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PMID: 42108991 Published · ppublish English

Computational Modeling and Experimental Validation of Variabilities in Chemical Vapor Deposition of Graphene on Metals.

Small (Weinheim an der Bergstrasse, Germany) ·Vol. 22 ·No. 32 ·2026-06-00

Joshi T, Raman RKS, Ventikos Y

Abstract

Achieving laterally uniform graphene coatings via low-pressure chemical vapor deposition (LPCVD) remains challenging due to substrate-scale variations in near-wall transport that govern precursor renewal and local growth. Here, transient 3D CFD is coupled with spatially resolved characterization to examine how substrate inclination reorganizes near-wall transport in a hot-wall quartz-tube LPCVD reactor and its influence on graphene thickness uniformity. Across four substrate tilt angles (9°, 21°, 33°, and 45°), uniform coatings emerge not from maximizing flow intensity, but from establishing a laterally distributed near-surface transport field without significant downstream shielding. Shallow inclination (9°) produces weak surface-parallel transport and thicker boundary layers. In contrast, steep inclination (45°) induces strong but highly localized acceleration followed by wake-driven transport heterogeneity. Intermediate inclinations (21°-33°) yield more balanced near-surface velocity and wall shear stress distributions, promoting spatially uniform precursor renewal across the substrate surface. Raman mapping and SEM-based morphology analysis corroborate these transport trends, confirming reduced spatial segregation and improved thickness coherence within the 21°-33° inclination window. These findings establish a transport-based framework for interpreting substrate orientation effects in LPCVD graphene growth and provide reactor-level guidance for achieving uniform coatings in comparable hot-wall LPCVD systems.

Keywords
chemical vapor deposition (CVD) computational fluid dynamics (CFD) graphene coating raman spectroscopy scanning electron microscopy (SEM) wall shear stress (WSS)
Article Info
Journal
Small (Weinheim an der Bergstrasse, Germany)
Abbr.
Small
ISSN
1613-6829
Published
2026-06-00
Language
English
Country/Region
Germany
NLM ID
101235338
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