Enhancing ReaxFF for molecular dynamics simulations of lithium-ion batteries: an interactive reparameterization protocol.

Lithium-ion batteries (LIBs) have become an essential technology for the green economy transition, as they are widely used in portable electronics, electric vehicles, and renewable energy systems. The solid-electrolyte interphase (SEI) is a key component for the correct operation, performance, and safety of LIBs. The SEI arises from the initial thermal metastability of the anode-electrolyte interface, and the resulting electrolyte reduction products stabilize the interface by forming an electrochemical buffer window. This article aims to make a first-but important-step towards enhancing the parametrization of a widely-used reactive force field (ReaxFF) for accurate molecular dynamics (MD) simulations of SEI components in LIBs. To this end, we focus on Lithium Fluoride (LiF), an inorganic salt of great interest due to its beneficial properties in the passivation layer. The protocol relies heavily on various Python libraries designed to work with atomistic simulations allowing robust automation of all the reparameterization steps. The proposed set of configurations, and the resulting dataset, allow the new ReaxFF to recover the solid nature of the inorganic salt and improve the mass transport properties prediction from MD simulation. The optimized ReaxFF surpasses the previously available force field by accurately adjusting the diffusivity of lithium in the solid lattice, resulting in a two-order-of-magnitude improvement in its prediction at room temperature. However, our comprehensive investigation of the simulation shows the strong sensitivity of the ReaxFF to the training set, making its ability to interpolate the potential energy surface challenging. Consequently, the current formulation of ReaxFF can be effectively employed to model specific and well-defined phenomena by utilizing the proposed interactive reparameterization protocol to construct the dataset. Overall, this work represents a significant initial step towards refining ReaxFF for precise reactive MD simulations, shedding light on the challenges and limitations of ReaxFF force field parametrization. The demonstrated limitations emphasize the potential for developing more versatile and advanced force fields to upscale ab initio simulation through our interactive reparameterization protocol, enabling more accurate and comprehensive MD simulations in the future.

Method for predicting the wettability of micro-structured surfaces by continuum phase-field modelling.

Numerical prediction of material properties is attracting the attention of the scientific community and industry because of its usefulness in the design process. In the fields of fluid dynamics and microfluidics, several simulation methods have been proposed and adopted to evaluate the properties of surfaces and material interfaces, thanks to the increasing computational power available. However, despite the efforts made, a general and standardized methodology for implementing such methods is still lacking, thus requiring a trial-and-error approach for each new problem, making them difficult to implement and creating a bottleneck at the initial stage of surface design. Here, we report a validated protocol to evaluate the wettability of micro-structured surfaces with a phase-field model. Summarizing:•Simulating physical phenomena with multi-phase flows and moving contact lines can be a challenging task, due to the coupling among disparate length scales.•Using the Cahn-Hilliard diffuse-interface model, moving contact lines can be extensively investigated, although difficulties may arise when implementing numerical simulations, e.g., model parameter calibration, selection of boundary conditions, post-processing of fluid dynamics/equilibrium.•A method for employing this model and evaluating the physical consistency of the results is proposed here, considering the wettability of micro-structured surfaces as a case study.

Atomistic to Mesoscopic Modelling of Thermophysical Properties of Graphene-Reinforced Epoxy Nanocomposites.

This research addresses the need for a multiscale model for the determination of the thermophysical properties of nanofiller-enhanced thermoset polymer composites. Specifically, we analyzed the thermophysical properties of an epoxy resin containing bisphenol-A diglyceryl ether (DGEBA) as an epoxy monomer and dicyandiamide (DICY) and diethylene triamine (DETA) as cross-linking agents. The cross-linking process occurs at the atomistic scale through the formation of bonds among the reactive particles within the epoxy and hardener molecules. To derive the interatomic coarse-grained potential for the mesoscopic model and match the density of the material studied through atomic simulations, we employed the iterative Boltzmann inversion method. The newly developed coarse-grained molecular dynamics model effectively reproduces various thermophysical properties of the DGEBA-DICY-DETA resin system. Furthermore, we simulated nanocomposites made of the considered epoxy additivated with graphene nanofillers at the mesoscopic level and verified them against continuum approaches. Our results demonstrate that a moderate amount of nanofillers (up to 2 wt.%) increases the elastic modulus and thermal conductivity of the epoxy resin while decreasing the Poisson's ratio. For the first time, we present a coarse-grained model of DGEBA-DICY-DETA/graphene materials, which can facilitate the design and development of composites with tunable thermophysical properties for a potentially wide range of applications, e.g., automotive, aerospace, biomedical, or energy ones.

