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Slippery covalently attached liquid surfaces (SCALS) are a family of nanothin polymer layers with remarkably low static droplet friction, characterised by a low contact angle hysteresis (CAH$< 5 \deg$), which makes them ideally suited to self-cleaning, water harvesting, and anti-fouling applications. Recently, a Goldilocks zone of lowest CAH has been identified for polydimethyl siloxane (PDMS) SCALS of intermediate thickness (3.5 nm), yet, molecular-level insights are missing to reveal the underlying physical mechanism of this elusive, slippery optimum. In this work, the agreement between coarse-grained molecular dynamics simulations and atomic force microscopy data shows that nanoscale defects, as well as deformation for thicker layers, are key to explaining the existence of this `just right' regime. At low thickness values, insufficient substrate coverage gives rise to chemical patchiness; at large thickness values, two features appear: 1) a waviness forms on the surface of the liquid layer due to a previously overlooked lateral microphase separation occurring in polydisperse brushes, and 2) layer deformation due to the contact line is larger than in thinner layers. The most pronounced slippery behaviour occurs for smooth PDMS layers that do not exhibit nanoscale waviness. The converging insights from molecular simulations, experiments, and a contact angle hysteresis theory provide design guidelines for tethered polymer layers with ultra-low contact angle hysteresis., Comment: 4 figures

Finding proper collective variables for complex systems and processes is one of the most challenging tasks in simulations, which limits the interpretation of experimental and simulated data and the application of enhanced sampling techniques. Here, we propose a machine learning approach able to distill few, physically relevant variables by associating instantaneous configurations of the system to their corresponding inherent structures as defined in liquids theory. We apply this approach to the challenging case of structural transitions in nanoclusters, managing to characterize and explore the structural complexity of an experimentally relevant system constituted by 147 gold atoms. Our inherent-structure variables are shown to be effective at computing complex free-energy landscapes, transition rates, and at describing non-equilibrium melting and freezing processes. The effectiveness of this machine learning strategy guided by the generally-applicable concept of inherent structures shows promise to devise collective variables for a vast range of systems, including liquids, glasses, and proteins.

The Martini model, a coarse-grained forcefield for biomolecular simulations, has experienced a vast increase in popularity in the past decade. Its building-block approach balances computational efficiency with high chemical specificity, enabling the simulation of various organic and inorganic molecules. The modeling of coarse-grained beads as Lennard-Jones particles poses challenges for the accurate reproduction of liquid-vapour interfacial properties, which are crucial in various applications, especially in the case of water. The latest version of the forcefield introduces refined interaction parameters for water beads, tackling the well-known artefact of Martini water freezing at room temperature. Additionally, multiple sizes of water beads are available for simulating the solvation of small cavities, including the smallest pockets of proteins. This work focuses on studying the interfacial properties of Martini water, including surface tension, surface thickness, and bulk densities for the liquid and vapour phases. Employing the test-area method, we systematically compute the liquid-vapour surface tension across various combinations of water bead sizes and for temperatures in the range from 300 to 350 K. Our findings provide a comprehensive characterization of Martini 3.0 water intefacial properties. These findings are of interest to the Martini community as they allow users to account for the low interfacial tension of Martini water by properly adjusting observables computed via coarse-grained simulations (e.g., capillary forces) to allow for accurate matching against all-atom or experimental results. Surface tension data are also interpreted in terms of local enrichment of the various mixture components at the liquid-vapour interface by means of Gibbs' adsorption formalism, Comment: 32 pages, 9 tables, 7 figures

The large-conductance, calcium-activated potassium (BK) channel lacks the typical intracellular bundle-crossing gate present in most ion channels of the 6TM family. This observation, initially inferred from Ca$^{2+}$-free-pore accessibility experiments and recently corroborated by a CryoEM structure of the non-conductive state, raises a puzzling question: how can gating occur in absence of steric hindrance? To answer this question, we carried out molecular simulations and accurate free energy calculations to obtain a microscopic picture of the sequence of events that, starting from a Ca$^{2+}$-free state leads to ion conduction upon Ca$^{2+}$ binding. Our results highlight an unexpected role for annular lipids, which turn out to be an integral part of the gating machinery. Due to the presence of fenestrations, the "closed" Ca$^{2+}$-free pore can be occupied by the methyl groups from the lipid alkyl chains. This dynamic occupancy triggers and stabilizes the nucleation of a vapor bubble into the inner pore cavity, thus hindering ion conduction. By contrast, Ca$^{2+}$ binding results into a displacement of these lipids outside the inner cavity, lowering the hydrophobicity of this region and thus allowing for pore hydration and conduction. This lipid-mediated hydrophobic gating rationalizes several seemingly problematic experimental observations, including the state-dependent pore accessibility of blockers.

