Fluids and Electrolytes under Confinement in Single-Digit Nanopores.

Confined fluids and electrolyte solutions in nanopores exhibit rich and surprising physics and chemistry that impact the mass transport and energy efficiency in many important natural systems and industrial applications. Existing theories often fail to predict the exotic effects observed in the narrowest of such pores, called single-digit nanopores (SDNs), which have diameters or conduit widths of less than 10 nm, and have only recently become accessible for experimental measurements. What SDNs reveal has been surprising, including a rapidly increasing number of examples such as extraordinarily fast water transport, distorted fluid-phase boundaries, strong ion-correlation and quantum effects, and dielectric anomalies that are not observed in larger pores. Exploiting these effects presents myriad opportunities in both basic and applied research that stand to impact a host of new technologies at the water-energy nexus, from new membranes for precise separations and water purification to new gas permeable materials for water electrolyzers and energy-storage devices. SDNs also present unique opportunities to achieve ultrasensitive and selective chemical sensing at the single-ion and single-molecule limit. In this review article, we summarize the progress on nanofluidics of SDNs, with a focus on the confinement effects that arise in these extremely narrow nanopores. The recent development of precision model systems, transformative experimental tools, and multiscale theories that have played enabling roles in advancing this frontier are reviewed. We also identify new knowledge gaps in our understanding of nanofluidic transport and provide an outlook for the future challenges and opportunities at this rapidly advancing frontier.

Nanobubble Formation and Coverage during High Current Density Alkaline Water Electrolysis.

Gas bubbles are a necessary byproduct of water electrolysis whereby hydrogen and oxygen are produced from water. These attached gases reduce the electrode's active area, which necessitates a deep understanding of the bubble life cycle starting from nanobubbles. Synchronized with the electrochemistry, the time evolution of the surface nanobubble size and coverage is resolved using grazing incidence small-angle X-ray scattering (GISAXS) and correlated with optical microscopy and theoretical calculations to show that a significant portion of the surface is covered in nanobubbles after larger micron-sized bubbles are observed. These nanobubbles increase in number and decrease in size, toward 2 nm diameter, with the charge passed. The trend in size and number is consistent with an increase in supersaturation, which reduces the nascent bubble size. Altogether, this study suggests a significant portion of the surface contains nanobubbles and that strategies to reduce the dissolved hydrogen would be effective at reducing the nanobubble surface coverage.

Rectified and Salt Concentration Dependent Wetting of Hydrophobic Nanopores.

Nanopores lined with hydrophobic groups function as switches for water and all dissolved species, such that transport is allowed only when applying a sufficiently high transmembrane pressure difference or voltage. Here we show a hydrophobic nanopore system whose wetting and ability to transport water and ions is rectified and can be controlled with salt concentration. The nanopore we study contains a junction between a hydrophobic zone and a positively charged hydrophilic zone. The nanopore is closed for transport at low salt concentrations and exhibits finite current only when the concentration reaches a threshold value that is dependent on the pore opening diameter, voltage polarity and magnitude, and type of electrolyte. The smallest nanopore studied here had a 4 nm diameter and did not open for transport in any concentration of KCl or KI examined. A 12 nm nanopore was closed for all KCl solutions but conducted current in KI at concentrations above 100 mM for negative voltages and opened for both voltage polarities at 500 mM KI. Nanopores with a hydrophobic/hydrophilic junction can thus function as diodes, such that one can identify a range of salt concentrations where the pores transport water and ions for only one voltage polarity. Molecular dynamics simulations together with continuum models provided a multiscale explanation of the observed phenomena and linked the salt concentration dependence of wetting with an electrowetting model. Results presented are crucial for designing next-generation chemical and ionic separation devices as well as understanding fundamental properties of hydrophobic interfaces under nanoconfinement.

Confinement effects on the solvation structure of solvated alkali [correction] metal cations in a single-digit 1T-MoS2 nanochannel: A first-principles study.

