111 years of Brownian motion

Bacterial Adhesion to Natural and Synthetic Fibre-Forming Polymers: Influence of Material Properties

Polymer-based textiles have a major impact on human well-being, as they provide the desired functional protection and aesthetic comfort when worn. However, natural and synthetic polymer-based textiles can also pose serious health risks, as they are surfaces that allow the adhesion of various bacteria, including pathogenic bacteria. To minimise these problems, antibacterial chemical treatments are generally applicable in the case of polymer-based textiles. However, to avoid the use of potentially toxic chemicals, sustainable approaches require the customised design of non-adhesive polymer-based textiles, considering their chemical, physicochemical, constructional, and textural properties. Before designing, several articles are required to gain sufficient knowledge of the described object. Despite the urgent need to combat bacteria (on polymer-based textiles), which pose a serious global health risk, only a few review articles have been published that address bacterial adhesion in the context of superhydrophobic and antibacterial textile materials, while only one review article holistically addresses the textile factors and their influence on this phenomenon. The aim of this review article is to expand the insufficient knowledge about bacterial adhesion to polymer-based textiles on the basis of theoretical findings and real examples through a high degree of structuring, simplification, holistic consideration, and visualization. Therefore, this review provides an insight into the mechanisms involved in bacterial adhesion and a comprehensive overview of the influence of different textile factors, such as chemical composition, hydrophilicity/hydrophobicity, surface charge, surface free energy, roughness, and porosity, on bacterial adhesion. To emphasise the importance of the synergistic effect of the combined textile factors, examples of the influence of hydrophilicity/hydrophobicity in combination with surface charge, surface roughness, and porosity are discussed. From the review, it can be concluded that the combination of hydrophilicity/hydrophobicity and the surface charge of textile fibres and bacteria is crucial for bacterial adhesion, with roughness and porosity being the most important factors among the constructive and textural properties of polymer-based textiles.

Measuring the Adsorption Cross Section of YOYO-1 to Immobilized DNA Molecules

Many interactions between small molecules and particles occur in solutions. They are surrounded by other molecules that do not react, for example, biological processes in water, chemical reactions in gas or liquid solutions, and environmental reactions in air and water. However, predicting the rate of such diffusive interactions remains challenging, due to the random motion of molecules in solutions, as exampled by the famous Brownian motion of pollen particles. In this report, we experimentally confirmed that a disruptive rate equation we have published before can predict the association rate of typical adsorption at interfaces, which has a surprising fraction order of 4/3 that has not been considered before. This could be an important step toward a generalized method to predict the adsorption rate of many reactions.

Chemically Specific Systematic Coarse-Grained Polymer Model with Both Consistently Structural and Dynamical Properties

The coarse-grained (CG) model serves as a powerful tool for the simulation of polymer systems; its reliability depends on the accurate representation of both structural and dynamical properties. However, strong correlations between structural and dynamical properties on different scales and also a strong memory effect, enforced by chain connectivity between monomers in polymer systems, render developing a chemically specific systematic CG model a formidable task. In this study, we report a systematic CG approach that combines the iterative Boltzmann inversion (IBI) method and the generalized Langevin equation (GLE) dynamics. Structural properties are ensured by using conservative CG potentials derived from the IBI method. To retrieve the correct dynamical properties in the system, we demonstrate that using a combination of a Rouse-type delta function and a time-dependent short-time kernel in the GLE simulation is practically efficient. The former can be used to adjust the long-time diffusion dynamics, and the latter can be reconstructed from an iterative procedure according to the velocity autocorrelation function (ACF) from all-atomistic (AA) simulations. Taking the polystyrene as an example, we show that not only structural properties of radial distribution function, intramolecular bond, and angle distributions can be reproduced but also dynamical properties of mean-square displacement, velocity ACF, and force ACF resulted from our CG model have quantitative agreement with the reference AA model. In addition, reasonable agreements are observed in other collective properties between our GLE-CG model and the AA simulations as well.

Dual-Angle Interferometric Scattering Microscopy for Optical Multiparametric Particle Characterization

Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles to be in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size and polarizability of individual nanoparticles (radius <170 nm) without requiring a priori information regarding the surrounding media or super-resolution imaging. DAISY achieves this by combining the information contained in concurrently measured forward and backward scattering images through twilight off-axis holography and interferometric scattering (iSCAT). Going beyond particle size and polarizability, single-particle morphology can be deduced from the fact that the hydrodynamic radius relates to the outer particle radius, while the scattering-based size estimate depends on the internal mass distribution of the particles. We demonstrate this by differentiating biomolecular fractal aggregates from spherical particles in fetal bovine serum at the single-particle level.

Noise-cancellation algorithm for simulations of Brownian particles

We investigate the usage of a recently introduced noise-cancellation algorithm for Brownian simulations to enhance the precision of measuring transport properties such as the mean-square displacement or the velocity-autocorrelation function. The algorithm is based on explicitly storing the pseudorandom numbers used to create the randomized displacements in computer simulations and subtracting them from the simulated trajectories. The resulting correlation function of the reduced motion is connected to the target correlation function up to a cross-correlation term. Using analytical theory and computer simulations, we demonstrate that the cross-correlation term can be neglected in all three systems studied in this paper. We further expand the algorithm to Monte Carlo simulations and analyze the performance of the algorithm and rationalize that it works particularly well for unbounded, weakly interacting systems in which the precision of the mean-square displacement can be improved by orders of magnitude.