The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: a machine learning approach.

Hybrid electric vehicles and portable electronic systems use supercapacitors for energy storage owing to their fast charging/discharging rates, long life cycle, and low maintenance. Specific capacitance is regarded as one of the most important performance-related characteristics of a supercapacitor's electrode. In the current study, Machine Learning (ML) algorithms were used to determine the impact of various physicochemical properties of carbon-based materials on the capacitive performance of electric double-layer capacitors. Published experimental datasets from 147 references (4899 data entries) were extracted and then used to train and test the ML models, to determine the relative importance of electrode material features on specific capacitance. These features include current density, pore volume, pore size, presence of defects, potential window, specific surface area, oxygen, and nitrogen content of the carbon-based electrode material. Additionally, categorical variables as the testing method, electrolyte, and carbon structure of the electrodes are considered as well. Among five applied regression models, an extreme gradient boosting model was found to best correlate those features with the capacitive performance, highlighting that the specific surface area, the presence of nitrogen doping, and the potential window are the most significant descriptors for the specific capacitance. These findings are summarized in a modular and open-source application for estimating the capacitance of supercapacitors given, as only inputs, the features of their carbon-based electrodes, the electrolyte and testing method. In perspective, this work introduces a new wide dataset of carbon electrodes for supercapacitors extracted from the experimental literature, also giving an instance of how electrochemical technology can benefit from ML models.

Mesoscopic Modeling and Experimental Validation of Thermal and Mechanical Properties of Polypropylene Nanocomposites Reinforced By Graphene-Based Fillers.

The development of nanocomposites relies on structure-property relations, which necessitate multiscale modeling approaches. This study presents a modeling framework that exploits mesoscopic models to predict the thermal and mechanical properties of nanocomposites starting from their molecular structure. In detail, mesoscopic models of polypropylene (PP)- and graphene-based nanofillers (graphene (Gr), graphene oxide (GO), and reduced graphene oxide (rGO)) are considered. The newly developed mesoscopic model for the PP/Gr nanocomposite provides mechanistic information on the thermal and mechanical properties at the filler-matrix interface, which can then be exploited to enhance the prediction accuracy of traditional continuum simulations by calibrating the thermal and mechanical properties of the filler-matrix interface. Once validated through a dedicated experimental campaign, this multiscale model demonstrates that with the modest addition of nanofillers (up to 2 wt %), the Young's modulus and thermal conductivity show up to 35 and 25% enhancement, respectively, whereas the Poisson's ratio slightly decreases. Among the different combinations tested, the PP/Gr nanocomposite shows the best mechanical properties, whereas PP/rGO demonstrates the best thermal conductivity. This validated mesoscopic model can contribute to the development of smart materials with enhanced mechanical and thermal properties based on polypropylene, especially for mechanical, energy storage, and sensing applications.

Assembling Biocompatible Polymers on Gold Nanoparticles: Toward a Rational Design of Particle Shape by Molecular Dynamics.

Gold nanoparticles (AuNPs) have received great attention in a number of fields ranging from the energy sector to biomedical applications. As far as the latter is concerned, due to rapid renal clearance and a short lifetime in blood, AuNPs are often encapsulated in a poly(lactic-co-glycolic acid) (PLGA) matrix owing to its biocompatibility and biodegradability. A better understanding of the PLGA polymers on the AuNP surface is crucial to improve and optimize the above encapsulation process. In this study, we combine a number of computational approaches to explore the adsorption mechanisms of PLGA oligomers on a Au crystalline NP and to rationalize the PLGA coating process toward a more efficient design of the NP shape. Atomistic simulations supported by a recently developed unsupervised machine learning scheme show the temporal evolution and behavior of PLGA clusterization by tuning the oligomer concentration in aqueous solutions. Then, a detailed surface coverage analysis coupled with free energy landscape calculations sheds light on the anisotropic nature of PLGA adsorption onto the AuNP. Our results prove that the NP shape and topology may address and privilege specific sites of adsorption, such as the Au {1 1 1} crystal planes in selected NP samples. The modeling-based investigation suggested in this article offers a solid platform to guide the design of coated NPs.

