AI Article Synopsis
- Understanding microscopic attributes in nanocomposites helps improve and speed up system designs.
- This study focuses on the binding strength between graphene oxide and cellulose, utilizing first-principles calculations combined with machine learning.
- We classify the systems by binding energies and validate our findings through theoretical X-ray photoelectron spectroscopy, ultimately offering a framework for better control of graphene oxide/cellulose interactions.
Article Abstract
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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| http://dx.doi.org/10.1021/acs.langmuir.1c02780 | DOI Listing |
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