Growth and Mechanics of Heterogeneous, 3D Carbon Nanotube Forest Microstructures Formed by Sequential Selective-Area Synthesis

Hines, Ryan Patrick; Hajilounezhad, Taher; Love-Baker, Cole; Koerner, G.; Maschmann, Matthew R.

doi:10.1021/acsami.0c03082

Cited by 20 publications

(14 citation statements)

References 68 publications

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“…The outer diameter was 10 nm for film-catalyst CNTs, and 40 nm for floating-catalyst CNTs. The inner diameter of all CNTs was 70% that of the outer diameter, consistent with experimental results shown in [113]. The population growth rate was based on a Gaussian population distribution with a mean of 60 nm per simulation step and a standard deviation of 5 nm/step.…”

Section: The Mechanical Behavior Of Single and Multiple-generation Cnt Forestssupporting

confidence: 86%

“…adding new elements to the base of each CNT at discrete time steps, approximating the base-growth mechanism that is frequently observed for CNT forests. First, the simulation construct is described for the growth and synthesis of CNT forests, followed by post-synthesis simulation models, namely compression and tension that resembles mechanical material characterization methods like nanoindentation [10,112,113].…”

Section: Data-and Artificial Intelligence-driven Materials Researchmentioning

confidence: 99%

“…In an experimental study [113], three-dimensional carbon nanotube (CNT) forest microstructures are synthesized using sequenced, site-specific synthesis techniques. Thin film layers of Al2O3 and Al2O3/Fe are patterned to support film-catalyst and floating-catalyst chemical vapor deposition (CVD) in specific areas.…”

Section: The Mechanical Behavior Of Single and Multiple-generation Cnt Forestsmentioning

confidence: 99%

“…The mechanical behavior of single and multiple-generation CNT forests was examined using the finiteelement mechanical model. Each simulated CNT forest was first synthesized numerically using growth parameters consistent with experimentally obtained parameters, described in details in [113]. The simulation span for both CNT forests was 25 µm, with periodic boundary conditions in the horizontal direction.…”

Section: The Mechanical Behavior Of Single and Multiple-generation Cnt Forestsmentioning

confidence: 99%

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Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

Hajilounezhad¹

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This work is aimed to explore process-structure-property relationships of carbon nanotube (CNT) forests. CNTs have superior mechanical, electrical and thermal properties that make them suitable for many applications. Yet, due to lack of manufacturing control, there is a huge performance gap between promising properties of individual CNTs and CNT forest properties that hinders their adoption into potential industrial applications. In this research, computational modelling, in-situ electron microscopy for CNT synthesis, and data-driven and high-throughput deep convolutional neural networks are employed to not only accelerate implementing CNTs in various applications but also to establish a framework to make validated predictive models that can be easily extended to achieve application-tailored synthesis of any materials. A time-resolved and physics-based finite-element simulation tool is modelled in MATLAB to investigate synthesis of CNT forests, specially to study the CNT-CNT interactions and generated mechanical forces and their role in ensemble structure and properties. A companion numerical model with similar construct is then employed to examine forest mechanical properties in compression. In addition, in-situ experiments are carried out inside Environmental Scanning Electron Microscope (ESEM) to nucleate and synthesize CNTs. Findings may primarily be used to expand the forest growth and self-assembly knowledge and to validate the assumptions of simulation package. Also, SEM images can be used as feed database to construct a deep learning model to grow CNTs by design. The chemical vapor deposition parameter space of CNT synthesis is so vast that it is not possible to investigate all conceivable combinations in terms of time and costs. Hence, simulated CNT forest morphology images are used to train machine learning and learning algorithms that are able to predict CNT synthesis conditions based on desired properties. Exceptionally high prediction accuracies of R2 > 0.94 is achieved for buckling load and stiffness, as well as accuracies of > 0.91 for the classification task. This high classification accuracy promotes discovering the CNT forest synthesis-structure relationships so that their promising performance can be adopted in real world applications. We foresee this work as a meaningful step towards creating an unsupervised simulation using machine learning techniques that can seek out the desired CNT forest synthesis parameters to achieve desired property sets for diverse applications.

