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

Interfacial water thickness at inorganic nanoconstructs and biomolecules: Size matters

The study of water dynamics at solid-liquid interfaces has strong implications in energy, environmental and biomedical elds. The peculiar behavior of water molecules in the proximity of solid nanostructures influences both the overall properties of liquid and the structure and functionality of solid particles. This article focuses on the hydration layer properties in the proximity of Carbon Nanotubes (CNTs) and biomolecules (proteins, polipeptides and amino acids). Here we show a quantitative relation between the solid surface extension and the characteristic length of water nanolayer (), which is conned at solid-liquid interfaces. Specically, the size dependence is attributed to the limited superposition of nonbonded interactions in case of small molecules. These results may facilitate the design of novel energy or biomedical colloidal nanosuspensions, and a more fundamental understanding of biomolecular processes influenced by nanoscale water dynamics

Mass transport phenomena at the solid-liquid nanoscale interface in biomedical application

Understanding heat and mass transfer phenomena at solid-liquid nanoscale interface plays a crucial role for introducing novel and more rationally designed theranostic particles, drug with tailored features and for gaining new insight on the biomolecules functioning. For instance, the water transport properties in the proximity of Amyloid beta peptides can influence the formation of amyloid plaques found in the brains of Alzheimer patients. In the present work, transport behavior of water molecules in nanoconfined conditions has been investigated. By means of equilibrium Molecular Dynamics (MD) simulations, characteristic length of water confinement has been evaluated in the proximity of several biomolecules such as proteins and amino acids. Moving from proteins to their building blocks (i.e. amino acids), a similarity in water behavior was initially expected; MD simulations results show, instead, a more complex picture revealing a difference between the potential of water nanoconfinement by either proteins or amino acids. Hence, the reduction of water mobility in the proximity of nanoscale interfaces does not rely only on the local physical and chemical properties of the biomolecules surface, but the effects of size and potentials overlap should be also taken into account

Towards a multiscale simulation approach of nanofluids for volumetric solar receivers: Assessing inter-particle potential energy

A modern concept for solar thermal collectors is based on volumetric absorption of sunlight, where nanoparticles suspended in liquids directly receive the incident radiation. Suspending nanoparticles in traditional fluids can drastically enhance their optical properties and improve thermo-physical performances, thus leading to highly efficient volumetric solar receivers. Several studies have been addressed on the physical understanding of such nanosuspensions; however, the relation between nanoscale effects and macroscopic properties is far from being fully understood. The present work represents a first step towards a multiscale modeling approach for relating nanoscale properties to macroscopic behaviour of nanofluids. In particular, a suitable Coarse-Grained (CG) method for nanofluids is described. By means of Molecular Dynamics (MD) simulations, the pair Potential of Mean Forces (pPMF) between CG beads of nanofluid is evaluated. A complete CG force field can be then defined by including the effects of water adsorbed at solid-liquid interface, nanoparticle surface charge and solution pH. Our multiscale model is intended to permit a future study of the complex mechanisms of nanoparticle clustering, which is known to affect nanofluids stability and properties. We hope that this multiscale approach may start the process of rational design of nanofluids thus facilitating technology transfer from lab experiments to large-scale industrial production

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

Many complex molecular systems owe their properties to local dynamic rearrangements or fluctuations that, despite the rise of machine learning (ML) and sophisticated structural descriptors, remain often difficult to detect. Here we show an ML framework based on a new descriptor, named Local Environments and Neighbors Shuffling (LENS), which allows identifying dynamic domains and detecting local fluctuations in a variety of systems via tracking how much the surrounding of each molecular unit changes over time in terms of neighbor individuals. Statistical analysis of the LENS time-series data allows to blindly detect different dynamic domains within various types of molecular systems with, e.g., liquid-like, solid-like, or diverse dynamics, and to track local fluctuations emerging within them in an efficient way. The approach is found robust, versatile, and, given the abstract definition of the LENS descriptor, capable of shedding light on the dynamic complexity of a variety of (not necessarily molecular) systems

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

Machine learning of microscopic structure-dynamics relationships in complex molecular systems

