PACKMOL: a package for building initial configurations for molecular dynamics simulations

To Hop or Not to Hop: Unveiling Different Modes of Ion Transport in Solid Polymer Electrolytes through Molecular Dynamics Simulations

Although the basic modes of ion transport in solid polymer electrolytes (SPEs) are already classified and well-described, their distribution in typical polymer electrolytes is not clear and neither are the effects on the distribution by different degrees of ion-ion and ion-polymer interactions. Here, the ion-transport mechanisms in poly(ethylene oxide) are studied along with poly(ε-caprolactone) at different molecular weights and LiTFSI salt concentrations using molecular dynamics simulations. Through tracking of the cation coordination changes, three transport mechanisms are categorized, i.e., ion hopping, continuous motion (successive exchange of the coordination sphere), and vehicular transport. The observed dominant transport mechanism is in all cases continuous motion, which changes from polymer-mediated to anion-mediated with increasing salt concentration, while polymer-mediated vehicular transport is not observed to be a major contributor to cation transport. In both systems, ion hopping is also essentially absent, as can be expected in systems with strong ion-polymer interactions. The results illustrate how the usual description of ion transport in polymer electrolytes as coupled to segmental motions is too simplistic to catch the full essence of the ion-transport phenomena, whereas the frequently used notion of "ion hopping" in the majority of cases is incorrect for SPEs.

Rapid Access to Small Molecule Conformational Ensembles in Organic Solvents Enabled by Graph Neural Network-Based Implicit Solvent Model

Understanding and manipulating the conformational behavior of a molecule in different solvent environments is of great interest in the fields of drug discovery and organic synthesis. Molecular dynamics (MD) simulations with solvent molecules explicitly present are the gold standard to compute such conformational ensembles (within the accuracy of the underlying force field), complementing experimental findings and supporting their interpretation. However, conventional methods often face challenges related to computational cost (explicit solvent) or accuracy (implicit solvent). Here, we showcase how our graph neural network (GNN)-based implicit solvent (GNNIS) approach can be used to rapidly compute small molecule conformational ensembles in 39 common organic solvents reproducing explicit-solvent simulations with high accuracy. We validate this approach using nuclear magnetic resonance (NMR) measurements, thus identifying the conformers contributing most to the experimental observable. The method allows the time required to accurately predict conformational ensembles to be reduced from days to minutes while achieving results within one kBT of the experimental values.

3D Printed Flexible Zinc-Ion Battery for Real-Time Health Monitoring Devices

The growing need for multifunctional wearable electronics for mobile applications has triggered the demand for flexible and reliable energy storage devices. 3D printing technology has emerged as a promising and attractive method for manufacturing these devices. This study presents the design and fabrication of a flexible quasi-solid-state Zn-ion battery using the direct-writing 3D printing technique. A conductive silver paste with high conductivity was printed onto a PET substrate to serve as the current collector. The cathode was fabricated from carbon-coated MnO2 nanorods produced using hydrothermal methods, while the anode consisted of commercial zinc powder. The cathode and anode slurries exhibiting excellent viscoelasticity were 3D printed on the current collector. To complete the flexible quasi-solid-state zinc-ion battery, a PVA gel electrolyte was printed onto the PET substrate. This battery delivered an initial capacity of 267.3 mAh g-1 and maintained a capacity of 189.7 mAh g-1 after 500 cycles at a current density of 0.2 A g-1. Furthermore, the 3D printed battery successfully powered a portable human heart rate sensor, showcasing the potential of 3D printing technology as an environmentally friendly, cost-effective, and scalable solution for wearable energy storage devices.

Empowering materials science with VASPKIT: a toolkit for enhanced simulation and analysis

Driven by rapid advances in high-performance supercomputing, computational materials science has emerged as a powerful approach for exploring, designing, and predicting material properties at the atomic and molecular scales. Among the various computational tools developed in this field, the Vienna Ab initio Simulation Package (VASP) stands out as a widely adopted and highly versatile platform for performing first-principles density functional theory (DFT) calculations. VASP is widely used to explore electronic structures, phonon behavior, magnetic properties, thermodynamics and catalytic mechanisms across a diverse range of materials systems. Despite its robust capabilities, utilizing VASP requires expertise in setting up simulations and analyzing results, which can be time consuming and technically challenging. To address these barriers, VASPKIT was developed as a comprehensive toolkit to simplify the workflow for VASP users. VASPKIT streamlines both preprocessing and postprocessing tasks, enabling users to generate essential input files based on customizable parameters and automate computational workflows. The postprocessing features of VASPKIT allow for efficient analysis of electronic, mechanical, optical and catalytic properties, thereby substantially reducing the need for advanced programming expertise. This protocol provides a detailed guide to using VASPKIT, including practical examples to demonstrate its versatility and utility in conducting and analyzing DFT calculations. For instance, the computation of elastic constants, electronic band structures and density of states for a graphene system can typically be completed within half an hour, depending on the computational resources available. By offering step-by-step guidance, this protocol aims to further expand the accessibility and impact of VASPKIT in the field of computational materials science.

Mechanistic basis for enhanced strigolactone sensitivity in KAI2 triple mutant

Striga hermonthica is a parasitic weed that destroys billions of dollars' worth of staple crops every year. Its rapid proliferation stems from an enhanced ability to metabolize strigolactones (SLs), plant hormones that direct root branching and shoot growth. Striga's SL receptor, ShHTL7, bears more similarity to the staple crop karrikin receptor karrikin insensitive 2 (KAI2) than to SL receptor D14, though KAI2 variants in plants like Arabidopsis thaliana show minimal SL sensitivity. Recently, studies have indicated that a small number of point mutations to HTL7 residues can confer SL sensitivity to AtKAI2. Here, we analyze both wild-type AtKAI2 and SL-sensitive mutant Var64 through all-atom, long-timescale molecular dynamics simulations to determine the effects of these mutations on receptor function at a molecular level. We demonstrate that the mutations stabilize SL binding by about 2 kcal/mol. They also result in a doubling of the average pocket volume and eliminate the dependence of binding on certain pocket conformational arrangements. Although the probability of certain nonbinding SL-receptor interactions increases in the mutant compared with the wild-type, the rate of binding also increases by a factor of 10. All these changes account for the increased SL sensitivity in mutant KAI2 and suggest mechanisms for increasing the functionality of host crop SL receptors.