Confinement plays an important role in determining ion transport in porous materials, which, in turn, may influence the performance of many energy storage and desalination devices. In this work, we combined density functional theory (DFT) with an implicit solvation model and ab initio molecular dynamics (AIMD) to investigate the effects of nanoconfinement on several solvated alkaline metal cations in a single-digit 1T-MoS2 nanochannel. Our DFT calculations with a solvation model indicated that cations with stronger hydration energy introduce a higher number of co-intercalated water molecules into the channel, consistent with early experimental observation obtained for MXene (2D transition metal carbide) channels. The predicted optimal water numbers for the cations were then used for AIMD simulations that explicitly include the effects of the solvent. When compared with the cations in bulk solution, our simulations showed that the hydration structure and coordination number (CN) of the solvated cations confined in the MoS2 channel can be significantly altered. We found that larger cations with weaker hydration energy (K+, Rb+, and Cs+) exhibited a distinctive CN decrease under confinement, while smaller cations (Li+ and Na+) retained a similar hydration shell as in the bulk solution. More specifically, the hydration shell of large cations (K+, Rb+, and Cs+) in MoS2 showed similar features of the coordination angle to the bulk, which suggests the partially broken hydration shell with no geometry change under confinement. Our simulations provided insights into the change of the hydration structure of alkaline metal cations under confinement, which may have important implications on their transport in the 1T-MoS2 channel.

Improving the accuracy and convergence of drug permeation simulations via machine-learned collective variables.

Understanding the permeation of biomolecules through cellular membranes is critical for many biotechnological applications, including targeted drug delivery, pathogen detection, and the development of new antibiotics. To this end, computer simulations are routinely used to probe the underlying mechanisms of membrane permeation. Despite great progress and continued development, permeation simulations of realistic systems (e.g., more complex drug molecules or biologics through heterogeneous membranes) remain extremely challenging if not intractable. In this work, we combine molecular dynamics simulations with transition-tempered metadynamics and techniques from the variational approach to conformational dynamics to study the permeation mechanism of a drug molecule, trimethoprim, through a multicomponent membrane. We show that collective variables (CVs) obtained from an unsupervised machine learning algorithm called time-structure based Independent Component Analysis (tICA) improve performance and substantially accelerate convergence of permeation potential of mean force (PMF) calculations. The addition of cholesterol to the lipid bilayer is shown to increase both the width and height of the free energy barrier due to a condensing effect (lower area per lipid) and increase bilayer thickness. Additionally, the tICA CVs reveal a subtle effect of cholesterol increasing the resistance to permeation in the lipid head group region, which is not observed when canonical CVs are used. We conclude that the use of tICA CVs can enable more efficient PMF calculations with additional insight into the permeation mechanism.

Similarities and differences between potassium and ammonium ions in liquid water: a first-principles study.

Understanding ion solvation in liquid water is critical in optimizing materials for a wide variety of emerging technologies, including water desalination and purification. In this work, we report a systematic investigation and comparison of solvated K+ and NH4+ using first-principles molecular dynamics simulations. Our simulations reveal a strong analogy in the solvation properties of the two ions, including the size of the solvation shell as well as the solvation strength. On the other hand, we find that the local water structure in the ion solvation is significantly different; specifically, NH4+ yields a smaller number of water molecules and a more ordered water structure in the first solvation shell due to the formation of hydrogen bonds between the ion and water molecules. Finally, our simulations indicate that a comparable solvation strength of the two ions is a result of an interplay between the nature of ion-water interaction and number of water molecules that can be accommodated in the ion solvation shell.

Mycolactone Toxin Membrane Permeation: Atomistic versus Coarse-Grained MARTINI Simulations.

Mycolactone, a cytotoxic and immunosuppressive macrolide produced by Mycobacterium ulcerans, is the central virulent factor in the skin disease Buruli ulcer. This multifunctional cytotoxin affects fundamental cellular processes such as cell adhesion, immune response, and cell death by targeting various cellular structures. Developing effective diagnostics that target mycolactone has been challenging, potentially because of suspected interactions with lipophilic architectures, including membranes. To better understand the pathogenesis of Buruli ulcer disease, aid in the development of diagnostics, and learn how amphiphiles in general use lipid trafficking to navigate the host environment, we seek to understand the nature of mycolactone-membrane interactions. Herein, we characterize how the two dominant isomers of mycolactone (A and B) interact with and permeate DPPC membranes with all-atom molecular dynamics simulations employing transition-tempered metadynamics and compare these results to those obtained by MARTINI coarse-grained simulations. Our all-atom simulations reveal that both isomers have a strong preference to associate with the membrane, although their mechanisms and energetics of membrane permeation differ slightly. Water molecules are found to play an important role in the permeation process. Although the MARTINI coarse-grained simulations give the correct free energy of membrane association, they fail to capture the mechanism of permeation and role of water during permeation as seen in all-atom simulations.