In many complex molecular systems, the macroscopic ensemble's properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic level structure-dynamics relationships fundamental to understand the macroscopic behavior of complex systems. Here we show that, by coupling advanced structural (e.g., Smooth Overlap of Atomic Positions, SOAP) with local dynamical descriptors (e.g., Local Environment and Neighbor Shuffling, LENS) in a unique dataset, it is possible to improve both individual SOAP- and LENS-based analyses, obtaining a more complete characterization of the system under study. As representative examples, we use various molecular systems with diverse internal structural dynamics. On the one hand, we demonstrate how the combination of structural and dynamical descriptors facilitates decoupling relevant dynamical fluctuations from noise, overcoming the intrinsic limits of the individual analyses. Furthermore, machine learning approaches also allow extracting from such combined structural/dynamical dataset useful microscopic-level relationships, relating key local dynamical events (e.g., LENS fluctuations) occurring in the systems to the local structural (SOAP) environments they originate from. Given its abstract nature, we believe that such an approach will be useful in revealing hidden microscopic structure-dynamics relationships fundamental to rationalize the behavior of a variety of complex systems, not necessarily limited to the atomistic and molecular scales

Thermal transport phenomena in nanoparticle suspensions

Nanoparticle suspensions in liquids have received great attention, as they may offer an approach to enhance thermophysical properties of base fluids. A good variety of applications in engineering and biomedicine has been investigated with the aim of exploiting the above potential. The multiscale nature of nanosuspensions raises several issues in defining a comprehensive modelling framework, incorporating relevant molecular details and much larger scale phenomena, such as particle aggregation and their dynamics. The objectives of the present topical review is to report and discuss the main heat and mass transport phenomena ruling their macroscopic behaviour, arising from molecular details. Relevant experimental results are included and properly put in the context of recent observations and theoretical studies, which solved long-standing debates about thermophysical properties enhancement. Major transport phenomena are discussed and in-depth analysis is carried out for highlighting the role of geometrical (nanoparticle shape, size, aggregation, concentration), chemical (pH, surfactants, functionalization) and physical parameters (temperature, density). We finally overview several computational techniques available at different scales with the aim of drawing the attention on the need for truly multiscale predictive models. This may help the development of next-generation nanoparticle suspensions and their rational use in thermal applications

TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra

Many molecular systems and physical phenomena are controlled by local fluctuations and microscopic dynamical rearrangements of the constitutive interacting units that are often difficult to detect. This is the case, for example, of phase transitions, phase equilibria, nucleation events, and defect propagation, to mention a few. A detailed comprehension of local atomic environments and of their dynamic rearrangements is essential to understand such phenomena and also to draw structure-property relationships useful to unveil how to control complex molecular systems. Considerable progress in the development of advanced structural descriptors [e.g., Smooth Overlap of Atomic Position (SOAP), etc.] has certainly enhanced the representation of atomic-scale simulations data. However, despite such efforts, local dynamic environment rearrangements still remain difficult to elucidate. Here, exploiting the structurally rich description of atomic environments of SOAP and building on the concept of time-dependent local variations, we developed a SOAP-based descriptor, TimeSOAP (τSOAP), which essentially tracks time variations in local SOAP environments surrounding each molecule (i.e., each SOAP center) along ensemble trajectories. We demonstrate how analysis of the time-series τSOAP data and of their time derivatives allows us to detect dynamic domains and track instantaneous changes of local atomic arrangements (i.e., local fluctuations) in a variety of molecular systems. The approach is simple and general, and we expect that it will help shed light on a variety of complex dynamical phenomena

Thermally triggered nanorocket from double-walled carbon nanotube in water

In this work, we propose and investigate the use of double-walled carbon nanotubes (DWCNTs) as nanosized rockets. The nanotubes are immersed in water, and the propulsion of inner nanotube is achieved by heating the water encapsulated within the DWCNT. Considering a setup made of (5,5)(8,8) DWCNT, molecular dynamics simulations for different water temperatures show that the trajectory can be divided into four phases: trigger, expulsion, damping and final equilibrium. After analysing the dynamics and the involved forces, we find out that the inner nanotube expulsion is mainly controlled by van der Waals interactions between the nanotubes; whereas, the damping role is predominantly played by the external aqueous environment. Based on these results, we propose an analytical model able to predict both the triggering time for a given water temperature and the whole dynamics of nanorocket. The validity of such dynamical model can be extended also to a broader variety of DWCNT configurations, once the different forces acting on the inner nanotube are provided. The proposed model may contribute to assist the design of nanorockets in several nanotechnology applications, such as triggered drug delivery, cell membrane piercing, or colloids with thermophoretic properties