Ellipsometry-Based Quantification of Catalyst–Support Diffusion Kinetics in Rapid Thermal Chemical Vapor DepositionSource: Journal of Manufacturing Science and Engineering:;2026:;volume( 148 ):;issue:008::page 4613DOI: 10.1115/1.4071907Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Abstract. Dynamic chemical vapor deposition (CVD) with rapid thermal processing (RTP) offers a powerful means to independently control catalyst preparation and growth stages in carbon nanotube (CNT) manufacturing. However, catalyst deactivation caused by diffusion into oxide supports continues to limit yield and process robustness. In this study, we establish a quantitative framework for characterizing catalyst–support diffusion kinetics using spectroscopic ellipsometry coupled with cross-sectional scanning transmission electron microscopy (STEM), X-ray diffraction (XRD), and scanning electron microscopy (SEM). A multilayer optical model was developed to extract the thickness and composition of the evolving iron–alumina interface during thermal pretreatment at 700 °C and 900 °C under reducing conditions. The fitted parameters reveal measurable differences in both the rate of subsurface diffusion and the loss of surface catalyst, enabling nondestructive quantification of interface evolution. These ellipsometry-based results correlate with independent electron microscopy evidence and provide process-relevant metrics for assessing catalyst stability. By linking optical signatures to interfacial diffusion behavior, this approach introduces a generalizable metrology method for process monitoring, model validation, and design of stable, repeatable dynamic CVD recipes for nanocarbon manufacturing.
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| contributor author | Tomaraei, Golnaz | |
| contributor author | Lee, Jaegeun | |
| contributor author | Kim, Seung Min | |
| contributor author | Abdulhafez, Moataz | |
| contributor author | Bedewy, Mostafa | |
| date accessioned | 2026-08-23T07:21:47Z | |
| date available | 2026-08-23T07:21:47Z | |
| date copyright | 2026/08/01 | |
| date issued | 2026 | |
| identifier issn | 1087-1357 | |
| identifier other | manu-25-1554.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4314995 | |
| description abstract | Abstract. Dynamic chemical vapor deposition (CVD) with rapid thermal processing (RTP) offers a powerful means to independently control catalyst preparation and growth stages in carbon nanotube (CNT) manufacturing. However, catalyst deactivation caused by diffusion into oxide supports continues to limit yield and process robustness. In this study, we establish a quantitative framework for characterizing catalyst–support diffusion kinetics using spectroscopic ellipsometry coupled with cross-sectional scanning transmission electron microscopy (STEM), X-ray diffraction (XRD), and scanning electron microscopy (SEM). A multilayer optical model was developed to extract the thickness and composition of the evolving iron–alumina interface during thermal pretreatment at 700 °C and 900 °C under reducing conditions. The fitted parameters reveal measurable differences in both the rate of subsurface diffusion and the loss of surface catalyst, enabling nondestructive quantification of interface evolution. These ellipsometry-based results correlate with independent electron microscopy evidence and provide process-relevant metrics for assessing catalyst stability. By linking optical signatures to interfacial diffusion behavior, this approach introduces a generalizable metrology method for process monitoring, model validation, and design of stable, repeatable dynamic CVD recipes for nanocarbon manufacturing. | |
| publisher | The American Society of Mechanical Engineers (ASME) | |
| title | Ellipsometry-Based Quantification of Catalyst–Support Diffusion Kinetics in Rapid Thermal Chemical Vapor Deposition | |
| type | Journal Paper | |
| journal volume | 148 | |
| journal issue | 8 | |
| journal title | Journal of Manufacturing Science and Engineering | |
| identifier doi | 10.1115/1.4071907 | |
| journal fristpage | 4613 | |
| journal lastpage | 4616 | |
| page | 4 | |
| tree | Journal of Manufacturing Science and Engineering:;2026:;volume( 148 ):;issue:008 | |
| contenttype | Fulltext |