Mechanisms and models for water transport in reverse osmosis membranes: history, critical assessment, and recent developments

Water scarcity is one of the greatest societal challenges facing humanity. Reverse osmosis (RO) desalination, a widely used membrane-based technology, has proven to be effective to augment water supply in water-stressed regions of our planet. However, progress in the design and development of RO membranes has been limited. To significantly enhance the performance of RO membranes, it is essential to acquire a deep understanding of the membrane separation and transport mechanisms. In this tutorial review, we cover the pivotal historical developments in RO technology, examine the chemical and physical properties of RO membrane materials, and critically review the models and mechanisms proposed for water transport in RO membranes. Based on recent experimental and computational findings, we conduct a thorough analysis of the key transport models-the solution-diffusion and pore-flow models-to assess their validity for accurately describing water transport in RO membranes. Our analysis involves examining the experimental evidence in favor of the solution-diffusion mechanism. Specifically, we explain whether the water content gradient within the membrane, cited as evidence for the key assumption in the solution-diffusion model, can drive a diffusive transport through RO membranes. Additionally, we review the recent molecular dynamics simulations which support the pore-flow mechanism for describing water transport in RO membranes. We conclude by providing future research directions aimed at addressing key knowledge gaps in water transport phenomena in RO membranes, with the goal of advancing the development of next-generation RO membranes.

Fast Dynamics of Difluprednate in Micelles or Swollen-Micelles Revealed by 19F Nuclear Magnetic Resonance Spin Relaxation Rates

Knowledge of molecular rotational dynamics is critical to interpret solution nuclear magnetic resonance (NMR) spectroscopy. The observation of sharp solute NMR signals in micelles contradicted the surfactant viscosity effects noted in the Stokes-Einstein-Debye (SED) equation. Herein, the ¹⁹F spin relaxation rates of difluprednate (DFPN) drug dissolved in polysorbate-80 (PS-80) micelles and castor oil swollen micelles (s-micelle) were measured and adequately fit using an isotropic diffusion model based spectral density function. Despite the high viscosity of PS-80 and castor oil, the fitting results revealed fast 4 and 12 ns dynamics of DFPN in both micelle globules. The observation of the fast ns motion in the viscous surfactant/oil micelle phase demonstrated motion decoupling between solute molecules inside micelles and the micelle itself in an aqueous solution. These observations support the role of intermolecular interaction in governing the rotational dynamics of small molecules, versus the viscosity of the solvent molecules as defined in the SED equation.

Liquid Structures and Diffusion Dynamics of Alkyl-Pyrene Liquids Studied by Molecular Dynamics Simulations

Functional molecular liquids (FMLs) based on alkylated π-conjugated molecules have attracted attention as solvent-free and nonvolatile liquid materials with prominent optoelectronic features. Recently, novel FML compounds containing pyrene as the functional core were synthesized, and their rheological and photochemical properties were investigated. Although the molecules differ only in the number of alkyl chain substituents and their substitution positions, their viscosity coefficients are largely different beyond the Stokes-Einstein relation on the assumption of identical microscopic friction, indicating that local microscopic molecular interactions are crucial for the macroscopic rheological properties. Here, we report a theoretical study on the rheological properties of the alkyl-pyrene liquids by means of atomistic molecular dynamics (MD) simulations. We performed long-time MD simulations for tens of microseconds to obtain ample statistical samples of the alkyl-pyrene liquids and analyzed their liquid structures and diffusion dynamics based on spatiotemporal correlation functions. We found the formation of characteristic local liquid structures of π-π stacking of the pyrene moieties and locally anisotropic and anomalous diffusion dynamics, which remarkably vary depending on the alkyl substituent patterns. The present results provide an atomistic insight into the macroscopic rheological properties of alkyl-π FMLs and molecular design strategy for them.

Constrained disorder principle-based variability is fundamental for biological processes: Beyond biological relativity and physiological regulatory networks

The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality.

Numerical simulations of confined Brownian-yet-non-Gaussian motion

Brownian motion is a central scientific paradigm. Recently, due to increasing efforts and interests towards miniaturization and small-scale physics or biology, the effects of confinement on such a motion have become a key topic of investigation. Essentially, when confined near a wall, a particle moves much slower than in the bulk due to friction at the boundaries. The mobility is therefore locally hindered and space-dependent, which in turn leads to the apparition of so-called multiplicative noises, and associated non-Gaussianities which remain difficult to resolve at all times. Here, we exploit simple, optimized and efficient numerical simulations to address Brownian motion in confinement in a broadrange and quantitative way. To do so, we integrate the overdamped Langevin equation governing the thermal dynamics of a negatively-buoyant single spherical colloid within a viscous fluid confined by two rigid walls, including surface charges. From the produced large set of long random trajectories, we perform a complete statistical analysis and extract all the key quantities, such as the probability distributions in displacements and their main moments. In particular, we propose a novel method to compute high-order cumulants by reducing convergence problems, and employ it to efficiently characterize the inherent non-Gaussianity of the confined process.

Mapping the diffusion pattern of 1O2 along DNA duplex by guanine photooxidation with an appended biphenyl photosensitizer

To realize nucleic acid-targeting photodynamic therapy, a photosensitizer should be attached at the optimal position on a complementary oligonucleotide, where a guanine photooxidation is maximized. Here we show the photooxidation of 22 DNA duplexes with varied lengths between a 1O2-generating biphenyl photosensitizer attached at a midchain thymine in a strand and the single guanine reactant in the other strand. The best photooxidation efficiencies are achieved at 9, 10, and 21 base intervals, which coincides with the pitch of 10.5 base pairs per turn in a DNA duplex. The low efficiencies for near and far guanines are due to quenching of the biphenyl by guanine and dilution of 1O2 by diffusion, respectively. The 1O2-diffusion mapping along DNA duplex provides clues to the development of efficient and selective photosensitizer agents for nucleic acid-targeting photodynamic therapy, as well as an experimental demonstration of diffusion of a particle along cylindrical surface in molecular level.

Why Should the Reaction Order of a Bimolecular Reaction be 2.33 Instead of 2?

Predicting the reaction kinetics, that is, how fast a reaction can happen in a solution, is essential information for many processes, such as industrial chemical manufacturing, refining, synthesis and separation of petroleum products, environmental processes in air and water, biological reactions in cells, biosensing, and drug delivery. Collision theory was originally developed to explain the reaction kinetics of gas reactions with no dilution. For a reaction in a diluted inert gas solution or a diluted liquid solution, diffusion often dominates the collision process. Thus, it is necessary to include diffusion in such a calculation. Traditionally, the classical Smoluchowski rate is used as a starting point to predict the collision frequency of two molecules in a diluted solution. In this report, a different collision model is derived from the adsorption of molecules on a flat surface. A surprising result is obtained, showing that the reaction order for bimolecular reactions should be 2 and 1/3 instead of 2, following a fractal reaction kinetics.

