The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Chemical compositions of improved model asphalt systems for molecular simulations
Open accessAbstract
New chemical compositions of model asphalts to represent the AAA-1, AAK-1 and AAM-1 asphalts of the Strategic Highway Research Program are proposed to enable molecular simulations that can further an understanding of asphalt physical, rheological and mechanical properties. Molecules for the three model asphalt systems were selected from compounds that had been identified in petroleum and geochemistry studies available in the literature. Choosing larger molecules than in past model asphalt systems improved agreement with size characterization data. The molecules were classified into saturates, naphthene aromatics, polar aromatics and asphaltenes using the Hansen solubility parameters, size and functional groups. Quantum mechanics was used for determining force field parameters and partial charges from electrostatic potential. Classical molecular dynamics simulations were used to calculate physical properties for one of the model asphalt systems. Densities for the model AAA-1 asphalt system are closer to experimental data compared to previous model systems, with good agreement for thermal expansion coefficient. These studies and results provide an improved tool for relating asphalt chemical compositions to rheological and mechanical properties.
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Metadata
- Authors
- Li, D. D., & Greenfield, M. L.
- Venue
- Fuel
- Publisher
- Elsevier BV
- Published
- 2014-01
- Volume
- 115
- Pages
- 347-356
- ISSN
- 0016-2361 (ISSN-L)
- OpenAlex
- W2071675580
- Cited by
- 1,114
- References
- 30
- FWCI
- 28.29
- Type
- journal article
- Language
- en
- Open access
- green
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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.