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Cited by 229

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

An optimized molecular potential for carbon dioxide

Zhang, Zhigang, Duan, Zhenhao·2005·The Journal of Chemical Physics

Abstract

An optimized molecular potential model for carbon dioxide is presented in this paper. Utilizing the established techniques of molecular-dynamics and histogram reweighting grand canonical Monte Carlo simulations, this model is demonstrated to show excellent predictability for thermodynamic, transport, and liquid structural properties in a wide temperature-pressure range with remarkable accuracies. The average deviations of this new model from experimental data for the saturated liquid densities, vapor densities, vapor pressures, and heats of vaporization are around 0.1%, 2.3%, 0.7%, and 1.9%, respectively. The calculated critical point is almost pinpointed by the new model. The experimental radial distribution functions ranging from 240.0 to 473.0 K are well reproduced as compared to neutron-diffraction measurements. The predicted self-diffusion coefficients are in good agreement with the nuclear-magnetic-resonance measurements. The previously published potential models for CO2 are also systematically evaluated, and our proposed new model is found to be superior to the previous models in general.

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Crossref · 10.1063/1.1924700

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Metadata

Authors
Zhang, Z., & Duan, Z.
Venue
The Journal of Chemical Physics
Published
2005-06-01
Volume
122
Issue
21
Pages
214507-214507
ISSN
  • 0021-9606 (ISSN-L)
  • 1089-7690
OpenAlex
W2071379270
Cited by
229
References
2
FWCI
3.61
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An optimized molecular potential for carbon dioxide. https://app.cochat.ai/papers/10.1063/1.1924700
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Zhang, Z., & Duan, Z. (2005). An optimized molecular potential for carbon dioxide. The Journal of Chemical Physics. https://doi.org/10.1063/1.1924700

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.