Multi-Scale Modelling of Aggregation of TiO2 Nanoparticle Suspensions in Water.

Titanium dioxide nanoparticles have risen concerns about their possible toxicity and the European Food Safety Authority recently banned the use of TiO2 nano-additive in food products. Following the intent of relating nanomaterials atomic structure with their toxicity without having to conduct large-scale experiments on living organisms, we investigate the aggregation of titanium dioxide nanoparticles using a multi-scale technique: starting from ab initio Density Functional Theory to get an accurate determination of the energetics and electronic structure, we switch to classical Molecular Dynamics simulations to calculate the Potential of Mean Force for the connection of two identical nanoparticles in water; the fitting of the latter by a set of mathematical equations is the key for the upscale. Lastly, we perform Brownian Dynamics simulations where each nanoparticle is a spherical bead. This coarsening strategy allows studying the aggregation of a few thousand nanoparticles. Applying this novel procedure, we find three new molecular descriptors, namely, the aggregation free energy and two numerical parameters used to correct the observed deviation from the aggregation kinetics described by the Smoluchowski theory. Ultimately, molecular descriptors can be fed into QSAR models to predict the toxicity of a material knowing its physicochemical properties, enabling safe design strategies.

A Modeling-Based Design to Engineering Protein Hydrogels with Random Copolymers.

Protein enzymes have shown great potential in numerous technological applications. However, the design of supporting materials is needed to preserve protein functionality outside their native environment. Direct enzyme-polymer self-assembly offers a promising alternative to immobilize proteins in an aqueous solution, achieving higher control of their stability and enzymatic activity in industrial applications. Herein, we propose a modeling-based design to engineering hydrogels of cytochrome P450 and of PETase with styrene/2-vinylpyridine (2VP) random copolymers. By tuning the copolymer fraction of polar groups and of charged groups via quaternization of 2VP for coassembly with cytochrome P450 and via sulfonation of styrene for coassembly with PETase, we provide quantitative guidelines to select either a protein-polymer hydrogel structure or a single-protein encapsulation. The results highlight that, regardless of the protein surface domains, the presence of polar interactions and hydration effects promote the formation of a more elongated enzyme-polymer complex, suggesting a membrane-like coassembly. On the other hand, the effectiveness of a single-protein encapsulation is reached by decreasing the fraction of polar groups and by increasing the charge fraction up to 15%. Our computational analysis demonstrates that the enzyme-polymer assemblies are first promoted by the hydrophobic interactions which lead the protein nonpolar residues to achieve the maximum coverage and to play the role of the most robust contact points. The mechanisms of coassembly are unveiled in the light of both protein and polymer physical-chemistry, providing bioconjugate phase diagrams for the optimal material design.

Machine learning and materials modelling interpretation of in vivo toxicological response to TiO2 nanoparticles library (UV and non-UV exposure).

Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability. In the present study we assessed the effects of a custom design Fe-doped TiO2 NPs library, using the soil invertebrate Enchytraeus crypticus (Oligochaeta), via a 5-day pulse via aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO2, realistic conditions should include UV exposure. The 11 Fe-TiO2 library contains NPs of size range between 5-27 nm with varying %Fe (enabling the photoactivation of TiO2 at energy wavelengths in the visible-light range). The NPs were each described by 122 descriptors, being a mixture of measured and atomistic model descriptors. The data were explored using single and univariate statistical methods, combined with machine learning and multiscale modelling techniques. An iterative pruning process was adopted for identifying automatically the most significant descriptors. TiO2 NPs toxicity decreased when combined with UV. Notably, the short-term water exposure induced lasting biological responses even after longer-term recovery in clean exposure. The correspondence with Fe-content correlated with the band-gap hence the reduction of UV oxidative stress. The inclusion of both measured and modelled materials data benefitted the explanation of the results, when combined with machine learning.

