While the physical properties of carbon nanotubes (CNTs) are often superior to conventional engineering materials, their widespread adoption into many applications is limited by scaling the properties of individual CNTs to macroscale CNT assemblies known as CNT forests. The self-assembly mechanics of CNT forests that determine their morphology and ensemble properties remain poorly understood. Few experimental techniques exist to characterize and observe the growth and self-assembly processes in situ. Here we introduce the use of in-situ scanning electron microscope (SEM) synthesis based on chemical vapor deposition (CVD) processing. In this preliminary report, we share best practices for in-situ SEM CVD processing and initial CNT forest synthesis results. Image analysis techniques are developed to identify and track the movement of catalyst nanoparticles during synthesis conditions. Finally, a perspective is provided in which in-situ SEM observations represent one component of a larger system in which numerical simulation, machine learning, and digital control of experiments reduces the role of humans and human error in the exploration of CNT forest process-structure-property relationships.
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Numerical Investigation of Internal Forces During Carbon Nanotube Forest Self-Assembly
A time-resolved two-dimensional finite element simulation is developed to model the forces generated during the self-assembly of actively growing CNT populations with distributed properties and growth characteristics. CNTs are simulated as interconnected frame elements that undergo the base growth mechanism. Mechanical equilibrium at each computational node is determined at each time step using the Updated Lagrangian method. Emphasis is placed on the transmission of force to the growth substrate, where catalyst particles reside. The simulated CNT forest structural morphology is similar to that of physical CNT forests, and results indicate that stresses on the order of GPa are transmitted to catalyst particles. The force transmitted to a given catalyst particle is correlated to the rate at which the CNT grows relative to the population averaged growth rate. The effect of diameter-dependent CNT growth rates and the persistence of vdW bonds are also examined relative to the forces generated during forest self-assembly. Results from this study may be applied to the study of CNT forest self-assembly, resultant ensemble forest properties, and force-modulated CNT growth kinetics.
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- Award ID(s):
- 1651538
- NSF-PAR ID:
- 10098171
- Date Published:
- Journal Name:
- ASME 2018 International Mechanical Engineering Congress and Exposition
- Volume:
- 2
- Page Range / eLocation ID:
- V002T02A088
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.
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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 propertiesmore » « less
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For the scalable production of commercial products based on vertically aligned carbon nanotubes (VACNTs), referred to as CNT forests, key manufacturing challenges must be overcome. In this work, we describe some of the main challenges currently facing CNT forest manufacturing, along with how we address these challenges with our custom-built rapid thermal processing chemical vapor deposition (CVD) reactor. First, the complexity of multistep processes and reaction pathways involved in CNT growth by CVD limits the control on CNT population growth dynamics. Importantly, gas-phase decomposition of hydrocarbons, formation of catalyst particles, and catalytic growth of CNTs are typically coupled. Here, we demonstrated a decoupled recipe with independent control of each step. Second, significant run-to-run variations plague CNT growth by CVD. To improve growth consistency, we designed various measures to remove oxygen-containing molecules from the reactor, including air baking between runs, dynamic pumping down cycles, and low-pressure baking before growth. Third, real-time measurements during growth are needed for process monitoring. We implement in situ height kinetics via videography. The combination of approaches presented here has the potential to transform lab-scale CNT synthesis to robust manufacturing processes.more » « less
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For the scalable production of commercial products based on vertically aligned carbon nanotubes (VACNTs), referred to as CNT forests, key manufacturing challenges must be overcome. In this work, we describe some of the main challenges currently facing CNT forest manufacturing, along with how we address these challenges with our custom-built rapid thermal processing chemical vapor deposition (CVD) reactor. First, the complexity of multistep processes and reaction pathways involved in CNT growth by CVD limits the control on CNT population growth dynamics. Importantly, gas-phase decomposition of hydrocarbons, formation of catalyst particles, and catalytic growth of CNTs are typically coupled. Here, we demonstrated a decoupled recipe with independent control of each step. Second, significant run-to-run variations plague CNT growth by CVD. To improve growth consistency, we designed various measures to remove oxygen-containing molecules from the reactor, including air baking between runs, dynamic pumping down cycles, and low-pressure baking before growth. Third, real-time measurements during growth are needed for process monitoring. We implement in situ height kinetics via videography. The combination of approaches presented here has the potential to transform lab-scale CNT synthesis to robust manufacturing processes.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/IMECE2018-86567