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Section: The Mechanical Behavior Of Single and Multiple-generation Cnt Forestssupporting

confidence: 86%

Section: Data-and Artificial Intelligence-driven Materials Researchmentioning

confidence: 99%

Section: The Mechanical Behavior Of Single and Multiple-generation Cnt Forestsmentioning

confidence: 99%

Section: The Mechanical Behavior Of Single and Multiple-generation Cnt Forestsmentioning

confidence: 99%

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Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

Hajilounezhad¹

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“…Combining internal combustion engines or SOFC with GT, the gaseous fuel can be converted to heat and power. However, the combination of SOFC and GT yields better performance [2][3][4][5]. This hybrid energy conversion system will provide superior utilization of natural resources that considerably decreases harmful impacts on the environment as well as making higher profit [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Solid Oxide Fuel Cell/GT Combined Heat and Power System: A comparison between Particle Swarm and Genetic Algorithms

Hajilounezhad¹,

Safari²,

Ehyaei³

2020

Preprint

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Many studies have attempted to optimize integrated Solid Oxide Fuel Cell-Gas Turbine (SOFC-GT), although different and somehow conflicting results are reported employing various algorithms. In this study, Multi-Objective Optimization (MOO) is employed to approach the optimal design of SOFC-GT considering all prevailing factors. The emphasis is placed on the evaluation of the Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) performance as two effective approaches for solving the multi-objective and non-linear optimization problems. Multi- objective optimization is carried out on two vital objectives; the electrical efficiency and the overall output power of the system. The considerable achievements are the set of optimal points that aim to identify the system optimal performance which provides a practical basis for the decision-makers to choose the appropriate target functions. For the studied conditions, the two algorithms nearly exhibit similar performance, while the PSO is faster and more efficient in terms of computational effort. The PSO appears to achieve its ultimate parameter values in fewer generations compared to the GA algorithm under the examined circumstances. It is found that the maximum power of 410 kW is accomplished employing the GA optimization method with an efficiency of 64%, while PSO method yields the maximum power of 419.19 kW at the efficiency of 58.9%. The results stress that PSO offers more satisfactory convergence and fidelity of the solution for the SOFC-GT MOO problems.

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Synthesis of N‐Doped Porous Carbon/Carbon Micro‐Nanotubes/Ni_xCo_yO_z Nanosheets as a High‐Capacity Electrode Material for Supercapacitors

Cong

Yang²,

et al. 2021

ChemistrySelect

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The N‐doped porous carbon (NPC) /carbon micro‐nanotubes (CMNT) /NixCoyOz nanosheets with rich mesopores and 1.37 wt% nitrogen doping are successfully synthesized by a pyrolysis‐ionization‐precipitation combination process, using coal‐based polyaniline and nickelocene as original materials. Therein, most of the CMNT are upright CMTs and worm‐like curved CNTs, and a few are bamboo‐like tubes or carbon nanofibers. Moreover, many flower‐like aggregates assembled by spinel NiCo2O4 nanosheets are anchored on the surface of the NPC and CMNT. The NPC/CMNT/NixCoyOz exhibits a high specific capacitance and cycling stability, attributed to the large BET specific surface area (485 m2/g), suitable BET average pore size (2.3 nm), good hydrophilicity and wettability, and synergistic effects among three components. The assembled NPC/CMNT/NixCoyOz//AC asymmetric supercapacitor also demonstres a high specific capacitance of 120 F g−1 at a current density of 1 A g−1 and a high energy density of 16.7 Wh kg−1 at a power density of 500 W kg−1.

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Growth and Mechanics of Heterogeneous, 3D Carbon Nanotube Forest Microstructures Formed by Sequential Selective-Area Synthesis

Cited by 20 publications

References 68 publications

Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

Multi-Objective Optimization of Solid Oxide Fuel Cell/GT Combined Heat and Power System: A comparison between Particle Swarm and Genetic Algorithms

Synthesis of N‐Doped Porous Carbon/Carbon Micro‐Nanotubes/Ni_xCo_yO_z Nanosheets as a High‐Capacity Electrode Material for Supercapacitors

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Growth and Mechanics of Heterogeneous, 3D Carbon Nanotube Forest Microstructures Formed by Sequential Selective-Area Synthesis

Cited by 20 publications

References 68 publications

Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

Multi-Objective Optimization of Solid Oxide Fuel Cell/GT Combined Heat and Power System: A comparison between Particle Swarm and Genetic Algorithms

Synthesis of N‐Doped Porous Carbon/Carbon Micro‐Nanotubes/NixCoyOz Nanosheets as a High‐Capacity Electrode Material for Supercapacitors

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Synthesis of N‐Doped Porous Carbon/Carbon Micro‐Nanotubes/Ni_xCo_yO_z Nanosheets as a High‐Capacity Electrode Material for Supercapacitors