Amino Acid Sequence Controls Enhanced Electron Transport in Heme-Binding Peptide Monolayers

Metal-binding proteins have the exceptional ability to facilitate long-range electron transport in nature. Despite recent progress, the sequence-structure-function relationships governing electron transport in heme-binding peptides and protein assemblies are not yet fully understood. In this work, the electronic properties of a series of heme-binding peptides inspired by cytochrome bc 1 are studied using a combination of molecular electronics experiments, molecular modeling, and simulation. Self-assembled monolayers (SAMs) are prepared using sequence-defined heme-binding peptides capable of forming helical secondary structures. Following monolayer formation, the structural properties and chemical composition of assembled peptides are determined using atomic force microscopy and X-ray photoelectron spectroscopy, and the electronic properties (current density-voltage response) are characterized using a soft contact liquid metal electrode method based on eutectic gallium-indium alloys (EGaIn). Our results show a substantial 1000-fold increase in current density across SAM junctions upon addition of heme compared to identical peptide sequences in the absence of heme, while maintaining a constant junction thickness. These findings show that amino acid composition and sequence directly control enhancements in electron transport in heme-binding peptides. Overall, this study demonstrates the potential of using sequence-defined synthetic peptides inspired by nature as functional bioelectronic materials.

Generator of Neural Network Potential for Molecular Dynamics: Constructing Robust and Accurate Potentials with Active Learning for Nanosecond-Scale Simulations

Neural network potentials (NNPs) enable large-scale molecular dynamics (MD) simulations of systems containing >10,000 atoms with the accuracy comparable to ab initio methods and play a crucial role in material studies. Although NNPs are valuable for short-duration MD simulations, maintaining the stability of long-duration MD simulations remains challenging due to the uncharted regions of the potential energy surface (PES). Currently, there is no effective methodology to address this issue. To overcome this challenge, we developed an automatic generator of robust and accurate NNPs based on an active learning (AL) framework. This generator provides a fully integrated solution encompassing initial data set creation, NNP training, evaluation, sampling of additional structures, screening, and labeling. Crucially, our approach uses a sampling strategy that focuses on generating unstable structures with short interatomic distances, combined with a screening strategy that efficiently samples these configurations based on interatomic distances and structural features. This approach greatly enhances the MD simulation stability, enabling nanosecond-scale simulations. We evaluated the performance of our NNP generator in terms of its MD simulation stability and physical properties by applying it to liquid propylene glycol (PG) and polyethylene glycol (PEG). The generated NNPs enable stable MD simulations of systems with >10,000 atoms for 20 ns. The predicted physical properties, such as the density and self-diffusion coefficient, show excellent agreement with the experimental values. This work represents a remarkable advance in the generation of robust and accurate NNPs for organic materials, paving the way for long-duration MD simulations of complex systems.

Self-Associated Engineering in P123 Micelles Rationalizing the Role of Other Pluronics with Varying Hydrophilicity as a Mixed System

This study explores the atomic-level interactions of different poly(ethylene oxide) (EO)-poly(propylene oxide) (PO)-based block copolymers (BCPs), commercially known as Pluronics, with varying hydrophilicity that influences the solution behavior within Pluronic P123 micelles as a mixed system. The critical insights into the thermoresponsiveness of P123 in the presence of different Pluronics with increasing %EO content (L61, L62, L64, and F68) is hypothesized to modulate the hydrophobic interactions, leading to distinct solution textures such as clear solution (sol), blue point (BP), and cloud point (CP). The solution relative viscosity (ηrel) and rheological analysis will depict the dynamic flow behavior and expose the viscoelastic properties of the blended system. The dynamic light scattering (DLS) analysis will exhibit a temperature-dependent variation in the hydrodynamic diameter (Dh) micelle size in the examined system as a function of temperature, depicting micellar growth, while small-angle neutron scattering (SANS) will explore the intricate micellar structural dynamics in terms of size and shape using various mathematical models. Complementing these findings, transmission electron microscopy (TEM) will offer direct visualization of these micellar structures, confirming the morphological growth/transitions. Coarse-grained molecular dynamics (CG-MD) simulations will elucidate this self-assembly at the molecular scale with micelle size distributions, computed scattering intensity, density profiles, solvent-accessible surface area (SASA), diffusion coefficient (D), and mean squared displacement (MSD) profiles at elevated temperatures to uncover molecular packing and stability.

Comprehensive Analysis of the Structural Evolution and Dynamic Mechanical Behavior of Ferrofluids through Coarse-Grained Molecular Dynamics and Experimental Testing

Understanding and predicting the relationship between the dynamic structure and dynamic mechanical behavior of ferrofluids at the mesoscale presents a significant challenge. Therefore, in this study, a ferrofluid model that considered the molecular structure of the carrier liquid was constructed to perform coarse-grained molecular dynamics simulations of the mesoscale structural evolution and internal particle arrangement characteristics of ferrofluids under the influence of a magnetic field. Subsequently, oscillatory shear deformation was applied to further investigate the mechanical properties of ferrofluids under dynamic strain, as well as the deformation and disruption of their orientational structures. The simulations show that the columnar aggregation of magnetic particles imparts typical viscoelastic characteristics to the ferrofluid, and these aggregated structures gradually deform and break down as the strain amplitude increases. During dynamic oscillatory shear deformation, the enhancement of the magnetic field allows the aggregated structure of magnetic particles to exhibit better resistance to deformation, thereby improving the absorption and dissipation of mechanical energy. The dynamic mechanical properties of ferrofluids obtained from coarse-grained simulations closely align with the experimental results under moderate to high magnetic fields, allowing for a certain degree of predictive capability regarding the dynamic mechanical behavior of ferrofluids.

Regional Characteristics in Ultradeep MXene Slit Nanopores: Insights from Molecular Dynamics Simulation

The presence of slit nanopores in MXene materials inevitably influences the electrochemical performance of supercapacitor electrodes. However, most studies focus on experimental approaches, lacking microscopic-scale analysis. Here, we performed molecular dynamics (MD) simulations to thoroughly analyze and predict the ion transport pathways and energy storage mechanisms of MXene/ionic liquid (IL) supercapacitors with ultradeep slit nanopores. The simulation results indicate that during the charging process, counterions migrate from the bulk region into the electrode pores, forming a counterion layer near the electrode surface, while some co-ions gradually exit the pores. When charging is completed, a distinct ion layering structure emerges. As the interlayer spacing varies, the ion distribution in the electrode pores exhibits regional characteristics: in the ordered region near the bulk region, a stable electrical double-layer (EDL) structure is maintained, whereas in the deeper mixed region, persistent co-ion presence and significant disorder are observed. Dominated by the mixed region, the total energy variation of the electrode decreases as the interlayer spacing decreases, with energy changes of 8526.52, 7443.52, and 6640.99 kJ·mol-1 at interlayer spacings of 1.2, 1.0, and 0.8 nm, respectively, representing reductions of approximately 12.7 and 22.1% compared to 1.2 nm. In the mixed region, after compensating for the interaction between counterions, the contribution of the interaction between counterions and electrode increases with decreasing interlayer spacing, reaching 123, 153, and 176% at 1.2, 1.0, and 0.8 nm, respectively. An increasing amount of energy is offset by interactions related to the co-ions, ultimately leading to the observed energy differences. These findings offer new insights into the impact of nanopore structure on supercapacitor performance and provide theoretical guidance for optimizing electrode design.

