RT info:eu-repo/semantics/article
T1 Molecular dynamics study on the interfacial properties of mixtures of monomers of polyvinylpyrrolidone (PVP)-based battery binders on graphene and graphite surfaces
A1 Gutiérrez Vega, Alberto
A1 Aparicio Martínez, Santiago
A1 Pekarovicova, Alexandra
A1 Wu, Qingliu
A1 Atilhan, Mert
K1 Density functional theory
K1 Molecular dynamics
K1 Knowledge representation
K1 Graphene
K1 Carbon based materials
K1 Electrolytes
K1 Interfacial properties
K1 Batteries
K1 Polymers
K1 Chemical bonding
K1 Dinámica molecular
K1 Molecular dynamics
AB This study investigates the behavior of two different mixtures of monomers of polyvinylpyrrolidone (PVP)-based battery binders, polyvinylpyrrolidone:polyvinylidene difluoride (PVP:PVDF) and polyvinylpyrrolidone:polyacrylic acid (PVP:PAA), at graphene and graphite interfaces using classical molecular dynamics simulations. The aim is to identify the best performing monomer binder blend and carbon-based material for the design of battery-optimized energy devices. The PVP:PAA monomer binder blend and graphite are found to have the best interaction energies, densification upon adsorption, and more ordered structure. The adsorption of both monomer binder blends is strongly guided by the higher affinity of PVP and PAA monomeric molecules for the surfaces compared to PVDF. The structure of adsorbed layers of PVP:PVDF monomer binder blend on graphene and graphite develops more quickly than PVP:PAA, indicating faster kinetics. This study complements a previous density functional theory study recently reported by our group and contributes to a better understanding of the nanoscopic features of relevant interfacial regions involving mixtures of monomers of PVP-based battery binders and different carbon-based materials. The effect of a blend of commonly used monomer binders on carbon-based materials is essential for obtaining tightly bound anode and cathode active materials in lithium-ion batteries, which is crucial for designing battery-optimized energy devices.
PB AIP Publishing
SN 0021-9606
YR 2023
FD 2023-07
LK https://hdl.handle.net/10259/12052
UL https://hdl.handle.net/10259/12052
LA eng
NO This study is based upon work supported by the U.S. Department of Energy’s Office on Energy Efficiency and Renewable Energy (EERE) under the Advanced Manufacturing Office, Award No. DE-EE0009111. We also acknowledge the European Union NextGenerationEU/PRTR funds. This work was also supported through computational resources and services provided by the Institute for Cyber-Enabled Research at Michigan State University and SCAYLE (Supercomputación Castilla y León, Spain). The statements made herein are solely the responsibility of the authors.
DS Repositorio Institucional de la Universidad de Burgos
RD 10-sep-2026