Unfolding the prospects of computational (bio)materials modeling.

In this perspective communication, we briefly sketch the current state of computational (bio)material research and discuss possible solutions for the four challenges that have been increasingly identified within this community: (i) the desire to develop a unified framework for testing the consistency of implementation and physical accuracy for newly developed methodologies, (ii) the selection of a standard format that can deal with the diversity of simulation data and at the same time simplifies data storage, data exchange, and data reproduction, (iii) how to deal with the generation, storage, and analysis of massive data, and (iv) the benefits of efficient "core" engines. Expressed viewpoints are the result of discussions between computational stakeholders during a Lorentz center workshop with the prosaic title Workshop on Multi-scale Modeling and are aimed at (i) improving validation, reporting and reproducibility of computational results, (ii) improving data migration between simulation packages and with analysis tools, (iii) popularizing the use of coarse-grained and multi-scale computational tools among non-experts and opening up these modern computational developments to an extended user community.

Wettability-Engineered Meshes for Gas Microvolume Precision Handling in Liquids.

The interaction of rising gas bubbles with submerged air-repelling or air-attracting surfaces is relevant to various technological applications that rely on gas-microvolume handling or removal. This work demonstrates how submerged metal meshes with super air-attracting/repelling properties can be employed to manipulate microvolumes of air, rising buoyantly in the form of bubbles in water. Superaerophobic meshes are observed to selectively allow the passage of air bubbles depending on the mesh pore size, the bubble volume-equivalent diameter, and the bubble impact velocity on the mesh. On the other hand, superaerophilic meshes reduce or amplify the volume captured from a train of incoming bubbles. Finally, a spatial wettability pattern on the mesh is used to control the size of the outgoing bubble, and an empirical relation is formulated to predict the released gas volume. The study demonstrates how porous materials with controlled wettability can be used to precisely modulate and control the outcome of bubble/mesh interactions.

Multistage and passive cooling process driven by salinity difference.

Space cooling in buildings is anticipated to rise because of an increasing thermal comfort demand worldwide, and this calls for cost-effective and sustainable cooling technologies. We present a proof-of-concept multistage device, where a net cooling capacity and a temperature difference are demonstrated as long as two water solutions at disparate salinity are maintained. Each stage is made of two hydrophilic layers separated by a hydrophobic membrane. An imbalance in water activity in the two layers naturally causes a non-isothermal vapor flux across the membrane without requiring any mechanical ancillaries. One prototype of the device developed a specific cooling capacity of up to 170 W m-2 at a vanishing temperature difference, considering a 3.1 mol/kg calcium chloride solution. To provide perspective, if successfully up-scaled, this concept may help satisfy at least partially the cooling needs in hot, humid regions with naturally available salinity gradients.

Water dynamics affects thermal transport at the surface of hydrophobic and hydrophilic irradiated nanoparticles.

Plasmonic nanoparticles, such as Au nanoparticles (NPs) coated with bio-compatible ligands, are largely studied and tested in nanomedicine for photothermal therapies. Nevertheless, no clear physical interpretation is currently available to explain thermal transport at the nanoparticle surface, where a solid-liquid (core-ligand) interface is coupled to a liquid-liquid (ligand-solvent) interface. This lack of understanding makes it difficult to control the temperature increase imposed by the irradiated NPs to the surrounding biological environment, and it has so far hindered the rational design of the NP surface chemistry. Here, atomistic molecular dynamics simulations are used to show that thermal transport at the nanoparticle surface depends dramatically on solvent diffusivity at the ligand-solvent interface. Furthermore, using physical indicators of water confinement around hydrophobic and hydrophilic ligands, a predictive model is developed to allow the engineering of NP coatings with the desired thermal conductivities at the nanoscale.