PEG Gels Significantly Improve the Storage Stability of Nucleic Acid Preparations

Currently, nucleic acid preparations have gained much attention due to their unique working principle and application value. However, as macromolecular drugs, nucleic acid preparations have complex construction and poor stability. The current methods to promote stability face problems such as high cost and inconvenient operatios. In this study, the hydrophilic pharmaceutical excipient PEG was used to gelate nucleic acid preparations to avoid the random movements of liquid particles. The results showed that PEG gelation significantly improved the stability of PEI25K-based and liposome-based nucleic acid preparations, compared with nucleic acid preparations without PEG gelation. After being stored at 4 °C for 3 days, non-PEG gelled nucleic acid preparations almost lost transfection activity, while PEGylated preparations still maintained high transfection efficiency. Fluorescence experiments showed that this effect was caused by inhibiting particle aggregation. The method described in this study was simple and effective, and the materials used had good biocompatibility. It is believed that this study will contribute to the better development of gene therapy drugs.

Toward a Realistic Model of Diffusion-Limited Aggregation: Rotation, Size-Dependent Diffusivities, and Settling

In this Brownian dynamics simulation study on the formation of aggregates made of spherical particles, we build on the well-established diffusion-limited cluster aggregation (DLCA) model. We include rotational effects, allow diffusivities to be size-dependent as is physically relevant, and incorporate settling under gravity. We numerically characterize the growth dynamics of aggregates and find that their radius of gyration, R _g, grows approximately as R _g ∼ t ^1.02 for classical DLCA but slows to an approximate growth rate of R _g ∼ t ^0.71 when diffusivity is size-dependent. We also analyze the fractal structure of the resulting aggregates and find that their fractal dimension, d, decreases from d ≈ 1.8 for classical DLCA to d ≈ 1.7 when size-dependent rotational diffusion is included. The addition of settling effects further reduces the fractal dimension observed to d ≈ 1.6 and appears to result in aggregates with a vertical extent marginally smaller than their horizontal extent.

Methods to analyze extracellular vesicles at single particle level

Extracellular vesicles (EVs) are nano-sized vesicles derived from cells that transport biomaterials between cells through biofluids. Due to their biological role and components, they are considered as potential drug carriers and for diagnostic applications. Today's advanced nanotechnology enables single-particle-level analysis that was difficult in the past due to its small size below the diffraction limit. Single EV analysis reveals the heterogeneity of EVs, which could not be discovered by various ensemble analysis methods. Understanding the characteristics of single EVs enables more advanced pathological and biological researches. This review focuses on the advanced techniques employed for EV analysis at the single particle level and describes the principles of each technique.

Human-Scale Brownian Ratchet: A Historical Thought Experiment

We present an experimental realization at macroscopic scale of the storied Brownian ratchet, which is an illustration of the Maxwell's demon. In our mechanism, the rotation of a centimeter-scale 1D Brownian object in a granular gas is detected by an electromechanical converter (dynamo), generating a voltage proportional to its angular velocity. The current generated by this random rotation is rectified by an electronic device (demon), such that only positive current passes. Eventually, work can be produced. The advantage of such a macroscopic setup is to allow measurement of all the observables with time: useful power (work), heat taken from the bath, and finally the efficiency of the equivalent heat engine. The feedback allowing the conversion from heat into work expresses as a bias on the Brownian motion.

A Novel Physical Mechanism to Model Brownian Yet Non-Gaussian Diffusion: Theory and Application

In the last years, a few experiments in the fields of biological and soft matter physics in colloidal suspensions have reported "normal diffusion" with a Laplacian probability distribution in the particle's displacements (i.e., Brownian yet non-Gaussian diffusion). To model this behavior, different stochastic and microscopic models have been proposed, with the former introducing new random elements that incorporate our lack of information about the media and the latter describing a limited number of interesting physical scenarios. This incentivizes the search of a more thorough understanding of how the media interacts with itself and with the particle being diffused in Brownian yet non-Gaussian diffusion. For this reason, a comprehensive mathematical model to explain Brownian yet non-Gaussian diffusion that includes weak molecular interactions is proposed in this paper. Based on the theory of interfaces by De Gennes and Langevin dynamics, it is shown that long-range interactions in a weakly interacting fluid at shorter time scales leads to a Laplacian probability distribution in the radial particle's displacements. Further, it is shown that a phase separation can explain a high diffusivity and causes this Laplacian distribution to evolve towards a Gaussian via a transition probability in the interval of time as it was observed in experiments. To verify these model predictions, the experimental data of the Brownian motion of colloidal beads on phospholipid bilayer by Wang et al. are used and compared with the results of the theory. This comparison suggests that the proposed model is able to explain qualitatively and quantitatively the Brownian yet non-Gaussian diffusion.

War and Peace between Electrostatic and van der Walls Forces Regulate Translational and Rotational Diffusion

Diffusion of a Brownian particle or a molecule in a liquid solvent is caused, in the Stokes-Einstein picture, by unbalanced fluctuations of osmotic forces on different sides of the particle. When the particle carries a charge or a higher multipolar moment, this picture is amended by fluctuations of electrostatic forces producing dielectric friction. Dielectric friction slows both the translational and rotational diffusion down. While this picture is well established and is physically sound, standard theories grossly overestimate the magnitude of dielectric friction for small dipolar solutes and larger colloidal particles, such as proteins. Motivated by recent simulation studies, this perspective discusses the interplay between osmotic (van der Waals) and electrostatic forces in promoting molecular and colloidal diffusion. Much can be learned about microscopic friction mechanisms from statistical and dynamical correlations between osmotic and electrostatic forces.

