). Thus, due to the raising of rice husk and FA, the CS of SCC was considerably improved. To raise the accuracy of the analysis, three soft computing models of particle swarm optimization (PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) were used. Six input parameters comprising FA replacement ratio, rice husk, total cementitious material, water cement ratio, high ratio water reducing agent and age of samples (AS), and one output parameter as the CS of SCC have been investigated. Subsequently, following the root mean square error and R 2 results, three methods were shown as reliable tools for assessing the influence of cementitious material on CS of self-compact concrete; however, ANFIS remarkably was better than ANN and PSO. As a result, rice husk showed less contribution to the strength of concrete at short times, but much at longer time than FA and calcium carbide. The enhanced influence of low amount of calcium carbide on CS was not significant. Also, it was found that the specimen incorporating of cement with 15% calcium carbide showed better workability than that of normal SCC specimen without calcium. Therefore, rice husk ash showed higher potential to be applied as partial replacements in SCC production.Discussion on this paper must be submitted within two months of the print publication. The discussion will then be published in print, along with the authors' closure, if any, approximately nine months after the print publication.

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An Analytical Approach to Calculate the Charge Density of Biofunctionalized Graphene Layer Enhanced by Artificial Neural Networks

Cited by 31 publications

References 34 publications

Functional Graphene Nanomaterials Based Architectures: Biointeractions, Fabrications, and Emerging Biological Applications

Functional Graphene Nanomaterials Based Architectures: Biointeractions, Fabrications, and Emerging Biological Applications

The metaheuristic optimization of the mechanical properties of sustainable energies using artificial neural networks and genetic algorithm: A case study by eggshell fine waste

Compressive strength of efficient self‐compacting concrete with rice husk ash, fly ash, and calcium carbide waste additives using multiple artificial intelligence methods

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