Implicit solvent coarse-grained model of polyamidoamine dendrimers: Role of generation and pH.

Highly branched polymers such as polyamidoamine (PAMAM) dendrimers are promising macromolecules in the realm of nanobiotechnology due to their high surface coverage of tunable functional groups. Modeling efforts of PAMAM can provide structural and morphological properties, but the inclusion of solvents and the exponential growth of atoms with generations make atomistic simulations computationally expensive. We apply an implicit solvent coarse-grained model, called the Dry Martini force field, to PAMAM dendrimers. The reduced number of particles and the absence of a solvent allow the capture of longer spatiotemporal scales. This study characterizes PAMAM dendrimers of generations one through seven in acidic, neutral, and basic pH environments. Comparison with existing literature, both experimental and theoretical, is done using measurements of the radius of gyration, moment of inertia, radial distributions, and scaling exponents. Additionally, ion coordination distributions are studied to provide insight into the effects of interior and exterior protonation on counter ions. This model serves as a starting point for future designs of larger functionalized dendrimers.

Phase segregation in bio-inspired multi-component vesicles encompassing double tail phospholipid species.

Our aim is to investigate the phase segregation and the structure of multi-component bio-inspired phospholipid vesicles via dissipative particle dynamics. The chemical distinction in the phospholipid species arises due to different head and tail group moieties, and molecular stiffness of the hydrocarbon tails. The individual amphiphilic phospholipid molecular species are represented by a hydrophilic head group and two hydrophobic tails. The distinct chemical nature of the moieties is modeled effectively via soft repulsive interaction parameters, and the molecular rigidity is tuned via suitable three-body potential constants. We demonstrate the formation of a stable hybrid vesicle through the self-assembly of the amphiphilic phospholipid molecules in the presence of a hydrophilic solvent. We investigate and characterize the phase segregation and the structure of the binary vesicles for different phospholipid mixtures. Our results demonstrate macroscopic phase separation for phospholipid mixtures composed of species with different hydrocarbon tail groups. We also investigate the relationship between the phase segregation and thermodynamic variables such as interfacial line tension and surface tension, and obtain correspondence between existing theory and experiments, and our simulation results. We report variations in the molecular chain stiffness to have negligible contributions to the phase segregation in the mixed bilayer, and to demonstrate shape transformations of the hybrid vesicle. Our results can be used to design novel bio-inspired hybrid vehicles for potential applications in biomedicine, sensing, imaging and sustainability.

Prediction of the essential intermolecular contacts for side-binding of VASP on F-actin.

Vasodilator-stimulated phosphoprotein (VASP) family proteins play a crucial role in mediating the actin network architecture in the cytoskeleton. The Ena/VASP homology 2 (EVH2) domain in each of the four identical arms of the tetrameric VASP consists of a loading poly-Pro region, a G-actin-binding domain (GAB), and an F-actin-binding domain (FAB). Together, the poly-Pro, GAB, and FAB domains allow VASP to bind to sides of actin filaments in a bundle, and recruit profilin-G-actin to processively elongate the filaments. The atomic resolution structure of the ternary complex, consisting of the loading poly-Pro region and GAB domain of VASP with profilin-actin, has been solved over a decade ago; however, a detailed structure of the FAB-F-actin complex has not been resolved to date. Experimental insights, based on homology of the FAB domain with the C region of WASP, have been used to hypothesize that the FAB domain binds to the cleft between subdomains 1 and 3 of F-actin. Here, in order to develop our understanding of the VASP-actin complex, we first augment known structural information about the GAB domain binding to actin with the missing FAB domain-actin structure, which we predict using homology modeling and docking simulations. In earlier work, we used mutagenesis and kinetic modeling to study the role of domain-level binding-unbinding kinetics of Ena/VASP on actin filaments in a bundle, specifically on the side of actin filaments. We further look at the nature of the side-binding of the FAB domain of VASP at the atomistic level using our predicted structure, and tabulate effective mutation sites on the FAB domain that would disrupt the VASP-actin complex. We test the binding affinity of Ena with mutated FAB domain using total internal reflection fluorescence microscopy experiments. The binding affinity of VASP is affected significantly for the mutant, providing additional support for our predicted structure.