Mass Changes the Diffusion Coefficient of Particles with Ligand-Receptor Contacts in the Overdamped Limit

Inertia does not generally affect the long-time diffusion of passive overdamped particles in fluids. Yet a model starting from the Langevin equation predicts a surprising property of particles coated with ligands that bind reversibly to surface receptors: heavy particles diffuse more slowly than light ones of the same size. We show this by simulation and by deriving an analytic formula for the mass-dependent diffusion coefficient in the overdamped limit. We estimate the magnitude of this effect for a range of biophysical ligand-receptor systems, and find it is potentially observable for tailored micronscale DNA-coated colloids.

The nanocaterpillar's random walk: diffusion with ligand-receptor contacts

Particles with ligand-receptor contacts bind and unbind fluctuating "legs" to surfaces, whose fluctuations cause the particle to diffuse. Quantifying the diffusion of such "nanoscale caterpillars" is a challenge, since binding events often occur on very short time and length scales. Here we derive an analytical formula, validated by simulations, for the long time translational diffusion coefficient of an overdamped nanocaterpillar, under a range of modeling assumptions. We demonstrate that the effective diffusion coefficient, which depends on the microscopic parameters governing the legs, can be orders of magnitude smaller than the background diffusion coefficient. Furthermore it varies rapidly with temperature, and reproduces the striking variations seen in existing data and our own measurements of the diffusion of DNA-coated colloids. Our model gives insight into the mechanism of motion, and allows us to ask: when does a nanocaterpillar prefer to move by sliding, where one leg is always linked to the surface, and when does it prefer to move by hopping, which requires all legs to unbind simultaneously? We compare a range of systems (viruses, molecular motors, white blood cells, protein cargos in the nuclear pore complex, bacteria such as Escherichia coli, and DNA-coated colloids) and present guidelines to control the mode of motion for materials design.

Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning

Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present pathology tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme to estimate the biologically meaningful model parameters using approximate Bayesian computation with a summary statistic that maximally discriminates between different types of patients. Inferred parameters from data collected on healthy volunteers and different patient types help us to identify specific biological parameters and hence biological reasoning behind the dysfunction for each type of patients. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment.

Non-Gaussian, transiently anomalous, and ergodic self-diffusion of flexible dumbbells in crowded two-dimensional environments: Coupled translational and rotational motions

We employ Langevin-dynamics simulations to unveil non-Brownian and non-Gaussian center-of-mass self-diffusion of massive flexible dumbbell-shaped particles in crowded two-dimensional solutions. We study the intradumbbell dynamics of the relative motion of the two constituent elastically coupled disks. Our main focus is on effects of the crowding fraction ϕ and of the particle structure on the diffusion characteristics. We evaluate the time-averaged mean-squared displacement (TAMSD), the displacement probability-density function (PDF), and the displacement autocorrelation function (ACF) of the dimers. For the TAMSD at highly crowded conditions of dumbbells, e.g., we observe a transition from the short-time ballistic behavior, via an intermediate subdiffusive regime, to long-time Brownian-like spreading dynamics. The crowded system of dimers exhibits two distinct diffusion regimes distinguished by the scaling exponent of the TAMSD, the dependence of the diffusivity on ϕ, and the features of the displacement-ACF. We attribute these regimes to a crowding-induced transition from viscous to viscoelastic diffusion upon growing ϕ. We also analyze the relative motion in the dimers, finding that larger ϕ suppress their vibrations and yield strongly non-Gaussian PDFs of rotational displacements. For the diffusion coefficients D(ϕ) of translational and rotational motion of the dumbbells an exponential decay with ϕ for weak and a power-law variation D(ϕ)∝(ϕ-ϕ^{★})^{2.4} for strong crowding is found. A comparison of simulation results with theoretical predictions for D(ϕ) is discussed and some relevant experimental systems are overviewed.

Simulating stochastic adsorption of diluted solute molecules at interfaces

This report uses Monte Carlo simulations to connect stochastic single-molecule and ensemble surface adsorption of molecules from dilute solutions. Monte Carlo simulations often use a fundamental time resolution to simulate each discrete step for each molecule. The adsorption rate obtained from such a simulation surprisingly contains an error compared to the results obtained from the traditional method. Simulating adsorption kinetics is interesting in many processes, such as mass transportation within cells, the kinetics of drug-receptor interactions, membrane filtration, and other general reaction kinetics in diluted solutions. Thus, it is important to understand the origin of the disagreement and find a way to correct the results. This report reviews the traditional model, explains the single-molecule simulations, and introduces a method to correct the results of adsorption rate. For example, one can bin finer time steps into time steps of interest to simulate the fractal diffusion or simply introduce a correction factor for the simulations. Then two model systems, self-assembled monolayer (SAM) and biosensing on the patterned surface, are simulated to check the accuracy of the equations. It is found that the adsorption rate of SAM is highly dependent on the conditions and the uncertainty is large. However, the biosensing system is relatively accurate. This is because the concentration gradient near the interface varies significantly with reaction conditions for SAMs while relatively stable for the biosensing system.

Simultaneous, Single-Particle Measurements of Size and Loading Give Insights into the Structure of Drug-Delivery Nanoparticles

Nanoparticles are a promising solution for delivery of a wide range of medicines and vaccines. Optimizing their design depends on being able to resolve, understand, and predict biophysical and therapeutic properties, as a function of design parameters. While existing tools have made great progress, gaps in understanding remain because of the inability to make detailed measurements of multiple correlated properties. Typically, an average measurement is made across a heterogeneous population, obscuring potentially important information. In this work, we develop and apply a method for characterizing nanoparticles with single-particle resolution. We use convex lens-induced confinement (CLiC) microscopy to isolate and quantify the diffusive trajectories and fluorescent intensities of individual nanoparticles trapped in microwells for long times. First, we benchmark detailed measurements of fluorescent polystyrene nanoparticles against prior data to validate our approach. Second, we apply our method to investigate the size and loading properties of lipid nanoparticle (LNP) vehicles containing silencing RNA (siRNA), as a function of lipid formulation, solution pH, and drug-loading. By taking a comprehensive look at the correlation between the intensity and size measurements, we gain insights into LNP structure and how the siRNA is distributed in the LNP. Beyond introducing an analytic for size and loading, this work allows for future studies of dynamics with single-particle resolution, such as LNP fusion and drug-release kinetics. The prime contribution of this work is to better understand the connections between microscopic and macroscopic properties of drug-delivery vehicles, enabling and accelerating their discovery and development.

