In-plane structure of the electric double layer in the primitive model using classical density functional theory.

The electric double layer (EDL) has a pivotal role in screening charges on surfaces as in supercapacitor electrodes or colloidal and polymer solutions. Its structure is determined by correlations between the finite-sized ionic charge carriers of the underlying electrolyte, and, this way, these correlations affect the properties of the EDL and of applications utilizing EDLs. We study the structure of EDLs within classical density functional theory (DFT) in order to uncover whether a structural transition in the first layer of the EDL that is driven by changes in the surface potential depends on specific particle interactions or has a general footing. This transition has been found in full-atom simulations. Thus far, investigating the in-plane structure of the EDL for the primitive model (PM) using DFT has proved a challenge. We show here that the use of an appropriate functional predicts the in-plane structure of EDLs in excellent agreement with molecular dynamics simulations. This provides the playground to investigate how the structure factor within a layer parallel to a charged surface changes as a function of both the applied surface potential and its separation from the surface. We discuss pitfalls in properly defining an in-plane structure factor and fully map out the structure of the EDL within the PM for a wide range of electrostatic electrode potentials. However, we do not find any signature of a structural crossover and conclude that the previously reported effect is not fundamental but rather occurs due to the specific force field of ions used in the simulations.

Perturbation of the host cell Ca2+ homeostasis and ER-mitochondria contact sites by the SARS-CoV-2 structural proteins E and M.

Coronavirus disease (COVID-19) is a contagious respiratory disease caused by the SARS-CoV-2 virus. The clinical phenotypes are variable, ranging from spontaneous recovery to serious illness and death. On March 2020, a global COVID-19 pandemic was declared by the World Health Organization (WHO). As of February 2023, almost 670 million cases and 6,8 million deaths have been confirmed worldwide. Coronaviruses, including SARS-CoV-2, contain a single-stranded RNA genome enclosed in a viral capsid consisting of four structural proteins: the nucleocapsid (N) protein, in the ribonucleoprotein core, the spike (S) protein, the envelope (E) protein, and the membrane (M) protein, embedded in the surface envelope. In particular, the E protein is a poorly characterized viroporin with high identity amongst all the ß-coronaviruses (SARS-CoV-2, SARS-CoV, MERS-CoV, HCoV-OC43) and a low mutation rate. Here, we focused our attention on the study of SARS-CoV-2 E and M proteins, and we found a general perturbation of the host cell calcium (Ca2+) homeostasis and a selective rearrangement of the interorganelle contact sites. In vitro and in vivo biochemical analyses revealed that the binding of specific nanobodies to soluble regions of SARS-CoV-2 E protein reversed the observed phenotypes, suggesting that the E protein might be an important therapeutic candidate not only for vaccine development, but also for the clinical management of COVID designing drug regimens that, so far, are very limited.

Anomalous Underscreening in the Restricted Primitive Model.

Underscreening is a collective term for charge correlations in electrolytes decaying slower than the Debye length. Anomalous underscreening refers to phenomenology that cannot be attributed alone to steric interactions. Experiments with concentrated electrolytes and ionic fluids report anomalous underscreening, which so far has not been observed in simulation. We present Molecular Dynamics simulation results exhibiting anomalous underscreening that can be connected to cluster formation. A theory that accounts for ion pairing confirms the trend. Our results challenge the classic understanding of dense electrolytes impacting the design of technologies for energy storage and conversion.

Charge Mapping of Pseudomonas aeruginosa Using a Hopping Mode Scanning Ion Conductance Microscopy Technique.