A combined soft X-ray and theoretical investigation discloses the water harvesting behaviour of Mg-MOF-74 at the crystal surface

Metal-organic frameworks (MOFs) are receiving growing interest as transformative materials for real-world atmospheric water harvesting applications. However, obtaining molecular-level details on how surface effects regulate MOF water uptake has proven to be elusive. Here, we present a novel methodology based on ambient pressure soft X-ray absorption spectroscopy (AP-NEXAFS), machine learning-assisted theoretical spectroscopy and molecular dynamics simulations to gain selective insights into the behaviour of water at a MOF crystal surface. We applied our interdisciplinary method to investigate the structural and dynamical properties of water at the surface of the Mg-MOF-74 system, while obtaining complementary information on the water uptake and release from the bulk by synchrotron powder X-ray diffraction. Our investigation pointed out the simultaneous presence of Mg open sites and residual gas-phase water during dehydration, and proved that during water release a high number of surface Mg sites still interact with one or two water molecules. Conversely, when looking at the bulk, a significantly lower number of Mg sites have been found to interact with water molecules in the same experimental conditions. This behaviour suggests that the water adsorption (desorption) process starts from the interior of the material and propagates towards the channel openings. The combined approach based on AP-NEXAFS, PXRD experimental determinations and ML-supported theoretical analyses has been found to be a valuable tool to provide a thorough description of the water harvesting process at both surface and bulk of the crystal.

Li+-migration influencing factors and non-destructive life extension of quasi-solid-state polymer electrolytes

Polymer-based quasi-solid-state electrolytes (QSSE) are believed to be the most feasible candidates for solid-state batteries, but they are hindered by relatively lower ionic conductivity and narrower electrochemical window. Here, we synthesize a series of ether-free acrylates containing Li+-ligands for high-voltage-stable QSSEs. Our findings demonstrate that the polymer-involved solvation structure is critical in determining the ionic conductivity, and low-temperature crystallization of the polymer can be used for non-destructive life extension of batteries. The prepared polymers do not contain ether unit and exhibit a polymerization degree of 99% in cells without residual double-bonded monomer, endowing them with high antioxidation capability and compatibility with high-voltage positive electrodes including LiNi0.85Co0.075Mn0.075O2, 4.6 V LiCoO2 and 4.8 V Li1.13Ni0.3Mn0.57O2. The confinement of liquid in QSSEs effectively suppresses the interfacial reactions, but the residual interface reactions still gradually consume liquid electrolytes and cause capacity fading, due to the limited diffusion of the confined solvent to wet the interface. Through crystallizing the polymer matrices at -50 °C, the confined liquid in QSSEs is released and re-wets the Li-metal/polymer interface, thereby recovering the capacity and extending the life of solid-state batteries in a non-destructive manner.

Effectively enhancing ion diffusion in superconcentrated ionic liquid electrolytes using co-solvent additives

The incorporation of high salt concentrations in ionic liquid (IL) electrolytes, forming superconcentrated ionic liquids, has been shown to improve Li-ion transference numbers and enhance cycling stability against lithium metal anodes. However, this benefit comes at the cost of significantly increased viscosity and reduced ionic conductivity due to the formation of large ion aggregates. To optimize conductivity further, a co-solvent can be introduced at an optimal concentration to enhance ion transport while preserving superior interfacial stability. The effectiveness of this approach depends on the solvent as it affects ion diffusion to varying degrees. This computational study examines how co-solvents can effectively enhance metal ion diffusion in superconcentrated ionic liquids by comparing two widely used organic solvents. We found that the key lies in their ability to effectively participate in Li solvation shells, disrupting the large Li-anion aggregates. Our results show that anion exchange in a Li(anion)x(solvent)y hybrid solvation shell occurs more rapidly than in a Li (anion)z solvation shell, facilitating Li diffusion through a structural diffusion mechanism. A co-solvent with a high donor number exhibits a stronger affinity for lithium ions, which is identified as a crucial factor in enhancing ion diffusion. This work provides valuable insights to guide the design of superconcentrated ionic liquid electrolytes for lithium-metal battery development.

Neural Network-Based Molecular Dynamics Simulation of Water Assisted by Active Learning

In our study, we combined classical molecular dynamics (MD) simulations with the simulated annealing (SA) method to explore the conformational landscape of water molecules. By using the K-means clustering method, we processed the MD simulation data to extract representative samples of water molecular structures used to train a deep potential (DP) model. Our DeePMD method showed accuracy in predicting water structural properties compared to DFT-MD results. Meanwhile, this approach achieves a balanced prediction of water density and self-diffusion coefficients compared with earlier DeePMD simulations. These results highlight the essential role of representative sampling techniques in training the DP model. Furthermore, we demonstrated the effectiveness of combining the DeePMD simulation with the centroid molecular dynamics (CMD) approach, which incorporates nuclear quantum effects (NQEs). This approach successfully reproduced the shoulder feature at 3250 cm-1 in the Raman spectra for the O-H stretch. Incorporating the path integral method into the DeePMD simulations underscores the importance of considering NQEs in understanding water molecules' structural and dynamic behaviors.

Understanding the Aggregation of Lanthanum(III) Nitrate Clusters in Pure Methanol: A Molecular Dynamics Investigation

A detailed analysis of the structure and speciation of La3+ clusters in the 0.1 mol L-1 La(NO3)3 salt methanol (MeOH) solution has been performed by means of molecular dynamics (MD) simulations. The time distribution and NO3-/MeOH ligand composition of these clusters have been computed using graph theory techniques. These analyses revealed the formation of branched-like polynuclear clusters in the solution, the predominant clusters being the 3, 7, and 8 La3+ clusters. In these clusters, the La3+ cations are bound by "monodentate" nitrate bridges. Moreover, the mechanism of aggregation of the La3+ clusters has been examined with the development of a 3-step model. Finally, the origin of the aggregation process has been identified by estimating the binding constant for the ion pair La3+-NO3- using the Bjerrum theory of dilute solutions, with pK° = 5.32 at 25 °C. The low value of the dielectric constant of methanol promotes the binding of the ion pair La3+-NO3- and the nitrato-bridging polymerization, resulting in the formation of clusters.