Optothermal pulling, trapping, and assembly of colloids using nanowire plasmons

Optical excitation of colloids can be harnessed to realize soft matter systems that are out of equilibrium. In this paper, we present our experimental studies on the dynamics of silica colloids in the vicinity of a silver nanowire propagating surface plasmon polaritons (SPPs). Due to the optothermal interaction, the colloids are directionally pulled towards the excitation point of the nanowire. Having reached this point, they are spatio-temporally trapped around the excitation location. By increasing the concentration of colloids in the system, we observe multi-particle assembly around the nanowire. This process is thermophoretically driven and assisted by the SPPs. Furthermore, we find such an assembly to be sensitive to the excitation polarization at the input of the nanowire. Numerically-simulated temperature distribution around an illuminated nanowire corroborates sensitivity to the excitation polarization. Our study will find relevance in exploration of SPP-assisted optothermal pulling, trapping and assembly of colloids, and can serve as a test-bed of plasmon-driven active matter.

2D Colloids: Size- and Shape-Controlled 2D Materials at Fluid-Fluid Interfaces

Advances in synthesis of model 3D colloidal particles with exotic shapes and physical properties have enabled discovery of new 3D colloidal phases not observed in atomic systems, and simulations and quasi-2D studies suggest 2D colloidal systems have an even richer phase behavior. However, a model 2D (one-atom-thick) colloidal system has yet to be experimentally realized because of limitations in solution-phase exfoliation of 2D materials and other 2D particle fabrication technologies. Herein, we use a photolithography-based methodology to fabricate size- and shape-controlled monolayer graphene particles, and then transfer the particles to an air-water interface to study their dynamics and self-assembly in real-time using interference reflection microscopy. Results suggest the graphene particles behave as "hard" 2D colloidal particles, with entropy influencing the self-assembled structures. Additional evidence suggests the stability of the self-assembled structures manifests from the edge-to-edge van der Waals force between 2D particles. We also show graphene discs with diameters up to 50 μm exhibit significant Brownian motion under optical microscopy due to their low mass. This work establishes a facile methodology for creating model experimental systems of colloidal 2D materials, which will have a significant impact on our understanding of fundamental 2D physics. Finally, our results advance our understanding of how physical particle properties affect the interparticle interactions between monolayer 2D materials at fluid-fluid interfaces. This information can be used to guide the development of scalable synthesis techniques (e.g., solution-phase processing) to produce bulk suspensions of 2D materials with desired physical particle properties that can be used as building blocks for creating thin films with desired structures and properties via interfacial film assembly.

Single-cell tracking reveals super-spreading brain cancer cells with high persistence

Cell migration is a fundamental characteristic of vital processes such as tissue morphogenesis, wound healing and immune cell homing to lymph nodes and inflamed or infected sites. Therefore, various brain defect diseases, chronic inflammatory diseases as well as tumor formation and metastasis are associated with aberrant or absent cell migration. We embedded multicellular brain cancer spheroids in Matrigel™ and utilized single-particle tracking to extract the paths of cells migrating away from the spheroids. We found that - in contrast to local invasion - single cell migration is independent of Matrigel™ concentration and is characterized by high directionality and persistence. Furthermore, we identified a subpopulation of super-spreading cells with >200-fold longer persistence times than the majority of cells. These results highlight yet another aspect of cell heterogeneity in tumors.

Nonequilibrium, weak-field-induced cyclotron motion: A mechanism for magnetobiology

There is a long-time quest for understanding physical mechanisms of weak magnetic field interaction with biological matter. Two factors impeded the development of such mechanisms: first, a high (room) temperature of a cellular environment, where a weak, static magnetic field induces a (classically) zero equilibrium response. Second, the friction in the cellular environment is large, preventing a weak field to alter nonequilibrium processes such as a free diffusion of charges. Here we study a class of nonequilibrium steady states of a cellular ion in a confining potential, where the response to a (weak, homogeneous, static) magnetic field survives strong friction and thermal fluctuations. The magnetic field induces a rotational motion of the ion that proceeds with the cyclotron frequency. Such nonequilibrium states are generated by a white noise acting on the ion additionally to the nonlocal (memory-containing) friction and noise generated by an equilibrium thermal bath. The intensity of this white noise can be weak, i.e., much smaller than the thermal noise intensity.

The initiation of shear band formation in deformed metallic glasses from soft localized domains

It has long been thought that shear band (SB) formation in amorphous solids initiates from relatively "soft" regions in the material in which large-scale non-affine deformations become localized. The test of this hypothesis requires an effective means of identifying "soft" regions and their evolution as the material is deformed to varying degrees, where the metric of "softness" must also account for the effect of temperature on local material stiffness. We show that the mean square atomic displacement on a caging timescale ⟨u²⟩, the "Debye-Waller factor," provides a useful method for estimating the shear modulus of the entire material and, by extension, the material stiffness at an atomic scale. Based on this "softness" metrology, we observe that SB formation indeed occurs through the strain-induced formation of localized soft regions in our deformed metallic glass free-standing films. Unexpectedly, the critical strain condition for SB formation occurs when the softness (⟨u²⟩) distribution within the emerging soft regions approaches that of the interfacial region in its undeformed state, initiating an instability with similarities to the transition to turbulence. Correspondingly, no SBs arise when the material is so thin that the entire material can be approximately described as being "interfacial" in nature. We also quantify relaxation in the glass and the nature and origin of highly non-Gaussian particle displacements in the dynamically heterogeneous SB regions at times longer than the caging time.

