arXiv subjects

Giancarlo Franzese

Publications and source records attributed to Giancarlo Franzese.

At least 19 recordsLinked to original sources

arXiv2026

Many-body contributions to polymorphism and polyhexaticity in a water monolayer

Nanoconfined water plays a crucial role in nanofluidics, biology, and cutting-edge technologies. The process of melting water monolayers and quasi-two-dimensional confined water involves, as an intermediate stage, the hexatic phase--a state that lies between solid and liquid and is characterized by quasi-long-range orientational order and short-range translational order. However, the influence of hydrogen bond (HB) cooperativity in this process has not been thoroughly investigated. This gap hampers our understanding of the phase behavior of confined water and limits the accuracy of our models. To address this, we extend the water model developed by Franzese and Stanley, which explicitly includes many-body interactions (MBIs) of HBs. We distinguish the contributions of three-body and five-body HB-MBIs. Our Monte Carlo calculations in the isobaric-isothermal ensemble produces a detailed pressure-temperature phase diagram, revealing polymorphism and polyhexaticity: low-density square ice and high-density triangular ice are separated from the liquid phase by distinct hexatic phases. Three-body interactions notably promote crystallization and can destabilize the low-density hexatic phase, while cooperative five-body interactions help restore it, thus modifying the thermodynamic landscape. These findings demonstrate that HB-MBIs are key in determining the phase behavior of confined water, influencing phenomena such as the non-monotonic specific heat, maximum density lines, and the accessibility of the liquid-liquid critical point. Beyond advancing theoretical understanding, these results have wide-ranging implications for nanofluidics, interfacial science, and applications in biology, food technology, and pharmaceutics, where controlling water under confinement is essential.

cond-mat.soft↗

arXiv2026

Contribution of Water to Pressure and Cold Denaturation of Proteins

The mechanisms of cold- and pressure-denaturation of proteins are matter of debate and are commonly understood as due to water-mediated interactions. Here we study several cases of proteins, with or without a unique native state, with or without hydrophilic residues, by means of a coarse-grain protein model in explicit solvent. We show, using Monte Carlo simulations, that taking into account how water at the protein interface changes its hydrogen bond properties and its density fluctuations is enough to predict protein stability regions with elliptic shapes in the temperature-pressure plane, consistent with previous theories. Our results clearly identify the different mechanisms with which water participates to denaturation and open the perspective to develop advanced computational design tools for protein engineering.

physics.bio-ph↗

arXiv2025

A Machine Learning Tool to Analyse Spectroscopic Changes in High-Dimensional Data

When nanoparticles (NPs) are introduced into a biological solution, layers of biomolecules form on their surface, creating a corona. Understanding how the structure of the protein evolves into the corona is essential for evaluating the safety and toxicity of nanotechnology. However, the influence of NP properties on protein conformation is not well understood. In this study, we propose a new method that addresses this issue by analyzing multi-component spectral data using Machine Learning (ML). We apply the method to fibrinogen, a crucial protein in human blood plasma, at physiological concentrations while interacting with hydrophobic carbon or hydrophilic silicon dioxide NPs, revealing striking differences in the temperature dependence of the protein structure between the two cases. Our unsupervised ML method a) does not suffer from the challenges associated with the curse of dimensionality, and b) simultaneously handles spectral data from various sources. The method offers a quantitative analysis of protein structural changes upon adsorption and enhances the understanding of the correlation between protein structure and NP interactions, which could support the development of nanomedical tools to treat various conditions.

cond-mat.stat-mech↗

arXiv2025

Unveiling the entropic role of hydration water in SOD1 partitioning within FUS condensate

Biological processes like the sequestration of Superoxide Dismutase 1 (SOD1) into biomolecular condensates such as FUS and stress granules are essential to understanding disease mechanisms, including amyotrophic lateral sclerosis (ALS). Our study demonstrates that the hydration environment is crucial in these processes. Using the advanced CVF water model, which captures hydrogen-bond networks at the molecular level, we show how water greatly impacts SOD1's behavior, residency times, and transition rates between different associative states. Importantly, when water is included to hydrate an implicit solvent model (OPEP), we gain a new perspective on the free energy landscape of the system, leading to a conclusion that clarifies that suggested by OPEP alone. While the OPEP model indicated that Bovine Serum Albumin (BSA) crowders reduce SOD1's partition coefficient (PC) mainly due to nonspecific interactions with BSA, our enhanced explicit-water approach reveals that the hydration entropy behavior in BSA drives the observed decrease in PC. This highlights that explicitly modeling water is essential for accurately understanding protein-crowder interactions and their biological relevance, emphasizing water's role in cellular phase separation and disease-related processes.

