Simulation and modeling of carbon nanotube synthesis: current trends and investigations

Raji, K.; Sobhan, C. B.

doi:10.1515/ntrev-2012-0038

Cited by 9 publications

(6 citation statements)

References 87 publications

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“…Of course, such a division is somewhat ambiguous, and oen more than one type of method is employed to tackle situations in which phenomena occur over multiple time and length scales. 79,80 Nevertheless, it serves as a convenient framework in which to discuss previous modelling studies.…”

Section: Review Of Modelling Methods and Previous Applications To Gramentioning

confidence: 99%

Atomistic modelling of CVD synthesis of carbon nanotubes and graphene

et al. 2013

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We discuss the synthesis of carbon nanotubes (CNTs) and graphene by catalytic chemical vapour deposition (CCVD) and plasma-enhanced CVD (PECVD), summarising the state-of-the-art understanding of mechanisms controlling their growth rate, chiral angle, number of layers (walls), diameter, length and quality (defects), before presenting a new model for 2D nucleation of a graphene sheet from amorphous carbon on a nickel surface. Although many groups have modelled this process using a variety of techniques, we ask whether there are any complementary ideas emerging from the different proposed growth mechanisms, and whether different modelling techniques can give the same answers for a given mechanism. Subsequently, by comparing the results of tight-binding, semi-empirical molecular orbital theory and reactive bond order force field calculations, we demonstrate that graphene on crystalline Ni(111) is thermodynamically stable with respect to the corresponding amorphous metal and carbon structures. Finally, we show in principle how a complementary heterogeneous nucleation step may play a key role in the transformation from amorphous carbon to graphene on the metal surface. We conclude that achieving the conditions under which this complementary crystallisation process can occur may be a promising method to gain better control over the growth processes of both graphene from flat metal surfaces and CNTs from catalyst nanoparticles.

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Section: Review Of Modelling Methods and Previous Applications To Gramentioning

confidence: 99%

Atomistic modelling of CVD synthesis of carbon nanotubes and graphene

et al. 2013

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“…Amongst the methods proposed for CNTs production, CVD technique have been applauded over other techniques for large scale CNTs synthesis. CVD allows for scale-up and precise control of CNTs orientation, alignment, diameter, length, purity, yield, etc (Bazargan & Mckay, 2012;Raji & Sobhan, 2013). This method is simple and cheap with less energy consumption and minimal pollution compared to other techniques (Bazargan & Mckay, 2012;Raji & Sobhan, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…CVD allows for scale-up and precise control of CNTs orientation, alignment, diameter, length, purity, yield, etc (Bazargan & Mckay, 2012;Raji & Sobhan, 2013). This method is simple and cheap with less energy consumption and minimal pollution compared to other techniques (Bazargan & Mckay, 2012;Raji & Sobhan, 2013). The unique thermal, electrical, mechanical, high aspect ratio properties of CNTs (Bazargan & Mckay, 2012) have been the driver for the growing demand (market) in applications such as in polymers additives, catalysts, gas-discharge tubes in telecom networks, electron eld emitters for cathode ray lighting elements, at panel display, electromagnetic-wave absorption & shielding, hydrogen storage, energy conversion, lithium-battery anodes, nanotube composites (by coating or lling), nanoelectrodes, nanolithography, cancer drug delivery, sensors, reinforcements in composites, and supercapacitor, CO 2 adsorption, wastewater treatment, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Different modelling methods such as computational uid dynamics (CFD), chemical kinetic modelling, response surface methodology (RSM), etc. has been applied in modelling the nucleation and growth of CNTs as well as predicting the yield and structure of CNTs (Raji & Sobhan, 2013). RSM, for example, has gained recognition in various elds such as in catalyst design and engineering, materials science, etc.…”

Section: Introductionmentioning

confidence: 99%

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Artificial neural network for predicting the performance of waste polypropylene plastic-derived carbon nanotubes

Modekwe

Akintola

Ayeleru

et al. 2023

Preprint

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In this study, artificial neural network (ANN) model [function fitting neural network (FITNET)] was developed to describe the yield and quality of multi-walled carbon nanotubes (MWCNTs) deposited over NiMo/CaTiO3 catalyst using waste polypropylene plastics as cheap hydrocarbon feedstock using a single-stage chemical vapour deposition (CVD) technique. Experimental dataset was developed using user-specific design with four numeric factors (input variable): synthesis temperature, furnace heating rate, residence time and carrier gas (nitrogen) flow rate to control the performance (yield and quality) of produced CNTs. Levenberg-Marquardt algorithm was utilized in training, validating, and testing the experimental dataset. The predicted model gave considerable coefficient (R) value close to 1. The presented model would be of remarkable benefit to successfully describe and predict the performance of PP-derived CNTs and show how the predictive variables could affect the response variables (quality and yield) of CNTs.

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“…This, in turn, will help to identify the anatomical factors that contribute to the OSA severity on patient-specific basis. Computational simulations have been widely utilized to provide an invaluable perception that complement and guide experimental measurements [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. The potential of computational fluid dynamics (CFD) in predicting clinical outcomes and assessing the diseased conditions in human upper airways has been previously demonstrated [ 21 , 22 , 23 , 24 , 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

Upper Airway Flow Dynamics in Obstructive Sleep Apnea Patients with Various Apnea-Hypopnea Index

Lin

Premaraj

Gamage

et al. 2022

Life

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Background and aim: This study evaluates the upper airway flow characteristics, anatomical features and analyzes their correlations with AHI in patients with varied degrees of OSA severity seeking for discernments of the underlying pathophysiological profile. Materials and Methods: Patient-specific computational fluid dynamics models were reconstructed from high-resolution cone-beam computed tomography images for 4 OSA patients classified as minimal, mild, moderate, and severe according to AHI. Results: The parameters, minimal cross-sectional area (MCA), and the pharyngeal airway volume did not show clear correlations with the OSA severity defined according to AHI. No correlations were found between the classically defined resistance of the airway in terms of pressure drop and AHI. The flow analysis further showed that the fluid mechanisms likely to cause airway collapse are associated with the degree of narrowing in the pharyngeal airway rather than AHI. Results also suggested that some patients classified as severe OSA according to the AHI can show less susceptibility to airway collapse than patients with relatively lower AHI values and vice versa. Conclusions: The relative contribution of anatomical and non-anatomical causes to the OSA severity can significantly vary between patients. AHI alone is inadequate to be used as a marker of the pathophysiological profile of OSA. Combining airflow analysis with AHI in diagnosing OSA severity may provide additional details about the underlying pathophysiology, subsequently improving the individualized clinical outcomes.

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Simulation and modeling of carbon nanotube synthesis: current trends and investigations

Cited by 9 publications

References 87 publications

Atomistic modelling of CVD synthesis of carbon nanotubes and graphene

Atomistic modelling of CVD synthesis of carbon nanotubes and graphene

Artificial neural network for predicting the performance of waste polypropylene plastic-derived carbon nanotubes

Upper Airway Flow Dynamics in Obstructive Sleep Apnea Patients with Various Apnea-Hypopnea Index

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