Mechanistic correlation between water infiltration and framework hydrophilicity in MFI zeolites.

Hydrophobic zeolites are nanoporous materials that are attracting an increasing interest, especially for catalysis, desalination, energy storage and biomedical applications. Nevertheless, a more profound understanding and control of water infiltration in their nanopores is still desirable to rationally design zeolite-based materials with tailored properties. In this work, both atomistic simulations and previous experimental data are employed to investigate water infiltration in hydrophobic MFI zeolites with different concentration of hydrophilic defects. Results show that limited concentrations of defects (e.g. 1%) induce a change in the shape of infiltration isotherms (from type-V to type-I), which denotes a sharp passage from typical hydrophobic to hydrophilic behavior. A correlation parametrized on both energy and geometric characteristics of the zeolite (infiltration model) is then adopted to interpolate the infiltration isotherms data by means of a limited number of physically-meaningful parameters. Finally, the infiltration model is combined with the water-zeolite interaction energy computed by simulations to correlate the water intrusion mechanism with the atomistic details of the zeolite crystal, such as defects concentration, distribution and hydrophilicity. The suggested methodology may allow a faster (more than one order of magnitude) and more systematic preliminary computational screening of innovative zeolite-based materials for energy storage, desalination and biomedical purposes.

Exploring the Free Energy Landscape To Predict the Surfactant Adsorption Isotherm at the Nanoparticle-Water Interface.

The long-lasting stability of nanoparticle (NP) suspensions in aqueous solution is one of the main challenges in colloidal science. The addition of surfactants is generally adopted to increase the free energy barrier between NPs and hence to ensure a more stable condition avoiding the NP sedimentation. However, a tailored prediction of surfactant concentration enabling a good dispersion of NPs is still an ambitious objective. Here, we demonstrate the efficiency of coupling steered molecular dynamics (SMD) with the Langmuir theory of adsorption in the low surfactant concentration regime, to predict the adsorption isotherm of sodium-dodecyl-sulfate (SDS) on bare α-alumina NPs suspended in aqueous solution. The resulting adsorption free energy landscapes (FELs) are also investigated by tuning the percentage of SDS molecules coating the target bare NP. Our findings shed light on the competing role of enthalpic and entropic interaction contributions. On one hand, the adsorption is highly promoted by the tail-NP and tail-tail nonbonded interaction adhesion; on the other hand, our results unveil the entropic nature of water and surfactant steric effects occurring at the NP surface and preventing the adsorption. Finally, a thorough analysis on the steering works emphasizes the role of the NP curvature in the FEL of adsorption. In particular, we show that, moving from a solid infinite flat surface to a nanoscale particle, a deviation from a Markovian dynamics of adsorption occurs in close proximity to a curved solid-liquid interface. Here, both the NP curvature effect and nanoscale morphology promote a modification of the thermodynamics state of adsorption with a consequent splitting of the free energy profiles and the identification of specific sites of adsorption. The modeling framework suggested in this Article provides physical insights in the surfactant adsorption onto spherical NPs and suggests some guidelines to rationally design stable NP suspensions in aqueous solutions.

From GROMACS to LAMMPS: GRO2LAM : A converter for molecular dynamics software.

Atomistic simulations have progressively attracted attention in the study of physical-chemical properties of innovative nanomaterials. GROMACS and LAMMPS are currently the most widespread open-source software for molecular dynamics simulations thanks to their good flexibility, numerous functionalities and responsive community support. Nevertheless, the very different formats adopted for input and output files are limiting the possibility to transfer GROMACS simulations to LAMMPS. In this article, we present GRO2LAM, a modular and open-source Python 2.7 code for rapidly translating input files and parameters from GROMACS to LAMMPS format. The robustness of the tool has been assessed by comparing the simulation results obtained by GROMACS and LAMMPS, after the format conversion by GRO2LAM. Specifically, three nanoscale configurations of interest in both engineering and biomedical fields are studied, namely a carbon nanotube, an iron oxide nanoparticle, and a protein immersed in water. In perspective, GRO2LAM may be the first step to achieve a full interoperability between molecular dynamics software. This would allow to easily exploit their complementary potentialities and post-processing functionalities. Moreover, GRO2LAM could facilitate the cross-check of simulation results, guaranteeing the reproducibility of molecular dynamics models and testing their robustness. Graphical Abstract GRO2LAM, a modular and open-source Python code for rapidly translating input files and parameters from GROMACS to LAMMPS format.

