搜索结果：Current topics in membranes

Lipid membranes are abundant in living organisms, where they constitute a surrounding shell for cells and their organelles. There are many circumstances in which the deformations of lipid membranes are involved in living cells: fusion and fission, membrane-mediated interaction between membrane inclusions, lipid-protein interaction, formation of pores, etc. In all of these cases, elastic parameters of lipid membranes are important for the description of membrane deformations, as these parameters determine energy barriers and characteristic times of membrane-involved phenomena. Since the development of molecular dynamics (MD), a variety of in silico methods have been proposed for the determination of elastic parameters of simulated lipid membranes. These MD methods allow for the consideration of details unattainable in experimental techniques and represent a distinct scientific field, which is rapidly developing. This work provides a review of these MD approaches with a focus on theoretical aspects. Two main challenges are identified: (i) the ambiguity in the transition from the continuum description of elastic theories to the discrete representation of MD simulations, and (ii) the d

For an electron, a spin-1/2 particle, the spin charge $\mathbf{s}$, a real pseudovector with constant length, could determine the spin polarization properties in quantum mechanics. Since spin density $ρ_{\mathbf{s}}$ could be expressed as the product of probability density $ρ$ and the spin charge $\mathbf{s}$, the spin continuity equation could be derived from fundamental principles of quantum mechanics and the definition of spin current arise naturally. Only in some specific conditions the conventional definition of spin current coincides with ours. The equilibrium spin currents would vanish automatically in two-dimensional electron gas with Rashba spin-orbit interaction by using our definition of spin current.

We present Monte Carlo simulations of an ultra coarse-grained lipid bilayer with different number of lipids on both leaflets. In the simulations, we employ a new method for measuring the elastic parameters of the membrane, including the area per lipid, area elasticity modulus, and bending rigidity. The method also allows to measure the spontaneous curvature and non-local bending modulus, which are not accessible by standard computer simulations with periodic boundary conditions. For membranes with lipid densities much smaller than the liquid to gel transition density, $ρ_g$, we find a very good agreement between the simulation results and the theory expressing the bilayer elastic free energy as the sum of quadratic free energies in the strains associated with the area density and the local curvature of the monolayers. The theory fails when the lipid area density (in the symmetric reference case) is only slightly smaller than $ρ_g$. Increasing the degree of asymmetry and changing the density of the condensed leaflet to a value larger than $ρ_g$, causes the layer to phase separate between regions with distinct densities which, in turn, may also induce density variations in the dilate

Single crystal, nanoscale diamond membranes are highly sought after for a variety of applications including nanophotonics, nanoelectronics and quantum information science. However, so far, the availability of conductive diamond membranes remained an unreachable goal. In this work we present a complete nanofabrication methodology for engineering high aspect ratio, electrically active single crystal diamond membranes. The membranes have large lateral directions, exceeding 500x500 um2 and are only several hundreds of nanometers thick. We further realize vertical single crystal p-n junctions, made from the diamond membranes that exhibit onset voltages of ~ 10V and a current of several mA. Moreover, we deterministically introduce optically active color centers into the membranes, and demonstrate for the first time a single crystal nanoscale diamond LED. The robust and scalable approach to engineer the electrically active single crystal diamond membranes, offers new pathways for advanced nanophotonics, nanoelectronics and optomechanics devices employing diamond.

We examine the validity of the principle of mass conservation for solutions of some typical equations in the theory of nonlinear diffusion, including equations in standard differential form and also their fractional counterparts. In Part 1, consisting of the first 9 sections, we use as main examples the heat equation, the porous medium equation and the $p$-Laplacian equation. Though these equations have the form of conservation laws, it happens that in some ranges of exponents the solutions posed in the whole Euclidean space lose mass in time. From the start we pay attention to the close connection between the validity of mass conservation and the existence of finite-mass self-similar solutions, as well as their role in the asymptotic behaviour of more general classes of solutions. Describing the extent of this connection is the common thread throughout the manuscript. When mass conservation does not hold, we are led to examine the situation when it is replaced by its extreme alternative, extinction in finite time, a very surprising fact. The next sections extend the detailed study to other models that occupy a relevant role in the current literature. Thus, the 3 sections of Part 2

