JOURNAL ARTICLE

Molecular dynamics simulation of amine formation in plasma-enhanced chemical vapor deposition with hydrocarbon and amino radicals.

  • Published In: Journal of Vacuum Science & Technology: Part A-Vacuums, Surfaces & Films, 2023, v. 41, n. 6. P. 1 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Harumningtyas, Anjar Anggraini; Ito, Tomoko; Isobe, Michiro; Zajíčková, Lenka; Hamaguchi, Satoshi 3 of 3

Abstract

This article focuses on molecular dynamics (MD) simulations investigating the formation of primary amines (−NH₂) in carbon-based polymer films deposited by plasma-enhanced chemical vapor deposition (PECVD) using methane (CH₄) and nitrogen (N₂) gases. The study models the deposition process assuming nitrogen is supplied solely as amino radicals (NH₂) to maximize primary amine content and examines how these radicals interact with the film surface under various ion energies and hydrogen supplies. Results indicate that even under idealized conditions, the primary amine concentration remains low (around 10–12% relative to nitrogen atoms) due to surface reactions transferring hydrogen atoms from NH₂ radicals to surrounding carbon atoms, limiting primary amine retention. These findings align with experimental observations and suggest that increasing NH₂ radical density or hydrogen supply in the plasma does not significantly enhance primary amine content in the deposited films.

Additional Information

  • Source:Journal of Vacuum Science & Technology: Part A-Vacuums, Surfaces & Films. 2023/12, Vol. 41, Issue 6, p1
  • Document Type:Article
  • Subject Area:Chemistry
  • Publication Date:2023
  • ISSN:07342101
  • DOI:10.1116/6.0002978
  • Accession Number:173977139
  • Copyright Statement:Copyright of Journal of Vacuum Science & Technology: Part A-Vacuums, Surfaces & Films is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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