Atomic-network conditions for bulk-modulus enhancement in amorphous SiOC through carbon-clustering suppression: Molecular dynamics comparison of CC repulsion and nitrogen co-doping

We performed molecular dynamics simulations to examine whether suppressing carbon clustering can enhance the bulk modulus of amorphous SiOC and to identify the atomic-network conditions responsible for this enhancement. Two types of models were compared under O-rich and O-poor stoichiometries: a-SiOC with an artificial short-range C C repulsion constraint (SiOC-CCRep) and equal-C/N-co-doped a-SiOCN. Under O-poor conditions, the bulk modulus of SiOC-CCRep reaches about 165 GPa at the carbon-rich end, substantially above the value of about 135 GPa for conventional melt–quench a-SiOC. This enhancement is associated with an increased total bond density and the incorporation of carbon mainly into Si-rich fourfold coordination environments. Under O-rich conditions, carbon remains in oxygen-containing low-coordination environments, and no comparable enhancement occurs. For the equal-C/N a-SiOCN compositions examined, C C bonding is reduced at higher carbon fractions, but the total bond density and bulk modulus remain close to those of conventional a-SiOC because Si-rich fourfold carbon environments do not become dominant. These results show that suppressing C C bonding enhances the bulk modulus only when it increases the total bond density by promoting carbon incorporation into Si-rich fourfold coordination environments.

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Publication Details

Journal
Computational Materials Science
Published
2026-10-29
DOI
https://doi.org/10.1016/j.commatsci.2026.115193
Primary Topic
Advanced ceramic materials synthesis
Type
article
Field-Weighted Citation Impact
0.00

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article

Atomic-network conditions for bulk-modulus enhancement in amorphous SiOC through carbon-clustering suppression: Molecular dynamics comparison of CC repulsion and nitrogen co-doping

Satoshi IZUMI, Hiroki Sakakima, Sakurako Miyazaki, Keigo Ogawa
1 links
Computational Materials Science
Advanced ceramic materials synthesis
article

Atomic-network conditions for bulk-modulus enhancement in amorphous SiOC through carbon-clustering suppression: Molecular dynamics comparison of CC repulsion and nitrogen co-doping

Satoshi IZUMI, Hiroki Sakakima, Sakurako Miyazaki, Keigo Ogawa
article en

Abstract

We performed molecular dynamics simulations to examine whether suppressing carbon clustering can enhance the bulk modulus of amorphous SiOC and to identify the atomic-network conditions responsible for this enhancement. Two types of models were compared under O-rich and O-poor stoichiometries: a-SiOC with an artificial short-range C C repulsion constraint (SiOC-CCRep) and equal-C/N-co-doped a-SiOCN. Under O-poor conditions, the bulk modulus of SiOC-CCRep reaches about 165 GPa at the carbon-rich end, substantially above the value of about 135 GPa for conventional melt–quench a-SiOC. This enhancement is associated with an increased total bond density and the incorporation of carbon mainly into Si-rich fourfold coordination environments. Under O-rich conditions, carbon remains in oxygen-containing low-coordination environments, and no comparable enhancement occurs. For the equal-C/N a-SiOCN compositions examined, C C bonding is reduced at higher carbon fractions, but the total bond density and bulk modulus remain close to those of conventional a-SiOC because Si-rich fourfold carbon environments do not become dominant. These results show that suppressing C C bonding enhances the bulk modulus only when it increases the total bond density by promoting carbon incorporation into Si-rich fourfold coordination environments.

Computational Materials ScienceVol. 276
The University of Tokyo (JP)
Japan Society for the Promotion of Science
Openalex Percentile: Top 26%
Advanced ceramic materials synthesis
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Atomic-network conditions for bulk-modulus enhancement in amorphous SiOC through carbon-clustering suppression: Molecular dynamics comparison of CC repulsion and nitrogen co-doping — Satoshi IZUMI, Hiroki Sakakima, et al. · Computational Materials Science (2026) | TGRS Research Map | TGRS