Structure and dynamics of nanoconfined water and aqueous solutions

This review is devoted to discussing recent progress on the structure, thermodynamic, reactivity, and dynamics of water and aqueous systems confined within different types of nanopores, synthetic and biological. Currently, this is a branch of water science that has attracted enormous attention of researchers from different fields interested to extend the understanding of the anomalous properties of bulk water to the nanoscopic domain. From a fundamental perspective, the interactions of water and solutes with a confining surface dramatically modify the liquid's structure and, consequently, both its thermodynamical and dynamical behaviors, breaking the validity of the classical thermodynamic and phenomenological description of the transport properties of aqueous systems. Additionally, man-made nanopores and porous materials have emerged as promising solutions to challenging problems such as water purification, biosensing, nanofluidic logic and gating, and energy storage and conversion, while aquaporin, ion channels, and nuclear pore complex nanopores regulate many biological functions such as the conduction of water, the generation of action potentials, and the storage of genetic material. In this work, the more recent experimental and molecular simulations advances in this exciting and rapidly evolving field will be reported and critically discussed.

Three-dimensional tracking of nanoparticles by dual-color position retrieval in a double-core microstructured optical fiber

Elastic light scattering-based three-dimensional (3D) tracking of objects at the nanoscale level is essential for unlocking the dynamics of individual species or interactions in fields such as biology or surface chemistry. In this work, we introduce the concept of dual-color 3D tracking in a double-core microstructured optical fiber that for the first time allows for full 3D reconstruction of the trajectory of a diffusing nanoparticle in a water-filled fiber-integrated microchannel. The use of two single-mode cores provides two opposite decaying evanescent fields of different wavelengths within the microchannel, bypassing spatial domains of ambiguous correlation between the scattered intensity and position. The novelty of the fiber design is the use of two slightly different single-mode cores, preventing modal crosstalk and thus allowing for longitudinally invariant dual-color illumination across the entire field of view. To demonstrate the capabilities of the scheme, a single gold nanosphere (80 nm) diffusing in the water-filled microchannel was tracked for a large number of images (about 32 000) at a high frame rate (1.389 kHz) over a long time (23 s), with the determined hydrodynamic diameters matching expectations. The presented 3D tracking approach yields unique opportunities to unlock processes at the nanoscale level and is highly relevant for a multitude of fields, particularly within the context of understanding sophisticated interaction of diffusing species with functionalized surfaces within the context of bioanalytics, nanoscale materials science, surface chemistry or life science.

A Practical Guide to Analyzing and Reporting the Movement of Nanoscale Swimmers

The recent invention of nanoswimmers-synthetic, powered objects with characteristic lengths in the range of 10-500 nm-has sparked widespread interest among scientists and the general public. As more researchers from different backgrounds enter the field, the study of nanoswimmers offers new opportunities but also significant experimental and theoretical challenges. In particular, the accurate characterization of nanoswimmers is often hindered by strong Brownian motion, convective effects, and the lack of a clear way to visualize them. When coupled with improper experimental designs and imprecise practices in data analysis, these issues can translate to results and conclusions that are inconsistent and poorly reproducible. This Perspective follows the course of a typical nanoswimmer investigation from synthesis through to applications and offers suggestions for best practices in reporting experimental details, recording videos, plotting trajectories, calculating and analyzing mobility, eliminating drift, and performing control experiments, in order to improve the reliability of the reported results.

Diffusion of Lipid Nanovesicles Bound to a Lipid Membrane Is Associated with the Partial-Slip Boundary Condition

During diffusion of nanoparticles bound to a cellular membrane by ligand-receptor pairs, the distance to the laterally mobile interface is sufficiently short for their motion to depend not only on the membrane-mediated diffusivity of the tethers but also in a not yet fully understood manner on nanoparticle size and interfacial hydrodynamics. By quantifying diffusivity, velocity, and size of individual membrane-bound liposomes subjected to a hydrodynamic shear flow, we have successfully separated the diffusivity contributions from particle size and number of tethers. The obtained diffusion-size relations for synthetic and extracellular lipid vesicles are not well-described by the conventional no-slip boundary condition, suggesting partial slip as well as a significant diffusivity dependence on the distance to the lipid bilayer. These insights, extending the understanding of diffusion of biological nanoparticles at lipid bilayers, are of relevance for processes such as cellular uptake of viruses and lipid nanoparticles or labeling of cell-membrane-residing molecules.

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.

Modelling and simulation of field directed linear assembly of aerosol particles

Unlike liquid phase colloidal assembly, significantly changing the structure of fractal aggregates in the aerosol phase, is considered impractical. In this study, we discuss the possibility of applying external magnetic and electric fields, to tune the structure and fractal dimension (D_f) of aggregates grown in the aerosol phase. We show that external fields can be used to induce dipole moments in primary nanoparticles. We found that an ensemble of particles with induced dipole moments will interact through directional attractive and repulsive forces, leading to the formation of linear, chain-like aggregates with D_f ~ 1. The aggregate structure transition is dependent on the primary particle sizes, temperature and applied field strength which was evaluated by performing a hybrid ensemble/cluster-cluster aggregation Monte Carlo simulation. We demonstrate that the threshold magnetic field strength required to linearly assemble 10-500 nm particle sizes are practically achievable whereas the electric field required to assemble sub-100 nm particles are beyond the breakdown strength of most gases. To theoretically account for the enhanced coagulation rates due to attractive interactions, we have also derived a correction factor to both free molecular and transition regime coagulation kernel, based on magnetic dipolar interactions. A comparison has been made between the coagulation time-scales estimated by theory and simulation, with the estimated magnetization time-scales of the primary particles along with oscillation time period of the magnetic field, to demonstrate that sub-50 nm superparamagnetic primary particles can be magnetized and assembled at any temperature, while below the Curie temperature ferromagnetic particles of all sizes can be magnetized and assembled, given the applied field is higher than the threshold.