Scanning ion conductance microscopy (SICM) is a topographic imaging technique capable of probing biological samples in electrolyte conditions. SICM enhancements have enabled surface charge detection based on voltage-dependent signals. Here, we show how the hopping mode SICM method (HP-SICM) can be used for rapid and minimally invasive surface charge mapping. We validate our method usingPseudomonas aeruginosaPA14 (PA) cells and observe a surface charge density of σPA = -2.0 ± 0.45 mC/m2 that is homogeneous within the ∼80 nm lateral scan resolution. This biological surface charge is detected from at least 1.7 µm above the membrane (395× the Debye length), and the long-range charge detection is attributed to electroosmotic amplification. We show that imaging with a nanobubble-plugged probe reduces perturbation of the underlying sample. We extend the technique to PA biofilms and observe a charge density exceeding -20 mC/m2. We use a solid-state calibration to quantify surface charge density and show that HP-SICM cannot be quantitatively described by a steady-state finite element model. This work contributes to the body of scanning probe methods that can uniquely contribute to microbiology and cellular biology.

The primitive model in classical density functional theory: beyond the standard mean-field approximation.

The primitive model describes ions by point charges with an additional hard-core interaction. In classical density-functional theory (DFT) the mean-field electrostatic contribution can be obtained from the first order of a functional perturbation of the pair potential for an uncharged reference system of hard spheres. This mean-field electrostatic term particularly contributes at particle separations that are forbidden due to hard-core overlap. In this work we modify the mean-field contribution such that the pair potential is constant for distances smaller than the contact distance of the ions. We motivate our modification by the underlying splitting of the potential, which is similar to the splitting of the Weeks-Chandler-Andersen potential and leads to higher-order terms in the respective expansion of the functional around the reference system. The resulting formalism involves weighted densities similar to the ones found in fundamental measure theory. To test our modifications, we analyze and compare density profiles, direct and total correlation functions, and the thermodynamic consistency of the functional via a widely established sum rule and the virial pressure formula for our modified functional, for established functionals, and for data from computer simulations. We found that our modifications clearly show improvements compared to the standard mean-field functional, especially when predicting layering effects and direct correlation functions in high concentration scenarios; for the latter we also find improved consistency when calculated via different thermodynamic routes. In conclusion, we demonstrate how modifications toward higher order corrections beyond mean-field functionals can be made and how they perform, by this providing a basis for systematic future improvements in classical DFT for the description of electrostatic interactions.

Extension of the primitive model by hydration shells and its impact on the reversible heat production during the buildup of the electric double layer.

Recently, the reversible heat production during the electric double layer (EDL) buildup in a sodium chloride solution was measured experimentally [Janssen et al., Phys. Rev. Lett. 119, 166002 (2017)] and matched with theoretical predictions from density functional theory and molecular dynamics simulations [Glatzel et al., J. Chem. Phys. 154, 064901 (2021)]. In the latter, it was found that steric interactions of ions with the electrode's walls, which result in the so-called Stern layer, are sufficient to explain the experimental results. As only symmetric ion sizes in a restricted primitive model were examined, it is instructive to investigate systems of unequal ion sizes that lead to modified Stern layers. In this work, we explore the impact of ion asymmetry on the reversible heat production for each electrode separately. In this context, we further study an extension of the primitive model where hydration shells of ions can evade in the vicinity of electrode's walls. We find a strong dependence on system parameters such as particle sizes and the total volume taken by particles. Here, we even found situations where one electrode was heated and the other electrode was cooled at the same time during charging, while, in sum, both electrodes together behaved very similarly to the already mentioned experimental results. Thus, heat production should also be measured in experiments for each electrode separately. By this, the importance of certain ingredients that we proposed to model electrolytes could be confirmed or ruled out experimentally, finally leading to a deeper understanding of the physics of EDLs.

Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes.

Electrolytes play an important role in a plethora of applications ranging from energy storage to biomaterials. Notwithstanding this, the structure of concentrated electrolytes remains enigmatic. Many theoretical approaches attempt to model the concentrated electrolyte by introducing the idea of ion pairs, with ions either being tightly "paired" with a counter-ion or "free" to screen charge. In this study, we reframe the problem into the language of computational statistics and test the null hypothesis that all ions share the same local environment. Applying the framework to molecular dynamics simulations, we find that this null hypothesis is not supported by data. Our statistical technique suggests the presence of two distinct local ionic environments at intermediate concentrations, whose differences surprisingly originate in like charge correlations rather than unlike charge attraction. Through considering the effect of these "aggregated" and "non-aggregated" states on bulk properties including effective ion concentration and dielectric constant, we identify a scaling relation between the effective screening length and theoretical Debye length, which applies across different dielectric constants and ion concentrations.