A Nanotherapeutic Agent for Synergistic Tumor Therapy: Co-Activation of Photochemical-Biological Effects

Single-mode photodynamic therapy (PDT) based on photochemical reactions is limited by the tumor microenvironment, which reduces the ablation efficiency for solid tumors. Making it vital to seek ways to improve the tumor therapeutic effect. Based on this, we propose a dual-mode intelligent nanotherapeutic system (HAP@BMPns) based on photochemical-biological effects. HAP@BMPns is composed of an acid-responsive high-calcium biomimetic nanomaterial (HAP) and photosensitizer (BMP), which can spontaneously activate photochemical (Type-I PDT) and biological effects for synergistic cancer therapy. HAP@BMPns breaks down upon entering tumor cells under acidic conditions, releasing a large amount of Ca2+ and BMP. Triggering intracellular Ca2+ overload, which induces mitochondrial damage, leading to apoptosis. Synchronously, Type-I PDT of BMP under two-photon (800 nm) laser irradiation becomes activated, resulting in enhanced destruction of tumor cells by the photochemical effect. Cell studies have indicated that HAP@BMPns (41.6 μg/mL) exhibits a strong inhibitory efficiency on tumor cells growth, with low (22.4 %) survival rate. However, the individual components, i. e. BMP (5.0 μM) and HAP (41.6 μg/mL) display low inhibitory efficiency with high survival rates (55.9 % and 63.0 % respectively). Therefore, this dual-mode synergistic treatment strategy using acid-triggered photochemical-biological effects significantly enhances the ablation of solid tumors, realizing the synergistic effect. We hope that this design strategy can provide guidance for the design and development of a tumor therapeutic platform.

Effect of Gold Substrate on the Interface between Graphene Monolayer and an Ionic Liquid

The unique properties of graphene make it an ideal material for electrochemical studies, particularly of the electrochemical double-layer. However, experimental studies generally require depositing graphene on substrates like gold, that may affect the electronic structure of the electrode and thus the ions adsorption properties. This study explores the impact of gold substrates on graphene electrochemical behavior using molecular dynamics. Two systems were compared: graphene on gold (Gr@Au) and standalone graphene (Gr), with ionic liquid ([EMIM][TFSI]) as the electrolyte. The model accounts for the different metallic behavior of graphene and gold under the various applied potentials. Despite a similar electrolyte structure, the interfacial capacitance is affected, which can be attributed to different charge distributions within the electrode. The variations of the van der Waals and Coulomb energies also show some differences in the presence of gold, in particular for low potentials.

On the nature of ion aggregation in EC-LiTFSI electrolytes

We investigate the structural and dynamic properties of concentrated ethylene carbonate (EC)-LiTFSI (lithium bis(trifluoromethanesulfonyl)imide) electrolytes using molecular dynamics (MD) simulations to elucidate the molecular mechanisms governing ion aggregation and transport. Increasing salt concentration induces a transition in the local solvation environment, marked by reduced radial distribution functions for ion-ion and ion-solvent interactions. This shift reflects the formation of ion pairs and larger ionic clusters, altering electrostatic interactions and weakening Li+-EC solvation. Ion aggregation probability, P(n), which quantifies the probability of n anions aggregating around a cation, peaks at n = 0 for dilute salt concentrations, n = 1 for intermediate salt concentrations, and n = 2 or n = 3 for high salt concentrations. These structural changes significantly impact dynamics, as ion aggregation slows ion mobility and reduces diffusion coefficients for Li+ and TFSI- ions. We observe strong correlations between ion diffusion, ion-pair relaxation times, and viscosity signifying the interplay between ion pairing, cluster formation, and mobility. This study provides molecular-level insights into how salt concentration influences ionic transport, advancing the theoretical framework for transport in dense liquid systems and guiding the design of advanced electrolytes.

Comprehensive investigation of the interactions between natural rubber and lignin by molecular dynamics simulation

Currently, the interaction behavior of natural rubber-lignin (NR-L) remains unclear. In this study, we successfully replicated the density, glass transition temperature, and mechanical properties of NR-L using molecular dynamics coupled with GAFF. Using these models, we rigorously investigated the interaction behavior of these systems, which revealed that when the ambient temperature was lower than the thermal decomposition temperature, there was almost no impact on system properties, including density, structure, interaction forces, radial pair distribution function, free volume, radius of gyration, and lignin hydrogen bonding, as no significant enthalpy change occurred. The influence of hardwood lignin and softwood lignin on NR strength varied according to the concentration of lignin. At low concentration, the electron density cloud between highly branched hardwood lignin and NR is lower, leading to smaller interactions. However, at higher concentrations, the van der Waals term between the less polar and larger number of atoms of hardwood lignin and NR becomes stronger, resulting in the opposite trend being observed. Furthermore, NR-L is governed primarily by van der Waals forces, while lignin-lignin is governed primarily by electrostatic interactions. These detailed characterizations offered valuable insight for future research endeavors aimed at designing and synthesizing green NR-L composites at the atomic scale.

Dispersion hardening using amorphous nanoparticles deployed via additive manufacturing

Nanoparticles or precipitates are long used to block dislocations to strengthen metals. However, this strengthening mechanism unavoidably adds stress concentrations at the obstacles, instigating crack initiation that hampers ductility. Here, we demonstrate a strategy that replaces the traditional crystalline dispersions with dense amorphous nanoparticles, which is made possible via laser powder bed fusion. Porosity-free copper-based nanocomposites are demonstrated as a prototype, consisting of densely and uniformly distributed amorphous boron-carbide nanoparticles (~47 nm in average diameter, up to 12% volume fraction) via an in situ nanofragmentation and melt-quench process. The amorphous nanoparticles act as dislocation sinks, thereby alleviating local stress concentration. They also self-harden along with tensile deformation, promoting strain hardening and therefore homogeneous plastic flow. The as-built composite achieves a tensile strength of more than one gigapascal and a total elongation of approximately 10%, more than twice that of its crystalline dispersion counterpart. Defect accumulation is also suppressed upon cyclic deformation of the as-built bulk nanocomposites, delivering a fatigue strength limit (at > 107 cycles) of more than 70% of the tensile strength. Our results demonstrate an effective strategy for additive manufacturing of metallic materials with superior properties.

Unravelling the cleavage-rate relationship from both the experimental and theoretical standpoint: The instance of fluorite dissolution

The phenomenon of solid dissolution into a solution constitutes a fundamental aspect in both natural and industrial contexts. Nevertheless, its intricate nature at the microscale poses a significant challenge for precise quantitative characterization at a foundational level. In this work, the influence across three specific cleavage planes, namely (100), (111), and (110) on the dissolution kinetics of fluorite in aqueous environments was examined from both experimental and theoretical standpoints. For the very first time, the surface potential of fluorite planes during dissolution was measured by means of a fluorite single-crystal electrode. Experimental results indicate that the dissolution of fluorite leads to a marked increase in surface roughness as well as an augmentation in the surface area of all analyzed surfaces. The most significant alteration in roughness is observed on the (111) plane, whereas the most substantial increase in surface area occurs on the (110) plane. In comparison to the (100) crystallographic plane, which demonstrates the slowest dissolution kinetics, the (111) and (110) planes display dissolution at a comparatively expedited rate. Theoretical simulations corroborate this trend, concurrently facilitating an effective examination of the system's free-energy landscape to analyze the dynamics and rates associated with the attachment and detachment of ions to the fluorite surface. Notably, the presence of interfacial defects has the potential to influence the free energy landscape, thereby altering the transition of ions into the bulk solution. Ultimately, the interplay of correlations and discrepancies between experimental findings and theoretical predictions is critically examined.

