Controllable Synthesis of Carbon Nanotubes

An, Jia; Zhan, Zhaoyao; Zheng, Lianxi

doi:10.1016/b978-0-323-41481-4.00001-0

Cited by 4 publications

(3 citation statements)

References 170 publications

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“…These desired performance metrics are intricately related to the structural morphology of the CNT forest itself, although the functional relationships between CNT forest structural morphology and CNT forest properties are currently not well understood. For instance, the CNT forest morphology, in terms of CNT length, waviness, and density, may be tuned according to specific applications [23]-thin-film-type transistors require dense and parallel single-walled carbon nanotubes (SWCNTs) to sustain large current [24] [25], while their ultralong length enables easy fabrication of numerous devices out of one individual SWCNT and the construction of logic circuits at a single nanotube level [26] [27]. Therefore, predicting the combination of processing parameters required to grow 978-1-7281-4732-1/19/$31.00 c 2019 IEEE application-tailored CNT forests would represent a significant advance that could enable new CNT forest-based applications that fully exploit the beneficial properties of individual CNTs.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Hajilounezhad¹,

Oraibi²,

Surya³

et al. 2020

Preprint

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The parameter space of CNT forest synthesis is vastand multidimensional, making experimental and/or numericalexploration of the synthesis prohibitive. We propose a morepractical approach to explore the synthesis-process relationshipsof CNT forests using machine learning (ML) algorithms toinfer the underlying complex physical processes. Currently, nosuch ML model linking CNT forest morphology to synthesisparameters has been demonstrated. In the current work, weuse a physics-based numerical model to generate CNT forestmorphology images with known synthesis parameters to trainsuch a ML algorithm. The CNT forest synthesis variablesof CNT diameter and CNT number densities are varied togenerate a total of 12 distinct CNT forest classes. Images of theresultant CNT forests at different time steps during the growthand self-assembly process are then used as the training dataset.Based on the CNT forest structural morphology, multiplesingle and combined histogram-based texture descriptors areused as features to build a random forest (RF) classifier topredict class labels based on correlation of CNT forest physicalattributes with the growth parameters. The machine learningmodel achieved an accuracy of up to 83.5% on predicting thesynthesis conditions of CNT number density and diameter.These results are the first step towards rapidly characterizingCNT forest attributes using machine learning. Identifying therelevant process-structure interactions for the CNT forests usingphysics-based simulations and machine learning could rapidlyadvance the design, development, and adoption of CNT forestapplications with varied morphologies and properties.

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Section: Introductionmentioning

confidence: 99%

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Hajilounezhad¹,

Oraibi²,

Surya³

et al. 2020

Preprint

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“…Es un material versátil que se puede obtener recubriendo superficies, en forma de polvo o incluso en fibras. La tecnología para la producción de estos materiales nanoestructurados basados en carbono ha madurado tanto que se los puede encontrar en infinidad de materiales y objetos muy utilizados en la actualidad [3], [4]. Por ejemplo, se han desarrollado procesos industriales para formar largas fibras y luego construir telas conductoras de alto rendimiento [5], [6].…”

Section: Introductionunclassified

Modificación de la conductividad eléctrica de tinta comercial para impresión inkjet utilizando nanotubos de carbono multipared

Pantoja-Suárez

Urquiza

Hernández-Chulde³

et al. 2019

RITI

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Resumen: La conductividad eléctrica de la tinta comercial de color negro fue modificada al introducir nanotubos de carbono (NTC) multipared en porcentajes de 0,5 hasta 1,5% en peso. Los grupos carboxílicos, presentes en la superficie de los NTC, fueron suficientes para que la dispersión de las nanoestructuras fuera adecuada en la tinta. Además, no se distinguió incompatibilidad entre los NTC y los componentes de la tinta. De hecho, se pudo constatar una sinergia entre el pigmento negro y los NTC. La aplicación de la tinta utilizando el bolígrafo corrector modificado permitió obtener líneas uniformes sobre papel ordinario a base de fibras de celulosa y sobre papel fotográfico. Los patrones dibujados a mano que mejor conductividad presentaron fueron aquellos realizados sobre papel ordinario y cuyo contenido de NTC fue igual o superior al 1% en peso.

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“…These desired performance metrics are intricately related to the structural morphology of the CNT forest itself, although the functional relationships between CNT forest structural morphology and CNT forest properties are currently not well understood. For instance, the CNT forest morphology, in terms of CNT length, waviness, and density, may be tuned according to specific applications [23]-thin-film-type transistors require dense and parallel single-walled carbon nanotubes (SWCNTs) to sustain large current [24] [25], while their ultralong length enables easy fabrication of numerous devices out of one individual SWCNT and the construction of logic circuits at a single nanotube level [26] [27]. Therefore, predicting the combination of processing parameters required to grow application-tailored CNT forests would represent a significant advance that could enable new CNT forest-based applications that fully exploit the beneficial properties of individual CNTs.…”

Section: Introductionmentioning

confidence: 99%

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Hajilounezhad

Oraibi

Surya

et al. 2019

2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)

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The parameter space of CNT forest synthesis is vast and multidimensional, making experimental and/or numerical exploration of the synthesis prohibitive. We propose a more practical approach to explore the synthesis-process relationships of CNT forests using machine learning (ML) algorithms to infer the underlying complex physical processes. Currently, no such ML model linking CNT forest morphology to synthesis parameters has been demonstrated. In the current work, we use a physics-based numerical model to generate CNT forest morphology images with known synthesis parameters to train such a ML algorithm. The CNT forest synthesis variables of CNT diameter and CNT number densities are varied to generate a total of 12 distinct CNT forest classes. Images of the resultant CNT forests at different time steps during the growth and self-assembly process are then used as the training dataset. Based on the CNT forest structural morphology, multiple single and combined histogram-based texture descriptors are used as features to build a random forest (RF) classifier to predict class labels based on correlation of CNT forest physical attributes with the growth parameters. The machine learning model achieved an accuracy of up to 83.5% on predicting the synthesis conditions of CNT number density and diameter. These results are the first step towards rapidly characterizing CNT forest attributes using machine learning. Identifying the relevant process-structure interactions for the CNT forests using physics-based simulations and machine learning could rapidly advance the design, development, and adoption of CNT forest applications with varied morphologies and properties.

show abstract

Controllable Synthesis of Carbon Nanotubes

Cited by 4 publications

References 170 publications

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

Modificación de la conductividad eléctrica de tinta comercial para impresión inkjet utilizando nanotubos de carbono multipared

Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning

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