Electrochemical phenomena in biology often unfold in confined geometries where micrometer- to millimeter-scale domains coexist with nanometer-scale interfacial diffuse charge layers. We analyze a model lipid membrane-electrolyte system where an ion channel-like current flows across the membrane while parallel electrodes simultaneously apply a step voltage, emulating an extrinsic electric field. Matched asymptotic expansions of the Poisson-Nernst-Planck equations show that, under physiological conditions, the diffuse charge layers rapidly reach a quasi-steady state, and the bulk electrolyte remains electroneutral. As a result, all free charge is confined to the nanometer-scale screening layers at the membrane and electrode interfaces. The bulk electric potential satisfies Laplace's equation, and is dynamically coupled to the interfacial layers through time-dependent boundary conditions. This multiscale coupling partitions the space-time response into distinct regimes. At sufficiently long times, we show that the system can be represented by an equivalent circuit analogous to those used in classical cable theory. We derive closed-form expressions of the transmembrane potential within

Current fluctuations in pure lipid membranes have been shown to occur under the influence of transmembrane electric fields (electroporation) as well as a result from structural rearrangements of the lipid bilayer during phase transition (soft perforation). We demonstrate that the ion permeability during lipid phase transition exhibits the same qualitative temperature dependence as the macroscopic heat capacity of a D15PC/DOPC vesicle suspension. Microscopic current fluctuations show distinct characteristics for each individual phase state. While current fluctuations in the fluid phase show spike-like behaviour of short time scales (~ 2ms) with a narrow amplitude distribution, the current fluctuations during lipid phase transition appear in distinct steps with time scales in the order of ~ 20ms. 1 We propose a theoretical explanation for the origin of time scales and permeability based on a linear relationship between lipid membrane susceptibilities and relaxation times in the vicinity of the phase transition.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by dementia and memory loss in the elderly population. The amyloid-$β$ peptide (A$β$ 1-42) is one of the main pathogenic factors in AD and is known to cause damage to neuronal cellular membranes. There is no cure currently available for AD and new approaches, including preventive strategies, are highly desirable. In this work, we explore the possibility of protecting neuronal membranes from amyloid-induced damage with naturally existing sugar trehalose. Trehalose has been shown to protect plant cellular membranes in extreme conditions and modify A$β$ misfolding. We hypothesize that trehalose can protect the neuronal membrane from amyloid toxicity. In this work, we studied the protective effect of trehalose against A$β$-induced damage in model lipid membranes (DPPC/POPC/Cholesterol) to mimic neuronal membranes using atomic force microscopy (AFM) and black lipid membrane (BLM) electrophysiology. Our results demonstrated that A$β$1-42 damaged membranes and led to ionic current leakage across these membranes due to the formation of various defects and pores. The presence of trehalose reduced the ion current across me

With YouTube's growing importance as a news platform, its recommendation system came under increased scrutiny. Recognizing YouTube's recommendation system as a broadcaster of media, we explore the applicability of laws that require broadcasters to give important political, ideological, and social groups adequate opportunity to express themselves in the broadcasted program of the service. We present audits as an important tool to enforce such laws and to ensure that a system operates in the public's interest. To examine whether YouTube is enacting certain biases, we collected video recommendations about political topics by following chains of ten recommendations per video. Our findings suggest that YouTube's recommendation system is enacting important biases. We find that YouTube is recommending increasingly popular but topically unrelated videos. The sadness evoked by the recommended videos decreases while the happiness increases. We discuss the strong popularity bias we identified and analyze the link between the popularity of content and emotions. We also discuss how audits empower researchers and civic hackers to monitor complex machine learning (ML)-based systems like YouTube's

The dynamics of suspended two-dimensional (2D) materials has received increasing attention during the last decade, yielding new techniques to study and interpret the physics that governs the motion of atomically thin layers. This has led to insights into the role of thermodynamic and nonlinear effects as well as the mechanisms that govern dissipation and stiffness in these resonators. In this review, we present the current state-of-the-art in the experimental study of the dynamics of 2D membranes. The focus will be both on the experimental measurement techniques and on the interpretation of the physical phenomena exhibited by atomically thin membranes in the linear and nonlinear regimes. We will show that resonant 2D membranes have emerged both as sensitive probes of condensed matter physics in ultrathin layers, and as sensitive elements to monitor small external forces or other changes in the environment. New directions for utilizing suspended 2D membranes for material characterization, thermal transport, and gas interactions will be discussed and we conclude by outlining the challenges and opportunities in this upcoming field.

