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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1021/acs.langmuir.1c02780
|2 doi
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|a pubmed24n1118.xml
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Petry, Romana
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Machine Learning of Microscopic Ingredients for Graphene Oxide/Cellulose Interaction
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a Date Completed 01.02.2022
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|a Date Revised 01.02.2022
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a 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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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a graphene oxide
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|a Cellulose
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|a 9004-34-6
|2 NLM
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| 700 |
1 |
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|a Silvestre, Gustavo H
|e verfasserin
|4 aut
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|a Focassio, Bruno
|e verfasserin
|4 aut
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1 |
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|a Crasto de Lima, Felipe
|e verfasserin
|4 aut
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1 |
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|a Miwa, Roberto H
|e verfasserin
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1 |
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|a Fazzio, Adalberto
|e verfasserin
|4 aut
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|i Enthalten in
|t Langmuir : the ACS journal of surfaces and colloids
|d 1999
|g 38(2022), 3 vom: 25. Jan., Seite 1124-1130
|w (DE-627)NLM098181009
|x 1520-5827
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|g volume:38
|g year:2022
|g number:3
|g day:25
|g month:01
|g pages:1124-1130
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|u http://dx.doi.org/10.1021/acs.langmuir.1c02780
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