Abstract Understanding and controlling the growth of vertically aligned carbon nanotube (VACNT) forests by chemical vapor deposition (CVD) is essential for unlocking their potential as candidate materials for next generation energy and mass transport devices. These advances in CNT manufacturing require developing in situ characterization techniques capable of interrogating how CNTs grow, interact, and self-assemble. Here we present a technique for real-time monitoring of VACNT forest height kinetics applied to a unique custom designed rapid thermal processing (RTP) reactor for CVD of VACNTs. While the integration of multiple infrared heating lamps enables creating designed spatiotemporal temperature profiles inside the reactor, they pose challenges for in situ measurements. Hence, our approach relies on contrast-adjusted videography and image processing, combined with calibration using 3D optical microscopy with large depth-of-field. Our work enables reliably measuring VACNT growth rates and catalytic lifetimes, which are not possible to measure using ex situ methods.
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In-Situ Scanning Electron Microscope Chemical Vapor Deposition as a Platform for Nanomanufacturing Insights
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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- NSF-PAR ID:
- 10334069
- Date Published:
- Journal Name:
- ASME 2021 International Mechanical Engineering Congress and Exposition
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/IMECE2021-73554
