Abstract
Associations between cellulose are important both in biofuel production and in the use of cellulose for biomaterials. Cellulose nanofibers (CNFs) are sustainable, strong, light-weight alternatives to traditional materials in manufacturing, but are challenging to obtain due to irreversible aggregation in solution during preparative fibrillation. Therefore, it is imperative to understand the underlying factors driving aggregation with a view to designing solvents that can effectively compete with interfiber interactions, hence reducing aggregation. Molecular dynamics (MD) simulation at atomic detail can provide useful information on local interactions. However, the length and timescales accessible are too short to fully capture association processes. Here, we provide a method for accessing the longer length and timescales required using coarse-grained (CG) MD simulations with a MARTINI force field to calculate the interaction behavior of CNFs in three selected solvents: NaOH-urea-water, acetone, and neat water. The CG results are consistent with our prior all-atom MD and with previous experimental results. While acetone is found not to be an effective solvent, urea and ionic moieties in NaOH-urea-water not only solvate the fibrils but also improve the confinement of water molecules around them as shown by the solvent residence times and mean-square displacements. Overall, the presence of urea and ions reduces the likelihood of aggregation in multi-CNF systems relative to neat water irrespective of whether the hydrophobic or hydrophilic CNF surfaces are interacting. The CG method shows clear promise for selecting potential high-performance solvents for experimental prioritization in bioenergy and biomaterials research in a relatively fast manner as well as for understanding the aggregation and rheological behavior of CNF-solvent systems.
| Original language | English |
|---|---|
| Journal | Biophysical Journal |
| DOIs | |
| State | Accepted/In press - 2025 |
Funding
This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). We acknowledge the funding from the DOE, Office of Energy Efficiency and Renewable Energy, Advanced Materials and Manufacturing Technologies Office (AMMTO), under Corporate Planning System (CPS) agreements 30534 and 35714, and the University of Maine's Hub & Spoke Sustainable Materials & Manufacturing Alliance for Renewable Technologies (SM2ART) Program with the Oak Ridge National Laboratory. This work was also supported by the US DOE, Office of Science, through the Genomic Science Program, Office of Biological and Environmental Research (contract no. FWP ERKP752). This work was performed as a part of the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program awarded by the DOE. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under contract DE-AC05-00OR22725. The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (https://www.energy.gov/doe-public-access-plan). The authors acknowledge Dr. Shih-Hsien Liu for the all-atom simulations data and discussions. This manuscript has been authored by UT-Battelle, LLC , under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). We acknowledge the funding from the DOE , Office of Energy Efficiency and Renewable Energy , Advanced Materials and Manufacturing Technologies Office (AMMTO), under Corporate Planning System (CPS) agreements 30534 and 35714 , and the University of Maine’s Hub & Spoke Sustainable Materials & Manufacturing Alliance for Renewable Technologies ( SM2ART ) Program with the Oak Ridge National Laboratory . This work was also supported by the US DOE , Office of Science , through the Genomic Science Program , Office of Biological and Environmental Research (contract no. FWP ERKP752 ). This work was performed as a part of the Innovative and Novel Computational Impact on Theory and Experiment ( INCITE ) program awarded by the DOE . This research used resources of the Oak Ridge Leadership Computing Facility , which is a DOE Office of Science User Facility supported under contract DE-AC05-00OR22725 . The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( https://www.energy.gov/doe-public-access-plan ). The authors acknowledge Dr. Shih-Hsien Liu for the all-atom simulations data and discussions.