Impulse response function for Brownian motion

Motivated from the central role of the mean-square displacement and its second time-derivative - that is the velocity autocorrelation function in the description of Brownian motion and its implications to microrheology, we revisit the physical meaning of the first time-derivative of the mean-square displacement of Brownian particles. By employing a rheological analogue for Brownian motion, we show that the time-derivative of the mean-square displacement of Brownian microspheres with mass m and radius R immersed in any linear, isotropic viscoelastic material is identical to , where h(t) is the impulse response function (strain history γ(t), due to an impulse stress τ(t) = δ(t - 0)) of a rheological network that is a parallel connection of the linear viscoelastic material with an inerter with distributed inertance . The impulse response function of the viscoelastic material-inerter parallel connection derived in this paper at the stress-strain level of the rheological analogue is essentially the response function of the Brownian particles expressed at the force-displacement level by Nishi et al. after making use of the fluctuation-dissipation theorem. By employing the viscoelastic material-inerter rheological analogue we derive the mean-square displacement and its time-derivatives of Brownian particles immersed in a viscoelastic material described with a Maxwell element connected in parallel with a dashpot and we show that for Brownian motion of microparticles immersed in such fluid-like materials, the impulse response function h(t) maintains a finite constant value in the long term.

Non-associative phase separation in an evaporating droplet as a model for prebiotic compartmentalization

The synthetic pathways of life’s building blocks are envisaged to be through a series of complex prebiotic reactions and processes. However, the strategy to compartmentalize and concentrate biopolymers under prebiotic conditions remains elusive. Liquid-liquid phase separation is a mechanism by which membraneless organelles form inside cells, and has been hypothesized as a potential mechanism for prebiotic compartmentalization. Associative phase separation of oppositely charged species has been shown to partition RNA, but the strongly negative charge exhibited by RNA suggests that RNA-polycation interactions could inhibit RNA folding and its functioning inside the coacervates. Here, we present a prebiotically plausible pathway for non-associative phase separation within an evaporating all-aqueous sessile droplet. We quantitatively investigate the kinetic pathway of phase separation triggered by the non-uniform evaporation rate, together with the Marangoni flow-driven hydrodynamics inside the sessile droplet. With the ability to undergo liquid-liquid phase separation, the drying droplets provide a robust mechanism for formation of prebiotic membraneless compartments, as demonstrated by localization and storage of nucleic acids, in vitro transcription, as well as a three-fold enhancement of ribozyme activity. The compartmentalization mechanism illustrated in this model system is feasible on wet organophilic silica-rich surfaces during early molecular evolution.

Prebiotic compartmentalization could prove essential for the evolution of life. Guo et al. show that liquid-liquid separation in an aqueous two-phase system driven by evaporation may already suffice to facilitate chemical processes required for the RNA world hypothesis.

A Bibliometric Analysis and Network Visualisation of Human Mobility Studies from 1990 to 2020: Emerging Trends and Future Research Directions

Studies on human mobility have a long history with increasingly strong interdisciplinary connections across social science, environmental science, information and technology, computer science, engineering, and health science. However, what is lacking in the current research is a synthesis of the studies to identify the evolutional pathways and future research directions. To address this gap, we conduct a systematic review of human mobility-related studies published from 1990 to 2020. Drawing on the selected publications retrieved from the Web of Science, we provide a bibliometric analysis and network visualisation using CiteSpace and VOSviewer on the number of publications and year published, authors and their countries and afflictions, citations, topics, abstracts, keywords, and journals. Our findings show that human mobility-related studies have become increasingly interdisciplinary and multi-dimensional, which have been strengthened by the use of the so-called ‘big data’ from multiple sources, the development of computer technologies, the innovation of modelling approaches, and the novel applications in various areas. Based on our synthesis of the work by top cited authors we identify four directions for future research relating to data sources, modelling methods, applications, and technologies. We advocate for more in-depth research on human mobility using multi-source big data, improving modelling methods and integrating advanced technologies including artificial intelligence, and machine and deep learning to address real-world problems and contribute to social good.

Ballistic Brownian motion of supercavitating nanoparticles

We show that the Brownian motion of a nanoparticle (NP) can reach a ballistic limit when intensely heated to form supercavitation. As the NP temperature increases, its Brownian motion displays a sharp transition from normal to ballistic diffusion upon the formation of a vapor bubble to encapsulate the NP. Intense heating allows the NP to instantaneously extend the bubble boundary via evaporation, so the NP moves in a low-friction gaseous environment. We find the dynamics of the supercavitating NP is largely determined by the near field effect, i.e., highly localized vapor phase property in the vicinity of the NP.

Fast transport of HCl across a hydrophobic layer over macroscopic distances by using a Pt(II) compound as the transporter: micro- and nanometric aggregates as effective transporters

Bis-(diethyl-dithioxamidate)platinum(ii) is able to transport HCl from the donor aqueous phase to the receiving one over a mean distance of 12 cm in about 3 minutes across an organic membrane in the bulk, without stirring of the organic phase, i.e. at a rate far exceeding the unidirectional macroscopic diffusion coefficient. The way in which this surprising phenomenon can happen is linked to the behaviour of HCl which, because of dynamic interactions with [Pt(HEt2C2N2S2)2] (in which HCl is hosted as a tight ion pair [Pt(H2Et2C2N2S2)2][Cl]2) and chloroform molecules, gives rise to observable nanometric and micrometric domains, more dense than the surrounding bulk, whose formation and disaggregation processes accelerate the unidirectional macroscopic diffusion of HCl. Thermodynamic parameters obtained from the study of acid-base behaviour of the system Pt(ii) species/HCl/CHCl3 also agree with the proposed mechanism of HCl transport.