cond-mat.stat-mech↗

arXiv2025

Efficient parallel algorithms for free-energy calculation of millions of water molecules in the fluid phases

Simulating water droplets made up of millions of molecules and on timescales as needed in biological and technological applications is challenging due to the difficulty of balancing accuracy with computational capabilities. Most detailed descriptions, such as ab initio, polarizable, or rigid models, are typically constrained to a few hundred (for ab initio) or thousands of molecules (for rigid models). Recent machine learning approaches allow for the simulation of up to 4 million molecules with ab initio accuracy but only for tens of nanoseconds, even if parallelized across hundreds of GPUs. In contrast, coarse-grained models permit simulations on a larger scale but at the expense of accuracy or transferability. Here, we consider the CVF molecular model of fluid water, which bridges the gap between accuracy and efficiency for free-energy and thermodynamic quantities due to i) a detailed calculation of the hydrogen bond contributions at the molecular level, including cooperative effects, and ii) coarse-graining of the translational and rotational degrees of freedom of the molecules. The CVF model can reproduce the experimental equation of state and fluctuations of fluid water across a temperature range of 60 degrees around ambient temperature and from 0 to 50 MPa. In this work, we describe efficient parallel Monte Carlo algorithms executed on GPUs using CUDA, tailored explicitly for the CVF model. We benchmark accessible sizes of 17 million molecules with the Metropolis and 2 million with the Swendsen-Wang Monte Carlo algorithm.

cond-mat.stat-mech↗

arXiv2025

Characterizing the Hard and Soft Nanoparticle-Protein Corona with Multilayer Adsorption

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.

cond-mat.soft↗

arXiv2024

Emergence of Disordered Hyperuniformity in Confined Fluids and Soft Matter

Confinement can significantly alter fluid properties, offering potential for specific technological applications. However, achieving precise control over the structural complexity of confined fluids and soft matter remains challenging, as it often requires careful tuning of system parameters. In this study, we perform large-scale molecular dynamics simulations of a fluid confined in a slit pore, employing an interaction potential applicable to a broad range of soft matter. Confinement induces the fluid to form two-dimensional (2D) layers, each of which self-assembles into structures of varying complexity. Notably, we observe a suppression of large-scale density fluctuations-akin to crystals-within complex nontrivial fluid-like geometries, leading to the emergence of disordered hyperuniformity. We demonstrate that fluid properties can be precisely adjusted by modifying control parameters. This work provides a foundation for designing experimental protocols to regulate the structure of self-assembling materials, stabilize disordered hyperuniform structures, and facilitate the fabrication of 2D arrays with tailored complexity. Additionally, it offers a new perspective for analyzing biological monolayers, such as epithelial tissues.

cond-mat.soft↗

arXiv2024

Phase behavior of metastable water from large-scale simulations of a quantitative accurate model: The liquid-liquid critical point

Water's unique anomalies are vital in various applications and biological processes, yet the molecular mechanisms behind these anomalies remain debated, particularly in the metastable liquid phase under supercooling and stretching conditions. Experimental challenges in these conditions have led to simulations suggesting a liquid-liquid phase transition between low-density and high-density water phases, culminating in a liquid-liquid critical point (LLCP). However, these simulations are limited by computational expense, small system sizes, and reliability of water models. Using the FS model, we improve accuracy in predicting water's density and response functions across a broad range of temperatures and pressures. The FS model avoid by design first-order phase transitions towards crystalline phases, allowing thorough exploration of the metastable phase diagram. We employ advanced numerical techniques to bypass dynamical slowing down and perform finite-size scaling on systems significantly larger than those used in previous analyses. Our study extrapolates thermodynamic behavior in the infinite-system limit, accurately demonstrating the existence of the LLCP in the 3D Ising universality class at TC = 186 +/- 4 K and PC = 174 +/- 14 MPa, following a liquid-liquid phase separation below 200 MPa. These predictions align with recent experimental data and more sophisticated models, highlighting that hydrogen bond cooperativity governs the LLCP and the origin of water anomalies. Moreover, we observe that the hydrogen bond network exhibits substantial cooperative fluctuations at scales larger than 10 nm, even at temperatures relevant to biopreservation. These findings have significant implications for fields such as nanotechnology and biophysics, offering new insights into water's behavior under varied conditions.