The Topics API for the web is Google's privacy-enhancing alternative to replace third-party cookies. Results of prior work have led to an ongoing discussion between Google and research communities about the capability of Topics to trade off both utility and privacy. The central point of contention is largely around the realism of the datasets used in these analyses and their reproducibility; researchers using data collected on a small sample of users or generating synthetic datasets, while Google's results are inferred from a private dataset. In this paper, we complement prior research by performing a reproducible assessment of the latest version of the Topics API on the largest and publicly available dataset of real browsing histories. First, we measure how unique and stable real users' interests are over time. Then, we evaluate if Topics can be used to fingerprint the users from these real browsing traces by adapting methodologies from prior privacy studies. Finally, we call on web actors to perform and enable reproducible evaluations by releasing anonymized distributions. We find that for the 1207 real users in this dataset, the probability of being re-identified across websites

Oily wastewater is an extensive source of pollution to soil and water, and its harmless treatment is of great importance for the protection of our aquatic ecosystems. Membrane filtration is highly desirable for removing oil from oily water because it has the advantages of energy efficiency, easy processing and low maintenance cost. However, membrane fouling during filtration leads to severe flux decline and impedes long-term operation of membranes in practical wastewater treatment. Membrane fouling includes reversible fouling and irreversible fouling. The fouling mechanisms have been explored based on classical fouling models, and on oil droplet behaviors (such as droplet deposition, accumulation, coalescence and wetting) on the membranes. Membrane fouling is dominated by droplet-membrane interaction, which is influenced by the properties of the membrane (e.g., surface chemistry, structure and charge) and the wastewater (e.g., compositions and concentrations) as well as the operation conditions. Typical membrane antifouling strategies, such as surface hydrophilization, zwitterionic polymer coating, photocatalytic decomposition and electrically enhanced antifouling are reviewed, and

A classical $E_{d(d)}$-invariant Hamiltonian formulation of world-volume theories of half-BPS p-branes in type IIb and eleven-dimensional supergravity is proposed, extending known results to $d \leq 6$. It consists of a Hamiltonian, characterised by a generalised metric, and a current algebra constructed s.t. it reproduces the $E_{d(d)}$ generalised Lie derivative. $E_{d(d)}$-covariance necessitates the introduction of so-called charges, specifying the type of p-brane and the choice of section. For p>2, currents of p-branes are generically non-geometric due to the imposition of U-duality, e.g. the M5-currents contain coordinates associated to the M2-momentum. A derivation of the $E_{d(d)}$-invariant current algebra from a canonical Poisson structure is in general not possible. At most, one can derive a current algebra associated to para-Hermitian exceptional geometry. The membrane in the SL(5)-theory is studied in detail. It is shown that in a generalised frame the current algebra is twisted by the generalised fluxes. As a consistency check, the double dimensional reduction from membranes in M-theory to strings in type IIa string theory is performed. Many features generalise to

Membranes are of great technological and biological as well as theoretical interest. Two main classes of membranes can be distinguished: Fluid membranes and polymerized, tethered membranes. Here, we review progress in the theoretical understanding of polymerized membranes, i.e. membranes with a fixed internal connectivity. We start by collecting basic physical properties, clarifying the role of bending rigidity and disorder, theoretically and experimentally as well as numerically. We then give a thorough introduction into the theory of self-avoiding membranes, or more generally non-local field theories with delta-like interactions. Based on a proof of perturbative renormalizability for non-local field-theories, renormalization group calculations can be performed up to 2-loop order, which in 3 dimensions predict a crumpled phase with fractal dimension of about 2.4. The tricritical behavior of membranes is discussed and shown to be quite different from that of polymers. Dynamical properties are studied in the same frame-work. Along the same lines, disorder can be included leading to interesting applications. We also construct a generalization of the O(N)-model, which in the limit N-&