Can Wicking Control Droplet Cooling?

Wicking, defined as absorption and passive spreading of liquid into a porous medium, has been identified as a key mechanism to enhance the heat transfer and prevent the thermal crisis. Reducing the evaporation time and increasing the Leidenfrost point (LFP) are important for an efficient and safe design of thermal management applications, such as electronics, nuclear, and aeronautics industry. Here, we report the effect of the wicking of superhydrophilic nanowires (NWs) on the droplet vaporization from low temperatures to temperatures above the Leidenfrost transition. By tuning the wicking capability of the surface, we show that the most wickable NW results in the fastest evaporation time (reduction of 82, 76, and 68% compared with a bare surface at, respectively, 51, 69, and 92 °C) and in one of the highest shifts of the LFP of a water droplet (5 µL) in the literature (about 260 °C).

Coffee-based colloids for direct solar absorption.

Despite their promising thermo-physical properties for direct solar absorption, carbon-based nanocolloids present some drawbacks, among which the unpleasant property of being potentially cytotoxic and harmful to the environment. In this work, a sustainable, stable and inexpensive colloid based on coffee is synthesized and its photo-thermal properties investigated. The proposed colloid consists of distilled water, Arabica coffee, glycerol and copper sulphate, which provide enhanced properties along with biocompatibility. The photo-thermal performance of the proposed fluid for direct solar absorption is analysed for different dilutions and compared with that of a traditional flat-plate collector. Tailor-made collectors, opportunely designed and realized via 3D-printing technique, were used for the experimental tests. The results obtained in field conditions, in good agreement with two different proposed models, show similar performance of the volumetric absorption using the proposed coffee-based colloids as compared to the classical systems based on a highly-absorbing surface. These results may encourage further investigations on simple, biocompatible and inexpensive colloids for direct solar absorption.

Multi-scale approach for modeling stability, aggregation, and network formation of nanoparticles suspended in aqueous solutions.

Suspensions of nanoparticles (NPs) in aqueous solutions hold promise in many research fields, including energy applications, water desalination, and nanomedicine. The ability to tune NP interactions, and thereby to modulate the NP self-assembly process, holds the key to rationally synthesize NP suspensions. However, traditional models obtained by coupling the DLVO (Derjaguin, Landau, Verwey, and Overbeek) theory of NP interactions, or suitable modifications of it, with the kinetic theory of colloidal aggregation are inadequate to precisely model NP self-assembly because they neglect hydration forces and discrete-size effects predominant at the nanoscale. By synergistically blending molecular dynamics and stochastic dynamics simulations with continuum theories, we develop a multi-scale (MS) model, which is able to accurately predict suspension stability, timescales for NP aggregation, and macroscopic properties (e.g., the thermal conductivity) of bare and surfactant-coated NP suspensions, in good agreement with the experimental data. Our results enable the formulation of design rules for engineering NP aqueous suspensions in a wide range of applications.

Dissipative particle dynamics simulations of tri-block co-polymer and water: Phase diagram validation and microstructure identification.

In this study, the phase diagram of Pluronic L64 and water is simulated via dissipative particle dynamics (DPD). The peculiar structures that form when the concentration varies from dilute to dense (i.e., spherical and rod-like micelles, hexagonal and lamellar phases, as well as reverse micelles) are recognized, and predictions are found to be in good agreement with experiments. A novel clustering algorithm is used to identify the structures formed, characterize them in terms of radius of gyration and aggregation number and cluster mass distributions. Non-equilibrium simulations are also performed, in order to predict how structures are affected by shear, both via qualitative and quantitative analyses. Despite the well-known scaling problem that results in unrealistic shear rates in real units, results show that non-Newtonian behaviors can be predicted by DPD and associated with variations of the observed microstructures.