Hierarchical Deep Potential with Structure Constraints for Efficient Coarse-Grained Modeling

Coarse-grained molecular dynamics is a powerful approach for simulating large-scale systems by reducing the number of degrees of freedom. Nonetheless, the development of accurate coarse-grained force fields remains challenging, particularly for complex systems, such as polymers. In this study, we introduce a novel framework, hierarchical deep potential with structure constraints (HDP-SC), designed to construct coarse-grained force fields for polymer materials. Our methodology integrates a prior energy term obtained through direct Boltzmann inversion with a deep neural network potential, which is trained using hierarchical bead environment descriptors. This framework facilitates the reproduction of structural distributions and the potential of mean force, thus enhancing the accuracy and efficiency of the coarse-grained model. We validate our approach using polystyrene systems, demonstrating that the HDP-SC model not only successfully reproduces the structural properties of these systems but also remains applicable at larger scales. Our findings underscore the promise of machine learning-based techniques in advancing the development of coarse-grained force fields for polymer materials.

KUTE: Green-Kubo Uncertainty-Based Transport Coefficient Estimator

An algorithm for the calculation of transport properties from molecular dynamics simulations, kute, is introduced. The method estimates the integrals from the Green-Kubo theorem, taking into account the uncertainties of the correlation functions in order to eliminate arbitrary cutoffs or external parameters whose values could alter the result. In this contribution, the performance of kute is tested against other popular methods for the case of a protic ionic liquid for a variety of transport properties. It is found that kute achieves the same degree of accuracy as the equivalent formulation of the Einstein relations while performing better than other methods to calculate transport properties using Green-Kubo methods.

Lessons Learned on Obtaining Reliable Dynamic Properties for Ionic Liquids

Ionic liquids are nowadays investigated with respect to their use as electrolytes for high-performance energy storage materials. In this study, we provide a tutorial on how to calculate dynamic properties such as self-diffusion coefficients, ionic conductivities, transference numbers, as well as ion pair and ion cage dynamics, that all play a role in judging the applicability of ionic liquids as electrolytes. For the case of the ionic liquid[ C 2 C 1 Im ] [ NTf 2 ] ${[{\rm{C}}_2 {\rm{C}}_1 {\rm{Im}}][{\rm{NTf}}_2 ]}$ , we investigate the performance of different force fields. Amongst them are non-polarizable models employing unity charges, a charge-scaled version of a non-polarizable model, a polarizable model and another non-polarizable model with refined Lennard-Jones parameters. We also study the influence of the system size on the dynamic properties. While all studied force field models capture qualitatively correct trends, only the polarizable force field and the non-polarizable force field with refined Lennard-Jones parameters provide quantitative agreement to reference data, making the latter model very attractive for the reason of lower computational costs.

Environmentally tolerant multifunctional eutectogel for highly sensitive wearable sensors

Flexible hydrogel sensors have found extensive applications. However, the insufficient sensing sensitivity and the propensity to freeze at low temperatures restrict their use, particularly in frigid conditions. Herein, a multifunctional eutectogel with high transparency, anti-freezing, anti-swelling, adhesive, and self-healing properties is prepared by a one-step photopolymerization of acrylic acid and lauryl methacrylate in a binary solvent comprising water and deep eutectic solvent (DES). The results from the molecular dynamics simulations and density functional theory indicate that the hydrogen bonds between DES and water mixtures possess better stability than those between water molecules. On the other hand, DES breaks down hydrogen bonds in water, providing eutectogels with excellent anti-freezing even at -60 °C. Cetyltrimethylammonium bromide is incorporated to establish stable hydrophobic interactions and electrostatic attractions with polymer chains in the eutectogel network, resulting in superior mechanical (elongation at break of 2890%) and anti-swelling (only 2% swelling in water over 7 days) properties. The eutectogel-based strain sensors exhibit remarkable sensitivity, achieving a gauge factor of up to 15.4. The multifunctional eutectogel sensors can monitor motion and transmit encrypted information at low temperatures, demonstrating considerable potential for applications in flexible electronics within low-temperature environments.

Delving into the crucial role of the initial structure in the dynamic and self-assembly of amyloid beta

Alzheimer's disease involves the accumulation of amyloid beta (Aβ) monomers that form oligomers and fibrils in the brain. Studying the Aβ monomer is critical for understanding Aβ assembly and peptide behavior and has implications for drug design. Choosing a starting structure with a higher aggregation tendency for cost-effective MD studies and drug design is crucial. Previous studies have utilized distinct initial conformations, leading to varying results. Hence, this study was conducted to compare different initial conformations using the same MD simulation protocol to investigate the behavior and oligomerization propensity of different starting structures of Aβ during 1μs. The behavior of the monomers and their self-assembly systems were studied thoroughly, and the results revealed that highly helical Aβ monomers which used as starting structures retain high helix content during the simulation, and their tautomerization states did not cause significant changes in the structure. On the other hand, the Aβ extended and S-shaped monomers displayed the fingerprints of the fibril structure, which is believed to be more favorable for self-assembly. Self-assembly behaviors were seen for three S-shaped and three Aβ extended peptides. However, both conformations did not show stable β-sheet intermolecular interaction. For the Aβ16-22 monomer as a fragment of the Aβ that can assemble into fibrils, the impacts of capping and uncapping on the initial structure were also investigated. The results displayed that capped and uncapped structures can form oligomers with β-sheet at termini. However, in the capped state, β-sheet interactions were more stable and remained relatively longer than uncapped.

Structure of Novel Phosphonium-Based Ionic Liquids with S and O Substitutions from Experiments and a Mixed Quantum-Classical Approach

This article presents experimental characterization information and synchrotron X-ray scattering measurements on a set of novel O- and S-substituted phosphonium-based ionic liquids (ILs) all coupled with the bis(fluorosulfonyl)imide (FSI-) anion. The ILs include the ethoxyethyltriethylphosphonium (P222(2O2)+) and triethyl[2-(ethylthio)ethyl]phosphonium (P222(2S2)+) cations, and we contrast results on these with those for unsubstituted triethylpentylphosphonium (P2225+). The article also introduces a physics-based protocol that combines classical force field studies on larger simulation boxes with classical and first-principles studies on smaller boxes. The method produces significantly improved S(q) functions in the regime which in prior publications we have associated with inter- and intraionic adjacency correlations. By understanding which shorter-range structural changes improve S(q) in the q-regime of interest, we are also able to pinpoint specific deficiencies in the classical force field model. The approach we take should be quite general and could help study other complex liquids on different length scales.