Interfacial viscoelasticity and jamming of colloidal particles at fluid-fluid interfaces: a review

Colloidal particles can be adsorbed at fluid-fluid interfaces, a phenomenon frequently observed in particle-stabilized foams, Pickering emulsions, and bijels. Particles adsorbed at interfaces exhibit unique physical and chemical behaviors, which affect the mechanical properties of the interface. Therefore, interfacial colloidal particles are of interest in terms of both fundamental and applied research. In this paper, we review studies on the adsorption of colloidal particles at fluid-fluid interfaces, from both thermodynamic and mechanical points of view, and discuss the differences as compared with surfactants and polymers. The unique particle interactions induced by the interfaces as well as the particle dynamics including lateral diffusion and contact line relaxation will be presented. We focus on the rearrangement of the particles and the resultant interfacial viscoelasticity. Particular emphasis will be given to the effects of particle shape, size, and surface hydrophobicity on the interfacial particle assembly and the mechanical properties of the obtained particle layer. We will also summarize recent advances in interfacial jamming behavior caused by adsorption of particles at interfaces. The buckling and cracking behavior of particle layers will be discussed from a mechanical perspective. Finally, we suggest several potential directions for future research in this area.

Role of Brownian Particle Velocity in Bioelectronic Emissions of DNA

Hypothesis: If double stranded DNA (dsDNA) is a charged biomolecule that moves in Earth's magnetic field at a Brownian velocity, then dsDNA may emit bioelectromagnetic waves at energies that reflect discrete genetic states. Methods: This work leverages the Planck-Einstein-de Broglie relationship and applies this concept to Brownian velocity of dsDNA within a cell, to describe the relationship between dsDNA mass, the average Brownian velocity of dsDNA within a cell, and the theoretical wavelengths at which DNA may emit bioelectromagnetic waves. Results: Theoretical emission wavelengths of dsDNA, derived from first principles, were found to correlate closely with experimentally observed emission wavelengths from spectroscopic measurements across various cellular systems in the literature. Conclusion: This work provides a conceptual basis for the potential for unification of bioelectromagnetism with Brownian motion, to elucidate how electromagnetic information can be generated at a subcellular level in biological systems. The implications of how finite mass changes in dsDNA can result in discrete emission wavelengths on electromagnetic timescales is discussed through the lens of genomics. Future refinements of this fundamental methodology may provide a conceptual basis to address previously unexplained multilevel phenomena in the field of biology and is general enough to be extended to other charged biomolecules at a subcellular level. Further exploration in this area could lead to new biological tool development that may augment current genomics methods.

Data-driven coarse-grained modeling of polymers in solution with structural and dynamic properties conserved

We present data-driven coarse-grained (CG) modeling for polymers in solution, which conserves the dynamic as well as structural properties of the underlying atomistic system. The CG modeling is built upon the framework of the generalized Langevin equation (GLE). The key is to determine each term in the GLE by directly linking it to atomistic data. In particular, we propose a two-stage Gaussian process-based Bayesian optimization method to infer the non-Markovian memory kernel from the data of the velocity autocorrelation function (VACF). Considering that the long-time behaviors of the VACF and memory kernel for polymer solutions can exhibit hydrodynamic scaling (algebraic decay with time), we further develop an active learning method to determine the emergence of hydrodynamic scaling, which can accelerate the inference process of the memory kernel. The proposed methods do not rely on how the mean force or CG potential in the GLE is constructed. Thus, we also compare two methods for constructing the CG potential: a deep learning method and the iterative Boltzmann inversion method. With the memory kernel and CG potential determined, the GLE is mapped onto an extended Markovian process to circumvent the expensive cost of directly solving the GLE. The accuracy and computational efficiency of the proposed CG modeling are assessed in a model star-polymer solution system at three representative concentrations. By comparing with the reference atomistic simulation results, we demonstrate that the proposed CG modeling can robustly and accurately reproduce the dynamic and structural properties of polymers in solution.

Microalgal ecotoxicity of nanoparticles: An updated review

Nowadays, nanotechnology and its related industries are becoming a rapidly explosive industry that offers many benefits to human life. However, along with the increased production and use of nanoparticles (NPs), their presence in the environment creates a high risk of increasing toxic effects on aquatic organisms. Therefore, a large number of studies focusing on the toxicity of these NPs to the aquatic organisms are carried out which used algal species as a common biological model. In this review, the influences of the physio-chemical properties of NPs and the response mechanisms of the algae on the toxicity of the NPs were discussed focusing on the "assay" studies. Besides, the specific algal toxicities of each type of NPs along with the NP-induced changes in algal cells of these NPs are also assessed. Almost all commonly-used NPs exhibit algal toxicity. Although the algae have similarities in the symptoms under NP exposure, the sensitivity and variability of each algae species to the inherent properties of each NPs are quite different. They depend strongly on the concentration, size, characteristics of NPs, and biochemical nature of algae. Through the assessment, the review identifies several gaps that need to be further studied to make an explicit understanding. The findings in the majority of studies are mostly in laboratory conditions and there are still uncertainties and contradictory/inconsistent results about the behavioral effects of NPs under field conditions. Besides, there remains unsureness about NP-uptake pathways of microalgae. Finally, the toxicity mechanisms of NPs need to be thoughtfully understood which is essential in risk assessment.

Hindered Diffusion near Fluid-Solid Interfaces: Comparison of Molecular Dynamics to Continuum Hydrodynamics

Studying the near-wall hindered diffusion of a particle suspended in a fluid is critical for understanding other more complex, confined systems. We provide a review of the previous experimental and simulation efforts trying to verify the classic calculations in hydrodynamics by Brenner and Faxén. We discuss some of the challenges of extracting the hindered diffusion constants from the mean squared displacements as often done in the literature. We demonstrate that the use of total force autocorrelation functions is a reliable alternative for calculating the diffusion constants without similar challenges for our molecular dynamics (MD) simulations. We find that the change in the diffusion constant in the perpendicular direction calculated in MD is roughly consistent with the hydrodynamic result by Brenner provided that they are normalized by the diffusion constant at the center between the two walls. However, the discrepancy grows large when the colloidal particle is very close to the wall where molecular details matter. Even though the agreement can be considerably improved when the attractions between the particles are made stronger to reduce slip to better fulfill the no-slip condition in MD, we report that there is an underlying difference between the range of the wall interactions with the colloidal particle predicted by MD and hydrodynamics.