Reversible heat production during electric double layer buildup depends sensitively on the electrolyte and its reservoir.

Several modern technologies for energy storage and conversion are based on the screening of electric charge on the surface of porous electrodes by ions in an adjacent electrolyte. This so-called electric double layer (EDL) exhibits an intricate interplay with the electrolyte's temperature that was the focus of several recent studies. In one of them, Janssen et al. [Phys. Rev. Lett. 119, 166002 (2017)] experimentally determined the ratio Qrev/Wel of reversible heat flowing into a supercapacitor during an isothermal charging process and the electric work applied therein. To rationalize that data, here, we determine Qrev/Wel within different models of the EDL using theoretical approaches such as density functional theory (DFT) as well as molecular dynamics simulations. Applying mainly the restricted primitive model, we find quantitative support for a speculation of Janssen et al. that steric ion interactions are key to the ratio Qrev/Wel. Here, we identified the entropic contribution of certain DFT functionals, which grants direct access to the reversible heat. We further demonstrate how Qrev/Wel changes when calculated in different thermodynamic ensembles and processes. We show that the experiments of Janssen et al. are explained best by a charging process at fixed bulk density or in a "semi-canonical" system. Finally, we find that Qrev/Wel significantly depends on parameters such as pore and ion size, salt concentration, and valencies of the cations and anions of the electrolyte. Our findings can guide further heat production measurements and can be applied in studies on, for instance, nervous conduction, where reversible heat is a key element.

Continuum percolation expressed in terms of density distributions.

We present an approach to derive the connectivity properties of pairwise interacting n-body systems in thermal equilibrium. We formulate an integral equation that relates the pair connectedness to the distribution of nearest neighbors. For one-dimensional systems with nearest-neighbor interactions, the nearest-neighbor distribution is in turn related to the pair-correlation function g through a simple integral equation. As a consequence, for those systems, we arrive at an integral equation relating g to the pair connectedness, which is readily solved even analytically if g is specified analytically. We demonstrate the procedure for a variety of pair potentials including fully penetrable spheres as well as impenetrable spheres, the only two systems for which analytical results for the pair connectedness exist. However, the approach is not limited to nearest-neighbor interactions in one dimension. Hence, we also outline the treatment of external fields and long-range interactions and we illustrate how the formalism can applied to higher-dimensional systems using the three-dimensional ideal gas as an example.

Phenazine oxidation by a distal electrode modulates biofilm morphogenesis.

Microbes living in biofilms, dense assemblages of cells, experience limitation for resources such as oxygen when cellular consumption outpaces diffusion. The pathogenic bacterium Pseudomonas aeruginosa has strategies for coping with hypoxia that support cellular redox balancing in biofilms; these include (1) increasing access to oxygen by forming wrinkles in the biofilm surface and (2) electrochemically reducing endogenous compounds called phenazines, which can shuttle electrons to oxidants available at a distance. Phenazine-mediated extracellular electron transfer (EET) has been shown to support survival for P. aeruginosa cells in anoxic liquid cultures, but the physiological relevance of EET over a distance for P. aeruginosa biofilms has remained unconfirmed. Here, we use a custom-built electrochemistry setup to show that phenazine-mediated electron transfer at a distance inhibits wrinkle formation in P. aeruginosa biofilms. This result demonstrates that phenazine-dependent EET to a distal oxidant affects biofilm morphogenesis.

Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks.

The massively parallel nature of biological information processing plays an important role due to its superiority in comparison to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.

Demonstrating Advantages of Neuromorphic Computation: A Pilot Study.

