Oriol Vilanova

s) and (ii) its final composition for silica NPs in a model plasma made of three blood proteins (human serum albumin, transferrin, and fibrinogen). When computer simulations are calibrated by experimental protein-NP binding affinities measured in single-protein solutions, the theoretical model correctly reproduces competitive protein replacement as proven by independent experiments. When we change the order of administration of the three proteins, we observe a memory effect in the final corona composition that we can explain within our model. Our combined experimental and computational approach is a step toward the development of systematic prediction and control of protein-NP corona composition based on a hierarchy of equilibrium protein binding constants.

Nanoparticles (NPs) in contact with biological fluid adsorb biomolecules into a corona. This corona comprises proteins that strongly bind to the NP (hard corona) and loosely bound proteins (soft corona) that dynamically exchange with the surrounding solution. While the kinetics of hard corona formation is relatively well understood, thanks to experiments and robust simulation models, the experimental characterization and simulation of the soft corona present a more significant challenge. Here, we review the current state of the art in soft corona characterization and introduce a novel open-source computational model to simulate its dynamic behavior, for which we provide the documentation. We focus on the case of transferrin (Tf) interacting with polystyrene NPs as an illustrative example, demonstrating how this model captures the complexities of the soft corona and offers deeper insights into its structure and behavior. We show that the soft corona is dominated by a glassy evolution that we relate to crowding effects. This work advances our understanding of the soft corona, bridging experimental limitations with improved simulation techniques.

Water is essential for life, and its unique properties present significant scientific challenges because of our limited understanding of its thermodynamic behavior. This knowledge gap hinders the accurate theoretical replication of water's properties across various temperatures and pressures, mainly due to the complex quantum nature of its many-body interactions. To address this challenge, we developed a novel molecular model for bulk liquid water that focuses on the hydrogen bond network and its cooperativity. We show that these factors are crucial to controlling water's thermodynamics. Our study introduces an innovative strategy to derive many-body parameters from quantum calculations, validated by advanced polarizable models and calibrated with experimental data under ambient conditions. Our results demonstrate that this model accurately predicts water's equation of state and response functions over a temperature range of approximately 60 degrees at atmospheric pressure and around 40 degrees up to 50 MPa. This quantitative validation underscores the model's reliability and transferability, providing new insights into water's cooperative fluctuations across a broader range of thermodynamic conditions than previously achieved. Moreover, our model's computational efficiency allows for scalability in simulating water droplets nearing micrometer sizes without extensive computational resources or long simulation times. This breakthrough holds significant theoretical and technological implications, opening avenues for advanced research across various scientific fields and applications. • The CVF water model includes cooperativity with ab initio-based parametrization. • Reliable: thermodynamically accurate for liquid water up to 50 MPa. • Efficient: for parallel Monte Carlo algorithms. • Scalable: up to 10 million water molecules on a GPU. • Transferable: even in the supercooled region.