cond-mat.stat-mech↗

arXiv2024

Small-Angle X-ray Scattering Unveils the Internal Structure of Lipid Nanoparticles

Lipid nanoparticles own a remarkable potential in nanomedicine, only partially disclosed. While the clinical use of liposomes and cationic lipid-nucleic acid complexes is well-established, liquid lipid nanoparticles (nanoemulsions), solid lipid nanoparticles, and nanostructured lipid carriers have even greater potential. However, they face obstacles in being used in clinics due to a lack of understanding about the molecular mechanisms controlling their drug loading and release, interactions with the biological environment (such as the protein corona), and shelf-life stability. To create effective drug delivery carriers and successfully translate bench research to clinical settings, it is crucial to have a thorough understanding of the internal structure of lipid nanoparticles. Through synchrotron small-angle X-ray scattering experiments, we determined the spatial distribution and internal structure of the nanoparticles' lipid, surfactant, and the water in them. The nanoparticles themselves have a barrel-like shape that consists of coplanar lipid platelets (specifically cetyl palmitate) that are partially covered by polysorbate 80 surfactant and retain a small amount of hydration water. Although the platelet structure was expected, the presence of surfactant molecules forming sticky patches between adjacent platelets challenges the classical core-shell model used to describe solid lipid nanoparticles. Additionally, the surfactant partially covers the water-nanoparticle interface, allowing certain lipid regions to come into direct contact with surrounding water. These structural features play a significant role in drug loading and release, biological fluid interaction, and nanoparticle stability, making these findings valuable for the rational design of lipid-based nanoparticles.

cond-mat.soft↗

arXiv2023

Water-methanol mixture confined in a graphene slit-pore

Efficient and sustainable techniques for separating water-methanol mixtures are in high demand in the industry. Recent studies have revealed that membranes and 2D materials could achieve such separation. In our research, we explore the impact of a nanoconfining graphene slit-pore on the dynamics and structure of water-methanol mixtures. By Molecular Dynamics simulations of a coarse-grained model for water mixtures containing up to 25% methanol, we show that, for appropriate pore sizes, water tends to occupy the center of the pore. In contrast, methanol's apolar moiety accumulates near the hydrophobic walls. Additionally, modifying the pore's width leads to a non-monotonic change in the diffusivity of each component. However, water always diffuses faster than methanol, implying that it should be possible to identify an optimal configuration for water-methanol separation based on physical mechanisms. Our calculations indicate that one of the more effective pore sizes, 12.5Å, is also mechanically stable, minimizing the energy cost of a possible filtering membrane.

cond-mat.soft↗

arXiv2023

Hydrophobic homopolymer's coil-globule transition and adsorption onto a hydrophobic surface under different conditions

Unstructured proteins can modulate cellular responses to environmental conditions by undergoing coil-globule transitions and phase separation. However, the molecular mechanisms of these phenomena still need to be fully understood. Here, we use Monte Carlo calculations of a coarse-grained model incorporating water's effects on the system's free energy. Following previous studies, we model an unstructured protein as a polymer chain. Because we are interested in investigating how it responds to thermodynamic changes near a hydrophobic surface under different conditions, we choose an entirely hydrophobic sequence to maximize the interaction with the interface. We show that a slit pore confinement without top-down symmetry enhances the unfolding and adsorption of the chain in both random coil and globular states. Moreover, we demonstrate that the hydration water modulates this behavior depending on the thermodynamic parameters. Our findings provide insights into how homopolymers, and possibly unstructured proteins, can sense and adjust to external stimuli such as nanointerfaces or stresses.