Neuromorphic devices represent an attempt to mimic aspects of the brain's architecture and dynamics with the aim of replicating its hallmark functional capabilities in terms of computational power, robust learning and energy efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic system to implement a proof-of-concept demonstration of reward-modulated spike-timing-dependent plasticity in a spiking network that learns to play a simplified version of the Pong video game by smooth pursuit. This system combines an electronic mixed-signal substrate for emulating neuron and synapse dynamics with an embedded digital processor for on-chip learning, which in this work also serves to simulate the virtual environment and learning agent. The analog emulation of neuronal membrane dynamics enables a 1000-fold acceleration with respect to biological real-time, with the entire chip operating on a power budget of 57 mW. Compared to an equivalent simulation using state-of-the-art software, the on-chip emulation is at least one order of magnitude faster and three orders of magnitude more energy-efficient. We demonstrate how on-chip learning can mitigate the effects of fixed-pattern noise, which is unavoidable in analog substrates, while making use of temporal variability for action exploration. Learning compensates imperfections of the physical substrate, as manifested in neuronal parameter variability, by adapting synaptic weights to match respective excitability of individual neurons.

High bandwidth approaches in nanopore and ion channel recordings - A tutorial review.

Transport processes through ion-channel proteins, protein pores, or solid-state nanopores are traditionally recorded with commercial patch-clamp amplifiers. The bandwidth of these systems is typically limited to 10 kHz by signal-to-noise-ratio (SNR) considerations associated with these measurement platforms. At high bandwidth, the input-referred current noise in these systems dominates, determined by the input-referred voltage noise of the transimpedance amplifier applied across the capacitance at the input of the amplifier. This capacitance arises from several sources: the parasitic capacitance of the amplifier itself; the capacitance of the lipid bilayer harboring the ion channel protein (or the membrane used to form the solid-state nanopore); and the capacitance from the interconnections between the electronics and the membrane. Here, we review state-of-the-art applications of high-bandwidth conductance recordings of both ion channels and solid-state nanopores. These approaches involve tightly integrating measurement electronics fabricated in complementary metal-oxide semiconductors (CMOS) technology with lipid bilayer or solid-state membranes. SNR improvements associated with this tight integration push the limits of measurement bandwidths, in some cases in excess of 10 MHz. Recent case studies demonstrate the utility of these approaches for DNA sequencing and ion-channel recordings. In the latter case, studies with extended bandwidth have shown the potential for providing new insights into structure-function relations of these ion-channel proteins as the temporal resolutions of functional recordings matches time scales achievable with state-of-the-art molecular dynamics simulations.

Wavelet Denoising of High-Bandwidth Nanopore and Ion-Channel Signals.

Recent work has pushed the noise-limited bandwidths of solid-state nanopore conductance recordings to more than 5 MHz and of ion channel conductance recordings to more than 500 kHz through the use of integrated complementary metal-oxide-semiconductor (CMOS) integrated circuits. Despite the spectral spread of the pulse-like signals that characterize these recordings when a sinusoidal basis is employed, Bessel filters are commonly used to denoise these signals to acceptable signal-to-noise ratios (SNRs) at the cost of losing many of the faster temporal features. Here, we report improvements to the SNR that can be achieved using wavelet denoising instead of Bessel filtering. When combined with state-of-the-art high-bandwidth CMOS recording instrumentation, we can reduce baseline noise levels by over a factor of 4 compared to a 2.5 MHz Bessel filter while retaining transient properties in the signal comparable to this filter bandwidth. Similarly, for ion-channel recordings, we achieve a temporal response better than a 100 kHz Bessel filter with a noise level comparable to that achievable with a 25 kHz Bessel filter. Improvements in SNR can be used to achieve robust statistical analyses of these recordings, which may provide important insights into nanopore translocation dynamics and mechanisms of ion-channel function.

Screening Lengths in Ionic Fluids.