Nanopore sensing is a key technology for single-molecule detection and analysis. Solid-state nanopores have emerged as a versatile platform, since their fabrication allows to engineer their properties by controlling size, shape, and chemical functionalization. However, lithography-based fabrication approaches for non-planar nanopores-on-chip rely on polymers that have limits with respect to hard- and robustness, durability, and refractive index. In this respect, nanopores made of metal oxides with high dielectric constant would be much more favourable and have the potential to extend the suitability of solid-state nanopores towards optoelectronic technologies. Here, we present a versatile method to fabricate three-dimensional nanopores of different dielectric oxides with controlled shapes. Our approach uses photoresist only as a template in the focused-ion-beam lithography to define the nanopore shape, which is subsequently coated with different oxides (SiO2, Al2O3, TiO2 and HfO2) by atomic-layer deposition. Then the photoresist is fully removed by chemo-physical treatment, resulting in nanopores entirely made from dielectric oxides on a thin solid-state membrane. Our methodology allows straightforward fabrication of convex, straight, and concave nanopore shapes that can be employed in various technologies and applications. We explored their performance as ionic nanochannels and investigated the dependence of the ionic current rectification on the nanopore geometry. We found hysteresis in the ionic conductance that enables potential applications of the nanopores in memristors. We also investigated the dielectric oxide nanopores for DNA sensing by measuring both cis-trans and trans-cis translocations and support our data with numerical simulations based on the Poisson-Nernst-Planck model.

Wetting phenomena are widespread in both natural and technological contexts. Despite the well-established nature of this scientific field and our extensive knowledge of its underlying principles, wetting remains a dynamic and vibrant area of study. It continues to pose fundamental questions while offering innovative avenues for controlling these phenomena to develop novel applications. By tailoring the wetting properties of surfaces, researchers and engineers can design materials with specific functionalities, such as self-cleaning surfaces, anti-fog coatings, and enhanced slipperiness. Recent years have witnessed significant advancements in wetting research, owing to the exquisite control achieved in surface topography and chemistry and to the development of novel experimental techniques. Additionally, simulations and theory have played a crucial role in these advancements. They provid the fundamental knowledge and quantitative tools to control wettability and design surfaces with enhanced properties. Given these recent breakthroughs, this special collection Chemical Physics of Controlled Wettability and Super Surfaces becomes particularly timely and significant. It serves as a platform to showcase some of the latest developments in the field of wetting. It highlights the exciting progress and potential applications in controlling wetting properties that are enabled by the synergy between theory, simulations, and experiments., Comment: EDITORIAL: Special Topic: Chemical Physics of Controlled Wettability and Super Surfaces

Reliable and controllable switches are crucial in nanofluidics and iontronics. Ion channels in nature serve as a rich source of inspiration due to their intricate mechanisms modulated by stimuli like pressure, temperature, chemicals, and voltage. The artificial replication of the properties of these channels is challenging due to their complex chemistry, limited stability range, and intricate moving parts, allosterically modulated. Nonetheless, we can harness some of their gating mechanisms for nanofluidic and iontronic purposes. This theoretical and computational study explores the use of electrowetting in hydrophobic nanopores to control their conductance using an external applied voltage. We employ restrained molecular dynamics to calculate the free energy required for wetting a model nanopore under different voltages. Utilizing a simple theory, we generate free energy profiles across a wide voltage range. We also computed transition rates between conductive and non-conductive states, showing their voltage dependence and how this behaviour can impair memory to the system, resembling the memristor behaviour voltage-gated channels in the brain. These findings offer a promising avenue for designing and controlling hydrophobic nanopores via electrowetting, enabling potential applications in neuromorphic iontronics.