As machine learning becomes more widely adopted across domains, it is critical that researchers and ML engineers think about the inherent biases in the data that may be perpetuated by the model. Recently, many studies have shown that such biases are also imbibed in Graph Neural Network (GNN) models if the input graph is biased, potentially to the disadvantage of underserved and underrepresented communities. In this work, we aim to mitigate the bias learned by GNNs by jointly optimizing two different loss functions: one for the task of link prediction and one for the task of demographic parity. We further implement three different techniques inspired by graph modification approaches: the Global Fairness Optimization (GFO), Constrained Fairness Optimization (CFO), and Fair Edge Weighting (FEW) models. These techniques mimic the effects of changing underlying graph structures within the GNN and offer a greater degree of interpretability over more integrated neural network methods. Our proposed models emulate microscopic or macroscopic edits to the input graph while training GNNs and learn node embeddings that are both accurate and fair under the context of link recommendations. We dem

Polyamide membranes, such as nanofiltration (NF) and reverse osmosis (RO) membranes, are widely used for water desalination and purification. However, the mechanisms of solute transport and solute rejection due to charge interactions remain unclear at the molecular level. Here we use molecular dynamics (MD) simulations to examine the transport of single-solute feeds through charged nanofiltration membranes with different membrane charge concentrations of COO$^{\text{-}}$ and NH$_2\!^+$ corresponding to different pH levels. Results show that Na$^+$ and Cl$^{\text{-}}$ solute ions are better rejected when the membrane has a higher concentration of negatively charged groups, corresponding to a higher pH, whereas CaCl$_2$ is well-rejected at all pH levels studied. These results are consistent with experimental findings which are performed at the same pH conditions as simulation setup. Moreover, solute transport behavior depends on the membrane functional group distribution. When COO$^{\text{-}}$ functional groups are concentrated at membrane feed surface, ion permeation into the membrane is reduced. Counter-ions tend to associate with charged functional groups while co-ions seem to pas

Interactions mediated by the cell membrane between inclusions, such as membrane proteins or antimicrobial peptides, play important roles in their biological activity. They also constitute a fascinating challenge for physicists, since they test the boundaries of our understanding of self-assembled lipid membranes, which are remarkable examples of two-dimensional complex fluids. Inclusions can couple to various degrees of freedom of the membrane, resulting in different types of interactions. In this chapter, we review the membrane-mediated interactions that arise from direct constraints imposed by inclusions on the shape of the membrane. These effects are generic and do not depend on specific chemical interactions. Hence, they can be studied using coarse-grained soft matter descriptions. We deal with long-range membrane-mediated interactions due to the constraints imposed by inclusions on membrane curvature and on its fluctuations. We also discuss the shorter-range interactions that arise from the constraints on membrane thickness imposed by inclusions presenting a hydrophobic mismatch with the membrane.

Nonlinear science has evolved significantly over the 35 years since the launch of the journal Chaos. This Focus Issue, dedicated to the 80th Birthday of its founding editor-in-chief, David K. Campbell, brings together a selection of contributions on influential topics, many of which were advanced by Campbell's own research program and leadership role. The topics include new phenomena and method development in the realms of network dynamics, machine learning, quantum and material systems, chaos and fractals, localized states, and living systems, with a good balance of literature review, original contributions, and perspectives for future research.

Deep learning has been proposed as an efficient alternative for the numerical approximation of PDE solutions, offering fast, iterative simulation of PDEs through the approximation of solution operators. However, deep learning solutions have struggle to perform well over long prediction durations due to the accumulation of auto-regressive error, which is compounded by the inability of models to conserve physical quantities. In this work, we present conserved quantity correction, a model-agnostic technique for incorporation physical conservation criteria within deep learning models. Our results demonstrate consistent improvement in the long-term stability of auto-regressive neural operator models, regardless of the model architecture. Furthermore, we analyze the performance of neural operators from the spectral domain, highlighting significant limitations of present architectures. These results highlight the need for future work to consider architectures that place specific emphasis on high frequency components, which are integral to the understanding and modeling of turbulent flows.