Machine Learning of Microscopic Ingredients for Graphene Oxide/Cellulose Interaction
Understanding the role of microscopic attributes in nanocomposites allows one to control and, therefore, accelerate experimental system designs. In this work, we extracted the relevant parameters controlling the graphene oxide binding strength to cellulose by combining first-principles calculations...
| Veröffentlicht in: | Langmuir : the ACS journal of surfaces and colloids. - 1999. - 38(2022), 3 vom: 25. Jan., Seite 1124-1130 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | Langmuir : the ACS journal of surfaces and colloids |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't graphene oxide Graphite 7782-42-5 Cellulose 9004-34-6 |
| Zusammenfassung: | Understanding the role of microscopic attributes in nanocomposites allows one to control and, therefore, accelerate experimental system designs. In this work, we extracted the relevant parameters controlling the graphene oxide binding strength to cellulose by combining first-principles calculations and machine learning algorithms. We were able to classify the systems among two classes with higher and lower binding energies, which are well defined based on the isolated graphene oxide features. Using theoretical X-ray photoelectron spectroscopy analysis, we show the extraction of these relevant features. In addition, we demonstrate the possibility of refined control within a machine learning regression between the binding energy values and the system's characteristics. Our work presents a guiding map to control graphene oxide/cellulose interaction |
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| Beschreibung: | Date Completed 01.02.2022 Date Revised 01.02.2022 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1520-5827 |
| DOI: | 10.1021/acs.langmuir.1c02780 |