This paper explores the behaviour of malicious hacker groups operating in cyberspace and how they organize themselves in structured networks. To better understand these groups, the paper uses Social Network Analysis (SNA) to analyse the interactions and relationships among several malicious hacker groups. The study uses a tested dataset as its primary source, providing an empirical analysis of the cooperative behaviours exhibited by these groups. The study found that malicious hacker groups tend to form close-knit networks where they consult, coordinate with, and assist each other in carrying out their attacks. The study also identified a "small world" phenomenon within the population of malicious actors, which suggests that these groups establish interconnected relationships to facilitate their malicious operations. The small world phenomenon indicates that the actor-groups are densely connected, but they also have a small number of connections to other groups, allowing for efficient communication and coordination of their activities., Comment: In order to determine whether the results drawn in the paper are still relevant, additional research is necessary using an additional data source. This situation necessitates caution in sharing the conclusions, as does the fact that the subject matter (cyber-security and hacker activity assessments) is extremely sensitive

The liquid to vapour transition can occur at unexpected conditions in nanopores, opening the door to fundamental questions and new technologies. The physics of boiling in confinement is progressively introduced, starting from classical nucleation theory, passing through nanoscale effects, and terminating to the material and external parameters which affect the boiling conditions. The relevance of boiling in specific nanoconfined systems is discussed, focusing on heterogeneous lyophobic systems, chromatographic columns, and ion channels. The current level of control of boiling in nanopores enabled by microporous materials, as metal organic frameworks, and biological nanopores paves the way to thrilling theoretical challenges and to new technological opportunities in the fields of energy, neuromorphic computing, and sensing.

Finite-temperature structures of Cu, Ag, and Au metal nanoclusters are calculated in the entire temperature range from 0 K to melting using a computational methodology that we proposed recently [Settem \emph{et al.}, Nanoscale, 2022, 14, 939]. In this method, Harmonic Superposition Approximation (HSA) and Parallel Tempering Molecular Dynamics (PTMD) are combined in a complementary manner. HSA is accurate at low temperatures and fails at higher temperatures. PTMD, on the other hand, effectively samples the high temperature region and melting. This method is used to study the size- and system-dependent competition between various structural motifs of Cu, Ag, and Au nanoclusters in the size range 1 to 2 nm. Results show that there are mainly three types of structural changes in metal nanoclusters depending on whether a solid-solid transformation occurs. In the first type, global minimum is the dominant motif in the entire temperature range. In contrast, when a solid-solid transformation occurs, the global minimum transforms either completely to a different motif or partially resulting in a co-existence of multiple motifs. Finally, nanocluster structures are analyzed to highlight the system-specific differences across the three metals., Comment: The following article has been accepted by Journal of Chemical Physics. After it is published, it will be found at https://doi.org/10.1063/5.0159257. Accepted version of the manuscript (along with supplementary material) consists of 32 pages, 11 figures

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large dataset of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and to distill a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold, in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences, such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables which has the potential to be used for exploration and enhanced sampling purposes., Comment: 28 pages, 13 figures

Brain-inspired computing has the potential to revolutionise the current von Neumann architecture, advancing machine learning applications. Signal transmission in the brain relies on voltage-gated ion channels, which exhibit the electrical behaviour of memristors, resistors with memory. State-of-the-art technologies currently employ semiconductor-based neuromorphic approaches, which have already demonstrated their efficacy in machine learning systems. However, these approaches still cannot match performance achieved by biological neurons in terms of energy efficiency and size. In this study, we utilise molecular dynamics simulations, continuum models, and electrophysiological experiments to propose and realise a bioinspired hydrophobically gated memristive nanopore. Our findings indicate that hydrophobic gating enables memory through an electrowetting mechanism, and we establish simple design rules accordingly. Through the engineering of a biological nanopore, we successfully replicate the characteristic hysteresis cycles of a memristor \tr{and construct a synaptic device capable of learning and forgetting}. This advancement offers a promising pathway for the realization of nanoscale, cost- and energy-effective, and adaptable bioinspired memristors.

Understanding intrusion and extrusion in nanoporous materials is a challenging multiscale problem of utmost importance for applications ranging from energy storage and dissipation to water desalination and hydrophobic gating in ion channels. Including atomistic details in simulations is required to predict the overall behavior of such systems, because the statics and dynamics of these processes depend sensitively on microscopic features of the pore such as the surface hydrophobicity, geometry, and charge distribution and on the composition of the liquid. On the other hand, the transitions between the filled (intruded) and empty (extruded) states are rare events which often require long simulation times difficult to achieve with standard atomistic simulations. In this work, we explored the intrusion and extrusion processes by a multiscale approach in which the atomistic details of the system, extracted from molecular dynamics simulations, inform a simple Langevin model of water intrusion/extrusion in the pore. We then used the Langevin simulations to compute the transition times at different pressures, validating our coarse-grained model by comparing it with nonequilibrium molecular dynamics simulations. The proposed approach reproduces experimentally relevant features such as the time and temperature dependence of the intrusion/extrusion cycles, as well as specific details about the shape of the cycle. This approach also drastically increases the timescales that can be simulated allowing to reduce the gap between simulations and experiments and showing promise for more complex systems., Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in "Gon\c{c}alo Paulo, Alberto Gubbiotti, Alberto Giacomello; J. Chem. Phys. 28 May 2023; 158 (20)" and may be found at https://doi.org/10.1063/5.0147647