Membrane lipids drive formation of KRAS4b-RAF1 RBDCRD nanoclusters on the membrane.

The oncogene RAS, extensively studied for decades, presents persistent gaps in understanding, hindering the development of effective therapeutic strategies due to a lack of precise details on how RAS initiates MAPK signaling with RAF effector proteins at the plasma membrane. Recent advances in X-ray crystallography, cryo-EM, and super-resolution fluorescence microscopy offer structural and spatial insights, yet the molecular mechanisms involving protein-protein and protein-lipid interactions in RAS-mediated signaling require further characterization. This study utilizes single-molecule experimental techniques, nuclear magnetic resonance spectroscopy, and the computational Machine-Learned Modeling Infrastructure (MuMMI) to examine KRAS4b and RAF1 on a biologically relevant lipid bilayer. MuMMI captures long-timescale events while preserving detailed atomic descriptions, providing testable models for experimental validation. Both in vitro and computational studies reveal that RBDCRD binding alters KRAS lateral diffusion on the lipid bilayer, increasing cluster size and decreasing diffusion. RAS and membrane binding cause hydrophobic residues in the CRD region to penetrate the bilayer, stabilizing complexes through ß-strand elongation. These cooperative interactions among lipids, KRAS4b, and RAF1 are proposed as essential for forming nanoclusters, potentially a critical step in MAP kinase signal activation.

Generating Protein Structures for Pathway Discovery Using Deep Learning.

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be approximated, but the mechanistic details in between are unknown. Here, we propose a representation-learning-based solution that uses interpolation and extrapolation in an abstract representation space to synthesize potential transition states, which are automatically validated using MD simulations. The new simulations of the synthesized transition states are subsequently incorporated into the representation learning, leading to an iterative framework for targeted path sampling. Our approach is demonstrated by recovering the transition of a RAS-RAF protein domain (CRD) from membrane-free to interacting with the membrane using coarse-grain MD simulations.

The confluence of machine learning and multiscale simulations.

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. This confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

Mechanistic Insights on Permeation of Water over Iron Cations in Nanoporous Silicon Oxide Films for Selective H2 and O2 Evolution.

Electrocatalysts encapsulated by an ultrathin and semipermeable oxide layer offer a promising avenue for efficient, selective, and cost-effective production of hydrogen through photoelectrochemical water splitting. This architecture is especially attractive for Z-scheme water splitting, for which a nanoporous oxide film can be leveraged to mitigate undesired, yet kinetically facile, reactions involving redox shuttles, such as aqueous iron cations, by limiting transport of these species to catalytically active sites. In this work, molecular dynamics simulations were combined with electrochemical measurements to provide a mechanistic understanding of permeation of water and Fe(III)/Fe(II) redox shuttles through nanoporous SiO2 films. It is shown that even for SiO2 pores with a width as small as 0.8 nm, water does not experience any energy barrier for permeating into the pores due to a favorable interaction with hydrophilic silanol groups on the oxide surface. In contrast, permeation of Fe(III) and Fe(II) into microporous SiO2 pores is limited due to high energy barriers, which stem from a combination of distortion and dehydration of the second and third ion solvation shells. Our simulations and experimental results show that SiO2 coatings can effectively mitigate undesired Fe(III)/Fe(II) redox reactions at underlying electrodes by attenuating permeation of iron cations, while allowing water to permeate and thus participate in water splitting reactions. In a broader context, our study demonstrates that selectivity of solvated cations can be manipulated by controlling the pore size and surface chemistry of oxide films.

Machine Learning-Driven Multiscale Modeling: Bridging the Scales with a Next-Generation Simulation Infrastructure.

