Thermodynamic and Transport Properties of Binary Mixtures of Polyethylene and Higher n-Alkanes from Physics-Informed and Machine-Learned Models
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909170345705472 |
|---|---|
| author | Ley-Flores, Maria Alessandri, Riccardo Najmi, Sean Valsecchi, Michele Jackson, George Galindo, Amparo Korley, LaShanda Vlachos, Dionisios G. de Pablo, Juan J. |
| author_facet | Ley-Flores, Maria Alessandri, Riccardo Najmi, Sean Valsecchi, Michele Jackson, George Galindo, Amparo Korley, LaShanda Vlachos, Dionisios G. de Pablo, Juan J. |
| contents | The thermodynamics and transport properties of polymeric materials are essential for the design of reactors and for the development of polymer deconstruction processes. Existing property prediction tools such as correlations based on entropy scaling, kinetic gas theory, and free-volume model are inadequate for polymers. In this paper, we introduce a data-driven model for polyolefins based on data from molecular dynamics simulations that can accurately predict the transport properties of polyethylenes and their binary mixtures with higher n-alkanes across a range of temperatures, pressures, concentrations, and oligomer molecular weights. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Thermodynamic and Transport Properties of Binary Mixtures of Polyethylene and Higher n-Alkanes from Physics-Informed and Machine-Learned Models Ley-Flores, Maria Alessandri, Riccardo Najmi, Sean Valsecchi, Michele Jackson, George Galindo, Amparo Korley, LaShanda Vlachos, Dionisios G. de Pablo, Juan J. Soft Condensed Matter The thermodynamics and transport properties of polymeric materials are essential for the design of reactors and for the development of polymer deconstruction processes. Existing property prediction tools such as correlations based on entropy scaling, kinetic gas theory, and free-volume model are inadequate for polymers. In this paper, we introduce a data-driven model for polyolefins based on data from molecular dynamics simulations that can accurately predict the transport properties of polyethylenes and their binary mixtures with higher n-alkanes across a range of temperatures, pressures, concentrations, and oligomer molecular weights. |
| title | Thermodynamic and Transport Properties of Binary Mixtures of Polyethylene and Higher n-Alkanes from Physics-Informed and Machine-Learned Models |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2404.09676 |