A computational approach to determine the equilibrium structures of nanoclusters in the whole temperature range from 0 K to melting is developed. Our approach relies on Parallel Tempering Molecular Dynamics (PTMD) simulations complemented by Harmonic Superposition Approximation (HSA) calculations and global optimization searches, thus combining the accuracy of global optimization and HSA in describing the low-energy part of configuration space, together with the PTDM thorough sampling of high-energy configurations. This combined methodology is shown to be instrumental towards revealing the temperature-dependent structural motifs in Au nanoclusters of sizes 90, 147, and 201 atoms. The reported phenomenology is particularly rich, displaying a size- and temperature-dependent competition between the global energy minimum and other structural motifs. In the case of Au$_{90}$ and Au$_{147}$, the global minimum is also the dominant structure at finite temperatures. In contrast, the Au$_{201}$ cluster undergoes a solid-solid transformation at low temperature (< 200 K). Results indicate that PTMD and HSA very well agree at intermediate temperatures, between 300 and 400 K. For higher temperatures, PTMD gives an accurate description of equilibrium, while HSA fails in describing the melting range. On the other hand, HSA is more efficient in catching low-temperature structural transitions. Finally, we describe the elusive structures close to the melting region which can present complex and defective geometries, that are otherwise difficult to characterize through experimental imaging., Comment: Uploaded accepted manuscript post embargo period

Deep Learning has established in the latest years as a successful approach to address a great variety of tasks. Healthcare is one of the most promising field of application for Deep Learning approaches since it would allow to help clinicians to analyze patient data and perform diagnoses. However, despite the vast amount of data collected every year in hospitals and other clinical institutes, privacy regulations on sensitive data - such as those related to health - pose a serious challenge to the application of these methods. In this work, we focus on strategies to cope with privacy issues when a consortium of healthcare institutions needs to train machine learning models for identifying a particular disease, comparing the performances of two recent distributed learning approaches - Federated Learning and Split Learning - on the task of Automated Chest X-Ray Diagnosis. In particular, in our analysis we investigated the impact of different data distributions in client data and the possible policies on the frequency of data exchange between the institutions., Comment: 8 pages, 1 figure

The study of wetting phenomena is of great interest due to the multifaceted technological applications of hydrophobic and hydrophilic surfaces. The theoretical approaches proposed by Wenzel and later by Cassie and Baxter to describe the behaviour of a droplet of water on a rough solid were extensively used and improved to characterize the apparent contact angle of a droplet. However, the equilibrium hypothesis implied in these models means that they are not always predictive of experimental contact angles due to strong metastabilities typically occurring on heterogeneous surfaces. A predictive scheme for contact angle is thus urgently needed both to characterise a surface by contact angle measurements and to design superhydrophobic and oleophobic surfaces with the desired properties, e.g., contact angle hysteresis. In this work a combination of Monte Carlo simulation and the string method is employed to calculate the free energy profile of a liquid droplet deposited on a pillared surface. For the analyzed surfaces, we show that there is only one minimum of the free energy that corresponds to the superhydrophobic wetting state while the wet state can present multiple minima. Furthermore, when the surface roughness decreases the amount of local minima observed in the free energy profile increases.

Chest X-ray (CXR) is perhaps the most frequently-performed radiological investigation globally. In this work, we present and study several machine learning approaches to develop automated CXR diagnostic models. In particular, we trained several Convolutional Neural Networks (CNN) on the CheXpert dataset, a large collection of more than 200k CXR labeled images. Then, we used the trained CNNs to compute embeddings of the CXR images, in order to train two sets of tree-based classifiers from them. Finally, we described and compared three ensembling strategies to combine together the classifiers trained. Rather than expecting some performance-wise benefits, our goal in this work is showing that the above two methodologies, i.e., the extraction of image embeddings and models ensembling, can be effective and viable to solve tasks that require medical imaging understanding. Our results in that perspective are encouraging and worthy of further investigation., Comment: Accepted at IJCNN 2021