Interdependence across time and length scales is common in biology, where atomic interactions can impact larger-scale phenomenon. Such dependence is especially true for a well-known cancer signaling pathway, where the membrane-bound RAS protein binds an effector protein called RAF. To capture the driving forces that bring RAS and RAF (represented as two domains, RBD and CRD) together on the plasma membrane, simulations with the ability to calculate atomic detail while having long time and large length- scales are needed. The Multiscale Machine-Learned Modeling Infrastructure (MuMMI) is able to resolve RAS/RAF protein-membrane interactions that identify specific lipid-protein fingerprints that enhance protein orientations viable for effector binding. MuMMI is a fully automated, ensemble-based multiscale approach connecting three resolution scales: (1) the coarsest scale is a continuum model able to simulate milliseconds of time for a 1 µm2 membrane, (2) the middle scale is a coarse-grained (CG) Martini bead model to explore protein-lipid interactions, and (3) the finest scale is an all-atom (AA) model capturing specific interactions between lipids and proteins. MuMMI dynamically couples adjacent scales in a pairwise manner using machine learning (ML). The dynamic coupling allows for better sampling of the refined scale from the adjacent coarse scale (forward) and on-the-fly feedback to improve the fidelity of the coarser scale from the adjacent refined scale (backward). MuMMI operates efficiently at any scale, from a few compute nodes to the largest supercomputers in the world, and is generalizable to simulate different systems. As computing resources continue to increase and multiscale methods continue to advance, fully automated multiscale simulations (like MuMMI) will be commonly used to address complex science questions.

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework.

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. Here, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (∼98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ∼1.5 million atoms is discussed in the context of MuMMI.

Ion Solvation and Transport in Narrow Carbon Nanotubes: Effects of Polarizability, Cation-π Interaction, and Confinement.

Understanding ion solvation and transport under confinement is critical for a wide range of emerging technologies, including water desalination and energy storage. While molecular dynamics (MD) simulations have been widely used to study the behavior of confined ions, considerable deviations between simulation results depending on the specific treatment of intermolecular interactions remain. In the following, we present a systematic investigation of the structure and dynamics of two representative solutions, that is, KCl and LiCl, confined in narrow carbon nanotubes (CNTs) with a diameter of 1.1 and 1.5 nm, using a combination of first-principles and classical MD simulations. Our simulations show that the inclusion of both polarization and cation-π interactions is essential for the description of ion solvation under confinement, particularly for large ions with weak hydration energies. Beyond the variation in ion solvation, we find that cation-π interactions can significantly influence the transport properties of ions in CNTs, particularly for KCl, where our simulations point to a strong correlation between ion dehydration and diffusion. Our study highlights the complex interplay between nanoconfinement and specific intermolecular interactions that strongly control the solvation and transport properties of ions.

Unraveling the Ion Adsorption Kinetics in Microporous Carbon Electrodes: A Multiscale Quantum-Continuum Simulation and Experimental Approach.

Understanding sorption in porous carbon electrodes is crucial to many environmental and energy technologies, such as capacitive deionization (CDI), supercapacitor energy storage, and activated carbon filters. In each of these examples, a practical model that can describe ion electrosorption kinetics is highly desirable for accelerating material design. Here, we proposed a multiscale model to study the ion electrosorption kinetics in porous carbon electrodes by combining quantum mechanical simulations with continuum approaches. Our model integrates the Butler-Volmer (BV) equation for sorption kinetics and a continuously stirred tank reactor (CSTR) formulation with atomistic calculations of ion hydration and ion-pore interactions based on density functional theory (DFT). We validated our model experimentally by using ion mixtures in a flow-through electrode CDI device and developed an in-line UV absorption system to provide unprecedented resolution of individual ions in the separation process. We showed that the multiscale model captures unexpected experimental phenomena that cannot be explained by the traditional ion electrosorption theory. The proposed multiscale framework provides a viable approach for modeling separation processes in systems where pore sizes and ion hydration effects strongly influence the sorption kinetics, which can be leveraged to explore possible strategies for improving carbon-based and, more broadly, pore-based technologies.

Fast Permeation of Small Ions in Carbon Nanotubes.

Simulations and experiments have revealed enormous transport rates through carbon nanotube (CNT) channels when a pressure gradient drives fluid flow, but comparatively little attention has been given to concentration-driven transport despite its importance in many fields. Here, membranes are fabricated with a known number of single-walled CNTs as fluid transport pathways to precisely quantify the diffusive flow through CNTs. Contrary to early experimental studies that assumed bulk or hindered diffusion, measurements in this work indicate that the permeability of small ions through single-walled CNT channels is more than an order of magnitude higher than through the bulk. This flow enhancement scales with the ion free energy of transfer from bulk solutions to a nanoconfined, lower-dielectric environment. Reported results suggest that CNT membranes can unlock dialysis processes with unprecedented efficiency.