Modulating product selectivity in lignin electroreduction with a robust metallic glass catalyst

Converting the lignin into value-added chemicals and fuels represents a promising way to upgrade lignin. Here, we present an effective electrocatalytic approach that simultaneously modulates the depolymerization and hydrogenation pathways of lignin model compounds within a single reaction system. By fine-tuning the pH of the electrolyte, we achieve a remarkable shift in product selectivity, from acetophenone (with selectivity >99%) to 1-phenylethanol (with selectivity >99%), while effectively preventing over-hydrogenation. The robust metallic glass (MG) catalyst, endowed with an amorphous structure, demonstrates high stability, activity, and full recyclability across over 100 consecutive cycles in ionic liquid electrolytes. The relatively strong affinity of the MG catalyst for the substrate during the initial reaction stage, in conjunction with its weaker binding to the phenolic product, as the reaction progresses, creates a delicate balance that optimizes substrate adsorption and product desorption, which is pivotal in driving the cascade hydrogenation process of acetophenone. This work opens versatile pathways for lignin upgrading through integrated tandem reactions and expands the scope of catalyst design with amorphous structures.

Understanding the Role of Solvent Polarity and Amino Acid Composition of Cyclic Peptides in Nanotube Stability

Cyclic peptides (CPs) possess the ability to self-assemble into cyclic peptide nanotubes (CPNTs), which find extensive applications in nanotechnology. The formation and stability of these nanotubes are influenced by multiple factors. The present study explores the stability of CPNTs in various solvents with varying polarity, focusing on three specific peptide sequences: DK4, WL4, and DLKL2. Using molecular dynamics simulations, the effect of solvent polarity and peptide composition on the stability of CPNTs is assessed through the determination of electrostatic, van der Waals, and hydrogen-bonding interactions. The binding free energy between adjacent cyclic peptide rings is analyzed via MM/GBSA and MM/PBSA methods, revealing that DLKL2, an amphiphilic peptide, exhibits greater stability than DK4 and WL4 in nonpolar solvents. The introduction of leucine residues in DLKL2 reduces intramolecular hydrogen bonding and electrostatic interactions, promoting stronger interpeptide backbone hydrogen bonds and maintaining the nanotube's structural integrity. Hydrogen bond lifetimes, computed using the corresponding time correlation function, indicate the longest-lasting hydrogen bonds occur in all the solvent environments except water, further contributing to the stability of DLKL2 nanotubes. Additionally, deformation from circularity in the peptide rings, analyzed using ellipticity values, highlights the degree of structural distortion across solvents, with DK4 showing the highest deviation due to stronger intramolecular interactions. These findings offer valuable insights into the roles of solvent and peptide composition in the self-assembly and stability of CPNTs, which have significant implications for their potential applications in nanotechnology and biomedicine.

Wettability of Chemically Heterogeneous Clay Surfaces: Correlation between Surface Defects and Contact Angles as Revealed by Machine Learning

Quantifying the wettability of clay surfaces and how it changes in the presence of gas mixtures is crucial for designing geo-energy applications such as underground hydrogen storage and carbon capture and sequestration. While computational studies exist for the wettability of atomically perfect mineral substrates, actual minerals possess heterogeneities. This study employs molecular dynamics simulations to examine the impact of surface defects on the wettability of kaolinite surfaces exposed to hydrogen, methane, and carbon dioxide. The results show that siloxane surfaces become more hydrophilic as defect densities increase and that the gases can strongly affect wettability. Carbon dioxide, in particular, shows stronger adsorption on heterogeneous surfaces than hydrogen and methane. As a consequence, carbon dioxide can strongly affect wettability. Additionally, our results show that higher salt concentrations reduce water contact angle, which is important because salt is likely present in the subsurface. A machine learning classification algorithm is applied to interpret the results and develop predictive capabilities. Our findings highlight the importance of surface defects on wettability, which is essential for designing geological repositories for geo-energy applications ranging from enhanced gas recovery to carbon sequestration and intermittent hydrogen storage.

Interactions of lycopene with β-cyclodextrins: Raman spectroscopy and theoretical investigation

Carotenoids (Cars) are essential molecules with diverse biological roles, and their solubility can be enhanced by forming complexes with cyclodextrins, which incorporate them into their cavities without chemical bonds. In this study, we investigated the interactions of lycopene with β-cyclodextrins using Raman spectroscopy, molecular dynamics and DFT. We simulated models of simplified structures, including various β-cyclodextrins and trans and cis lycopene configurations, to better understand how structural changes influence the v1 band shift observed in Raman spectroscopy. Our focus was on reducing interactions such as van der Waals interactions, hydrogen bonds, and electrostatic effects to isolate the impact of structural alterations on the methyl group in carotenoids. We measured solid lycopene and its complexes with β-cyclodextrins, which exhibited Raman v1 shifts. According to the modelling data, this can be attributed to the monomer band of lycopene.

Effects of Hydration Level and Hydrogen Bonds on Hydroxide Transport Mechanisms in Anion Exchange Membranes

The transport of hydroxide in anion exchange membranes (AEMs) is generally determined by multiple factors, including hydration levels, pore morphologies, and the hydration shells of cationic groups and hydroxides. Thus, clarifying the working mechanisms benefits the proposal of strategies for enhancing the hydroxide transport, thereby enabling a rational design of high-performance AEMs. Herein, by using ReaxFF molecular dynamics (MD) simulations and RDAnalyzer, this study explores the straightforward but effective correlations for steric hindrance versus hydration shell, hydration level versus free/associated diffusion, and strong (short) hydrogen bond (SHB) versus vehicular/Grotthuss diffusion. The theoretical investigations indicate that higher steric hindrance of cationic groups results in less water in the first hydration shell of cationic groups in AEMs. Meanwhile, a higher hydration level facilitates wider hydrophilic pores of AEMs and increases the ratio of the free diffusion mechanism of hydroxides. Interestingly, this study finds a strong correlation between the number of SHBs and the Grotthuss diffusion, thereby enhancing the understanding of the high conductivity of covalent organic framework (COF)-based AEMs that contain obvious SHBs. This work provides a theoretical view for fine-tuning the free/associated and vehicular/Grotthuss transport of hydroxide in AEMs.

