Multiscale Computational Study of Cellulose Acetate-Water Miscibility: Insights from Molecularly Informed Field-Theoretic Modeling.

Cellulose acetate (CA), a prominent water-soluble derivative of cellulose, is a promising biodegradable ingredient that has applications in films, membranes, fibers, drug delivery, and more. In this work, we present a molecularly informed field-theoretic model for CA to explore its phase behavior in aqueous solutions. By integrating atomistic details into large-scale field-theoretic simulations via the relative entropy coarse-graining framework, our approach enables efficient calculations of CA's miscibility window as a function of the degree of substitution (DS) of cellulose hydroxyl groups with acetate side chains. This allows us to capture the intricate phase behavior of CA, particularly its unique miscibility at intermediate substitution, without relying on experimental input. Additionally, the model directly probes CA solution behavior specific to the relative DS at C2, C3, and C6 alcohol sites, providing insights for the rational design of water-soluble CA for diverse applications. This work demonstrates a promising integration of molecularly informed field theories, complementing wet-lab experimentation, for engineering the next-generation polymeric materials with precisely tailored properties.

Molecularly informed field theory for estimating critical micelle concentrations of intrinsically disordered protein surfactants.

The critical micelle concentration (CMC) is a crucial parameter in understanding the self-assembly behavior of surfactants. In this study, we combine simulation and experiment to demonstrate the predictive capability of molecularly informed field theories in estimating the CMC of biologically based protein surfactants. Our simulation approach combines the relative entropy coarse-graining of small-scale atomistic simulations with large-scale field-theoretic simulations, allowing us to efficiently compute the free energy of micelle formation necessary for the CMC calculation while preserving chemistry-specific information about the underlying surfactant building blocks. We apply this methodology to a unique intrinsically disordered protein platform capable of a wide variety of tailored sequences that enable tunable micelle self-assembly. The computational predictions of the CMC closely match experimental measurements, demonstrating the potential of molecularly informed field theories as a valuable tool to investigate self-assembly in bio-based macromolecules systematically.