cond-mat.soft↗

arXiv2023

Size-pore-dependent methanol sequestration from water-methanol mixtures by an embedded graphene slit

The separation of liquid mixture components is relevant to many applications -- ranging from water purification to biofuel production -- and is a growing concern related to the UN Sustainable Development Goals (SDGs), such as ``Clean water and Sanitation'' and ``Affordable and clean energy''. One promising technique is using graphene slit-pores as filters, or sponges, because the confinement potentially affects the properties of the mixture components in different ways, favoring their separation. However, no systematic study has shown how the size of a pore changes the thermodynamics of the surrounding mixture. Here, we focus on water-methanol mixtures and explore, using Molecular Dynamics simulations, the effects of a graphene pore, with size ranging from 6.5 to 13 Å, for three compositions: pure water, 90\%-10\%, and 75\%-25\% water-methanol. We show that tuning the pore size can change the mixture pressure, density and composition in bulk due to the size-dependent methanol sequestration within the pore. Our results can help in optimizing the graphene pore size for filtering applications.

cond-mat.mes-hall↗

arXiv2023

Many-Body Contributions in Water Nano-Clusters

Many-body interactions in water are known to be important but difficult to treat in atomistic models and often are included only as a correction. Polarizable models treat them explicitly, with long-range many-body potentials, within their classical approximation. However, their calculation is computationally expensive. Here, we evaluate how relevant the contributions to the many-body interaction associated with different coordination shells are. We calculate the global energy minimum, and the corresponding configuration, for nano-clusters of up to 20 water molecules. We find that including the first coordination shell, i.e., the five-body term of the central molecule, is enough to approximate within 5% the global energy minimum and its structure. We show that this result is valid for three different polarizable models, the Dang-Chang, the MB-pol, and the Kozack-Jordan potentials. This result suggests a strategy to develop many-body potentials for water that are reliable and, at the same time, computationally efficient.

physics.chem-ph↗

arXiv2022

Using Car-Parrinello simulations and microscopic order descriptors to reveal two locally favored structures with distinct molecular dipole moments and dynamics in ambient liquid water

Water is essential for life and technological applications, mainly for its unique thermodynamic and dynamic properties, often anomalous or counterintuitive. These anomalies result from the hydrogen-bonds fluctuations, as evidenced by studies for supercooled water. However, it is difficult to characterize these fluctuations under ambient conditions. Here, we fill this knowledge gap thanks to the Car-Parrinello ab initio molecular dynamics (MD) simulation technique. We calculate the local structural order parameter ζ, quantifying the coordination shells separation, and find two locally-favored structures or states: High-ζ and Low-ζ. On average, High-ζ molecules have a tetrahedral arrangement, with four hydrogen bonds, and the first and the second coordination shell well separated. The Low-ζ molecules are less connected, partially merging the first and the second shells. The appearance of isosbestic points in the radial distribution functions and the collective density fluctuations at different length scales and timescales reveal that the two-state model, consistent with available experimental data for supercooled water, also holds under ambient conditions, as we confirm by analyzing the vibrational spectrum of both types of water molecules. Significant consequences of the structural differences between the two states are that High-ζ molecules have a dipole moment 6 % higher than Low-ζ. At the same time, Low-ζ structures are more disordered and with more significant angular fluctuations. These differences are also reflected in the dynamics under ambient conditions. The Low-ζ molecules decorrelate their reorientation faster than High-ζ and merge their coordination shells within 0.2 ps, while the High-ζ preserve the shell separation for longer times.