On the Local Structure of Water Surrounding Inorganic Anions Within Layered Double Hydroxides

Understanding the microscopic structure and physical-chemical properties of materials with nanoconfined domains is essential for advancing technologies in catalysis, nanomaterial design, and pharmaceutical applications. Layered double hydroxides (LDHs) are promising candidates for such innovations due to their tunable interlayer environment, which can be precisely controlled by varying the type of intercalated anion and the amount of water present. However, optimizing LDH-based technologies requires detailed insights into the local structure within the interlayer region, where complex interactions occur among anions, water molecules, and the inorganic surfaces. In this work, we present a comprehensive computational study of LDHs intercalating small inorganic anions at varying hydration levels, using atomistic molecular dynamics simulations. Our findings show good agreement with existing experimental and simulation data. We observe that monoatomic ions form either a monolayered or double-layered structures, with water molecules lying flat at low hydration and adopting more disordered configurations near the surfaces at higher hydration. In contrast, polyatomic anions exhibit distinct structural behaviors: nitrates adopt tilted orientations and form double layers at high hydration, similar to perchlorates, while carbonates consistently remain flat. Additionally, water molecules strongly interact with both anions and the surface, whereas anion-surface interactions weaken slightly as hydration increases. These results offer valuable insights into the local structural dynamics of LDHs, paving the way for more efficient design and application of these versatile materials.

Controlling the Morphology and Orientation of the Helical Self-Assembly of Pyrazine Derivatives by Tuning Hydration Shells

A combination of density functional theory (DFT) and classical molecular dynamics simulations is performed to unveil the guiding force in the self-assembly process of the pyrazine-based biopolymers to helical nanostructures. The highlight of the study shows the decisive role of the solvent-ligand H-bonding and the inter-molecular pi-pi stacking not only ensures the unidirectional packing of the helical structure but also the rotation of left-handed to the right-handed helical structure of the molecule. This transition is supported by the bulk release of the "ordered" water molecules. The extent of this bonding can be tuned by the temperature, concentration, and type of the metal ions. Smaller ions like Na+ and Al3+ destroy the structure, whereas bigger ions like Zn2+, Ni2+, and Au3+ preserve and rotate the structure according to their concentration. The interaction energy between the pyrazine derivatives is found to be high (-9000 kJ mol-1) for right-handed rotation of the helix, which increases further with the addition of D-histidine, forming a superhelical structure (-10300 kJ mol-1). The insights gained from this work can be used to generate nanostructures of desired morphology.

Transport properties of dissolved [O] in the (LiF-CaF2)eut.-NdOF molten salt system

The transport properties of [O] in dissolved rare-earth oxyfluorides provide an important theoretical basis for optimizing anode reactions during molten salt electrolysis in fluoride systems for rare-earth metal production. In this study, the solubility of NdOF in (LiF-CaF2)eut. was investigated, and the electrical conductivity and density of the saturated NdOF system were measured. The self-diffusion coefficients and radial distribution functions of the ions in the system were analyzed using first-principles molecular dynamics. The transport number of dissolved O*(ii) ions in the saturated (LiF-CaF2)eut.-NdOF system was determined using the coulometric method, providing a comprehensive analysis of the variation in the migration rate and diffusion coefficients of dissolved O*(ii) ions. The results showed that the solubility of NdOF in the (LiF-CaF2)eut. System, along with the electrical conductivity and density of the saturated system, exhibited linear variations in the temperature range of 1123-1373 K. The transport number, migration rate, and diffusion coefficient of dissolved O*(ii) ions underwent non-linear changes with increasing potential within the range of 3.0-4.5 V, reaching a maximum in the range of 3.75-4.25 V, while still increasing linearly with temperature. When the [temperature-potential] was in the [1200 K↑-3.5 V↑] range, the migration rate and diffusion coefficient of O*(ii) ions were the highest, with the potential playing a dominant role in the diffusion coefficient of O*(ii) ions. The radial distribution function values of the ions in the system indicated that Nd*(iii) ions had the strongest restraining effect on the dissolved O*(ii) ions during the diffusion process.

Photoluminescent delocalized excitons in donor polymers facilitate efficient charge generation for high-performance organic photovoltaics

Efficient delocalization of photo-generated excitons is a key to improving the charge-separation efficiencies in state-of-the-art organic photovoltaic (OPV) absorber. While the delocalization in non-fullerene acceptors has been widely studied, we expand the scope by studying the properties of the conjugated polymer donor D18 on both the material and device levels. Combining optical spectroscopy, X-ray diffraction, and simulation, we show that D18 exhibits stronger π-π interactions and interchain packing compared to classic donor polymers, as well as higher external photoluminescence quantum efficiency (~26%). Using picosecond transient absorption spectroscopy and streak camera photoluminescence measurements, we show that the initial D18 excitons form delocalized intermediates, which decay radiatively with high efficiency in neat films. In single-component OPV cells based on D18, these intermediate excitations can be harvested with an internal quantum efficiency >30%, while in blends with acceptor Y6 they provide a pathway to free charge generation that partially bypasses performance-limiting charge-transfer states at the D18:Y6 interface. Our study demonstrates that donor polymers can be further optimized using similar design strategies that have been successful for non-fullerene acceptors, opening the door to even higher OPV efficiencies.

Screening and Design of Aqueous Zinc Battery Electrolytes Based on the Multimodal Optimization of Molecular Simulation

Aqueous batteries, such as aqueous zinc-ion batteries (AZIB), have garnered significant attention because of their advantages in intrinsic safety, low cost, and eco-friendliness. However, aqueous electrolytes tend to freeze at low temperatures, which limits their potential industrial applications. Thus, one of the core challenges in aqueous electrolyte design is optimizing the formula to prevent freezing while maintaining good ion conductivity. However, the experimental trial-and-error approach is inefficient for this purpose, and existing simulation tools are either inaccurate or too expensive for high-throughput phase transition predictions. In this work, we employ a small amount of experimental data and differentiable simulation techniques to develop a multimodal optimization workflow. With minimal human intervention, this workflow significantly enhances the prediction power of classical force fields for electrical conductivity. Most importantly, the simulated electrical conductivity can serve as an effective predictor of electrolyte freezing at low temperatures. Generally, the workflow developed in this work introduces a new paradigm for electrolyte design. This paradigm leverages both easily measurable experimental data and fast simulation techniques to predict properties that are challenging to access by using either approach alone.