The decay of correlations in ionic fluids is a classical problem in soft matter physics that underpins applications ranging from controlling colloidal self-assembly to batteries and supercapacitors. The conventional wisdom, based on analyzing a solvent-free electrolyte model, suggests that all correlation functions between species decay with a common decay length in the asymptotic far field limit. Nonetheless, a solvent is present in many electrolyte systems. We show using an analytical theory and molecular dynamics simulations that multiple decay lengths can coexist in the asymptotic limit as well as at intermediate distances once a hard sphere solvent is considered. Our analysis provides an explanation for the recently observed discontinuous change in the structural force across a thin film of ionic liquid-solvent mixtures as the composition is varied, as well as reframes recent debates in the literature about the screening length in concentrated electrolytes.

Three-body correlations and conditional forces in suspensions of active hard disks.

Self-propelled Brownian particles show rich out-of-equilibrium physics, for instance, the motility-induced phase separation (MIPS). While decades of studying the structure of liquids have established a deep understanding of passive systems, not much is known about correlations in active suspensions. In this work we derive an approximate analytic theory for three-body correlations and forces in systems of active Brownian disks starting from the many-body Smoluchowski equation. We use our theory to predict the conditional forces that act on a tagged particle and their dependence on the propulsion speed of self-propelled disks. We identify preferred directions of these forces in relation to the direction of propulsion and the positions of the surrounding particles. We further relate our theory to the effective swimming speed of the active disks, which is relevant for the physics of MIPS. To test and validate our theory, we additionally run particle-resolved computer simulations, for which we explicitly calculate the three-body forces. In this context, we discuss the modeling of active Brownian swimmers with nearly hard interaction potentials. We find very good agreement between our simulations and numerical solutions of our theory, especially for the nonequilibrium pair-distribution function. For our analytical results, we carefully discuss their range of validity in the context of the different levels of approximation we applied. This discussion allows us to study the individual contribution of particles to three-body forces and to the emerging structure. Thus, our work sheds light on the collective behavior, provides the basis for further studies of correlations in active suspensions, and makes a step towards an emerging liquid state theory.

Single-channel recordings of RyR1 at microsecond resolution in CMOS-suspended membranes.

Single-channel recordings are widely used to explore functional properties of ion channels. Typically, such recordings are performed at bandwidths of less than 10 kHz because of signal-to-noise considerations, limiting the temporal resolution available for studying fast gating dynamics to greater than 100 µs. Here we present experimental methods that directly integrate suspended lipid bilayers with high-bandwidth, low-noise transimpedance amplifiers based on complementary metal-oxide-semiconductor (CMOS) integrated circuits (IC) technology to achieve bandwidths in excess of 500 kHz and microsecond temporal resolution. We use this CMOS-integrated bilayer system to study the type 1 ryanodine receptor (RyR1), a Ca2+-activated intracellular Ca2+-release channel located on the sarcoplasmic reticulum. We are able to distinguish multiple closed states not evident with lower bandwidth recordings, suggesting the presence of an additional Ca2+ binding site, distinct from the site responsible for activation. An extended beta distribution analysis of our high-bandwidth data can be used to infer closed state flicker events as fast as 35 ns. These events are in the range of single-file ion translocations.

Extended beta distributions open the access to fast gating in bilayer experiments-assigning the voltage-dependent gating to the selectivity filter.

Lipid bilayers provide many benefits for ion channel recordings, such as control of membrane composition and transport molecules. However, they suffer from high membrane capacitance limiting the bandwidth and impeding analysis of fast gating. This can be overcome by fitting the deviations of the open-channel noise from the baseline noise by extended beta distributions. We demonstrate this analysis step-by-step by applying it to the example of viral K+  channels (Kcv), from the choice of the gating model through the fitting process, validation of the results, and what kinds of results can be obtained. These voltage sensor-less channels show profoundly voltage-dependent gating with dwell times in the closed state of about 50 µs. Mutations assign it to the selectivity filter.