cond-mat.soft↗

arXiv2021

Nanoconfined fluids: Uniqueness of water compared to other liquids

Nanoconfinement can drastically change the behavior of liquids, puzzling us with counterintuitive properties. Moreover, it is relevant in applications, including decontamination and crystallization control. It still lacks a systematic analysis for fluids with different bulk properties. Here we fill this gap. We compare, by molecular dynamics simulations, three different liquids in a graphene slit pore: (A) A simple fluid, such as argon, described by a Lennard-Jones potential; (B) An anomalous fluid, such as a liquid metal, modeled with an isotropic core-softened potential; (C) Water, the prototypical anomalous liquid, with directional hydrogen bonds. We study how the slit-pore width affects the structure, thermodynamics, and dynamics of the fluids. We check that all the fluids, as expected, show similar oscillating properties by changing the pore size. However, the nature of the free-energy minima for the three fluids is quite different: i) only for the simple liquid all the minima are energy-driven, while their structural order increases with decreasing slit-pore width; ii) only for the isotropic core-softened potential all the minima are entropy-driven, while the energy in the minima increases with decreasing slit-pore width; iii) only the water has a changing nature of the minima: the monolayer minimum is entropy-driven, at variance with the simple liquid, while the bilayer minimum is energy-driven, at variance with the other anomalous liquid. Also, water diffusion has a large increase for sub-nm slit-pores, becoming faster than bulk. Instead, the other two fluids have diffusion oscillations much smaller than water slowing down for decreasing slit-pore width. Our results clarify that nanoconfined water is unique compared to other (simple or anomalous) fluids under similar confinement, and are possibly relevant in nanopores applications, e.g., in water purification from contaminants.

cond-mat.soft↗

arXiv2021

Water Contribution to the Protein Folding and its Relevance in Protein Design and Protein Aggregation

Water plays a fundamental role in protein stability. However, the effect of the properties of water on the behaviour of proteins is only partially understood. Several theories have been proposed to give insight into the mechanisms of cold and pressure denaturation, or the limits of temperature and pressure above which no protein has a stable, functional state, or how unfolding and aggregation are related. Here we review our results based on a theoretical approach that can rationalise the water contribution to protein solutions' free energy. We show, using Monte Carlo simulations, how we can rationalise experimental data with our recent results. We discuss how our findings can help develop new strategies for the design of novel synthetic biopolymers or possible approaches for mitigating neurodegenerative pathologies.

cond-mat.soft↗

arXiv2021

Water under extreme confinement in graphene: Oscillatory dynamics, structure, and hydration pressure explained as a function of the confinement width

Graphene nanochannels are relevant for their possible applications, as in water purification, and for the challenge of understanding how they change the properties of confined liquids. Here, we use all-atom molecular dynamics simulations to investigate water confined in an open graphene slit-pore as a function of its width $w$, down to sub-nm scale. We find that the water translational and rotational dynamics exhibits an oscillatory dependence on $w$, due to water layering. The oscillations in dynamics correlate with those in hydration pressure, which can be negative (hydrophobic attraction), or as high as $\sim 1$ GPa, as seen in the experiments. At pore widths commensurable with full layers (around $7.0$ Å and $9.5$ Å for one and two layers, respectively), the free energy of the system has minima, and the hydration pressure vanishes. These are the separations at which the dynamics of confined water slows down. Nevertheless, the hydration pressure vanishes also where the free energy has maxima, i.e., for those pore-widths which are incommensurable with the formation of well-separated layers, as $w\simeq 8.0$ Å. Around these values of $w$, the dynamics is faster than in bulk, with water squeezed out from the pore. This behavior has not been observed for simple liquids under confinement, either for water in closed nano-pores. The decomposition of the free energy clarifies the origins of the dynamics speedups and slowdowns. In particular, we find that the nature of the slowdown depends on the number of water layers: for two layers, it is due to the internal energy contribution, as in simple liquids, while for one layer, it has an entropic origin possibly due to the existence of a hydrogen-bond network in water. Our results shed light on the mechanisms ruling the dynamics and thermodynamics of confined water and are a guide for future experiments.

cond-mat.soft↗

arXiv2021

Protein Unfolding and Aggregation near a Hydrophobic Interface

The behavior of proteins near interfaces is relevant for biological and medical purposes. Previous results in bulk show that, when the protein concentration increases, the proteins unfold and, at higher concentrations, aggregate. Here, we study how the presence of a hydrophobic surface affects this course of events. To this goal, we use a coarse-grained model of proteins and study by simulations their folding and aggregation near an ideal hydrophobic surface in an aqueous environment by changing parameters such as temperature and hydrophobic strength, related, e.g., to ions concentration. We show that the hydrophobic surface, as well as the other parameters, affect both the protein unfolding and aggregation. We discuss the interpretation of these results and define future lines for further analysis, with their possible implications in neurodegenerative diseases.

cond-mat.soft↗