RT info:eu-repo/semantics/article
T1 Synergistic Density Functional Theory and Molecular Dynamics Approach to Elucidate PNIPAM–Water Interaction Mechanisms
A1 Alomari, Noor
A1 Aparicio Martínez, Santiago
A1 Meyer, Paul
A1 Zeng, Yi
A1 Cui, Shuang
A1 Gutiérrez Vega, Alberto
A1 Atilhan, Mert
K1 Poly(N-isopropylacrylamide) (PNIPAM)
K1 Water sorption
K1 Hydrogen bonding interactions
K1 DFT calculations
K1 MD(LAMMPS) simulations
K1 lower critical solution temperature
K1 Dinámica molecular
K1 Molecular dynamics
AB This study employs Density Functional Theory (DFT) and Molecular Dynamics (MD) simulations to investigate interactions between water molecules and Poly(N-isopropylacrylamide) (PNIPAM). DFT reveals preferential water binding sites, with enhanced binding energy observed in the linker zone. Quantum Theory of Atoms in Molecules (QTAIM) and electron localization function (ELF) analyses highlight the roles of hydrogen bonding and steric hindrance. MD simulations unveil temperature-dependent hydration dynamics, with structural transitions marked by changes in the radius of gyration (Rg) and the radial distribution function (RDF), aligning with DFT findings. Our work goes beyond prior studies by combining a DFT, QTAIM and MD simulations approach across different PNIPAM monomer-to-30mer structures. It introduces a systematic quantification of pseudo-saturation thresholds and explores water clustering dynamics with structural specificity, which have not been previously reported in the literature. These novel insights establish a more complete molecular-level picture of PNIPAM hydration behavior and temperature responsiveness, emphasizing the importance of amide hydrogen and carbonyl oxygen sites in hydrogen bonding, which weakens above the lower critical solution temperature (LCST), resulting in increased hydrophobicity and paving the way for understanding water sorption mechanisms, offering guidance for future applications such as dehumidification and atmospheric water harvesting.
PB MDPI
YR 2025
FD 2025-05
LK https://hdl.handle.net/10259/12028
UL https://hdl.handle.net/10259/12028
LA eng
NO This research 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 number DE-EE0010200. This work was also supported through computational resources and services provided by the Institute for Cyber-Enabled Research at Michigan State University. Author Alberto Gutiérrez received grant BG22/00089, funded by Spanish Ministerio de Universidades. And Shuang Cui also acknowledges the support from the University of Texas at Dallas startup fund and the National Science Foundation (grant No. 2318720). The statements made herein are solely the responsibility of the authors. This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Support for the work was also provided by the Industrial Efficiency and Decarbonization Office under Award Number DE-EE0010200. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or to allow others to do so, for U.S. Government purposes.
DS Repositorio Institucional de la Universidad de Burgos
RD 10-sep-2026