Unraveling the impact of binary vs. ternary alcohol solutions on the conformation and solvation of the SARS-CoV-2 receptor-binding domain

The use of alcohol as hand sanitizer to prevent the spread of contamination of SARS-CoV-2 is known. In this work, a series of atomistic molecular dynamics (MD) simulations were carried out with the receptor-binding-domain (RBD) of SARS-CoV-2 in different aqueous binary and ternary mixtures of concentrated ethanol, n-propanol (n-pr), and iso-propanol (iso-pr) solutions to elucidate the structural alteration of RBD at ambient and elevated temperature and to understand RBD's interactions with the host cellular receptor ACE2. Computation of several structural metrics like RMSD, Rg, and fraction of native contacts along with the construction of a 2D-free energy landscape suggests that among all the water-alcohol(s) solutions, the structural transition of RBD conformation was more pronounced in the water-etoh-iso-pr mixture under ambient conditions which further altered significantly and RBD adopted partially unfolded states at 350 K, as compared to the native form. We observed that the preferential exclusion of different alcohols from the RBD surface regulates the solvation features of RBD and hence the RBD-alcohol hydrogen bonds, which is one of the crucial factors that rupture RBD's structure heterogeneously. From the comparative study, it was inferred that relative to binary mixtures, the ternary solutions rupture the native RBD structure more effectively which was caused by the relative reduction in dynamics in the ternary mixture for the particular pair of hydrogen bonds arising from the hindered rotation of certain alcohol molecules. Our microscopic investigation identified that the specific binding zone was disrupted remarkably, and as a result, the contact distances between the deformed binding zone of RBD and ACE2 were found to increase from the molecular docking study; this could prevent further transmission.

PCDTBT: Force Field Parameterization and Properties by Molecular Dynamics Simulation

Conjugated polymers are indispensable building blocks in a variety of organic electronics applications such as solar cells, light-emitting diodes, and field-effect transistors. Poly[N-9'-heptadecanyl-2,7-carbazole-alt-5,5-(4',7'-di-2-thienyl-2',1',3'-benzothiadiazole)] (PCDTBT) is a carbazole-benzothiadiazole-based copolymer with a donor-acceptor structure, consisting of electron-donating and electron-withdrawing subunits and featuring a low band gap. In this work, the General Amber Force Field is extended in two ways, specifically for modeling PCDTBT. First, a set of partial atomic charges is derived that mimic a long chain and adequately describe different conformations that may be encountered in a bulk environment. Second, torsional terms are reparametrized for all dihedral angles in the backbone via ab initio computations. Subsequently, a series of large-scale Molecular Dynamics simulations are employed to construct and equilibrate bulk ensembles of three PCDTBT oligomers using different starting conformations of the oligomer chains. Several structural properties are computed, namely mass density, chain stiffness (through persistence length and Kuhn segment length), and glass transition temperature. Our results are in good agreement with available literature data, demonstrating the suitability of the new parametrization.

Generative Landscapes and Dynamics to Design Multidomain Artificial Transmembrane Transporters

Protein design is challenging as it requires simultaneous consideration of interconnected factors, such as fold, dynamics, and function. These evolutionary constraints are encoded in protein sequences and can be learned through the latent generative landscape (LGL) framework to predict functional sequences by leveraging evolutionary patterns, enabling exploration of uncharted sequence space. By simulating designed proteins through molecular dynamics (MD), we gain deeper insights into the interdependencies governing structure and dynamics. We present a synergized workflow combining LGL with MD and biochemical characterization, allowing us to explore the sequence space effectively. This approach has been applied to design and characterize two artificial multidomain ATP-driven transmembrane copper transporters, with native-like functionality. This integrative approach proved effective in unraveling the intricate relationships between sequence, structure, and function.

Optimizing Polyethylene Glycol Coating for Stealth Nanodiamonds

Nanodiamonds (NDs) have emerged as potential candidates for versatile platforms in nanomedicine, offering unique properties that enhance their utility in drug delivery, imaging, and therapeutic applications. To improve their biocompatibility and nanomedical applicability, NDs are coated with organic polymer chains, such as poly(ethylene glycol) (PEG), which are well known to prolong their blood-circulating lifetime by reducing the surface adsorption of serum proteins. Theoretical simulations are useful tools to define, at the atomic level, the optimal parameters that guide the presentation of the coating chains in the biological environment and the interaction of coated NDs with proteins. In this work, we perform atomistic molecular dynamics (MD) simulations of several PEGylated spherical ND models immersed in a realistic physiological medium. In particular, we evaluate the effect of the polymer chain's terminal group, length, grafting density, and the ND core dimension on both the structural properties of the PEG coating and the interaction of the nanoconjugates with the aqueous phase. Moreover, we investigate the role played by the chemical nature of the core material through a comparative analysis with a PEGylated spherical titanium dioxide (TiO2) nanoparticle (NP). Among all the parameters evaluated, we find that the PEG grafting density, the PEG chain length, and the NP core material are key factors in determining the dynamic behavior of PEGylated nanosystems in solution, whereas the PEG terminal group and the ND dimension only play a marginal role. These factors can be strategically adjusted to identify the optimal conditions for enhanced clinical performance. Finally, we prove that the PEG coating prevents the aggregation of two ND particles. We believe that this computational study will provide valuable insights to the experimental community, supporting the rational design of polymer-coated inorganic NPs for more efficient nanomedical applications.

Investigation of Surface-Induced Dissociation Processes via Molecular Dynamics Simulations of Wall Collisions of Large Droplets Produced by Electrospray Ionization

Electrospray ionization is one of the most utilized ionization techniques in atmospheric pressure mass spectrometry. Recent experimentally reported results are in disagreement with fundamentals revolving around ESI droplet sizes and their lifetimes. Specifically, much larger droplet sizes and longer lifetimes have been experimentally observed to exist in typical ESI ion sources. Experiments involving a custom scan mode on a triple quadrupole system have shown that high-mass fragments of large ESI droplets can be observed in mass spectra. Initial hypotheses rationalizing these results were focused on the creation of droplet fragments by collision-induced dissociation (CID). The collision energy accumulated by CID is most likely too small to lead to the observed mass spectra. In response, surface-induced dissociation (SID) was proposed as an additional mechanism to provide large amounts of collision energy to the droplets. The present work thus investigates the possible fragmentation pathways and dynamics of droplet fragments resulting from aspirated ESI droplets upon surface collisions through classical molecular dynamics simulations. Different types of collisions are simulated, where the impact of the simulated droplet fragments is either frontal or angled. The resulting fragmentation dynamics are thoroughly analyzed, showing the possibility for charged fragments to be liberated through SID events. A second, much larger droplet fragment is employed to illustrate the altered collision dynamics found for such larger aggregates, where no charged clusters are released through the surface collision. Since approximated force fields have to be used to model the interactions between the particles observed in the simulation, a sensitivity study is carried out regarding the critical parameters governing such processes. Further modifications of the MD system have to be carried out, including more realistic walls and much larger ESI droplets, to clarify the possibility of charged fragment releases from larger droplet fragments through SID.

High Output Voltage Aqueous Supercapacitors by Water Deactivated Electrolyte over Wide Temperature Range

Confined by factors such as low operating voltage, poor temperature resistance, and instability at high voltage, the energy density of conventional symmetric aqueous supercapacitors is undesirable over a wide temperature range. It is still challenging to develop aqueous flexible supercapacitors (AFSCs) that can provide stable and high voltage output (>2.0 V) at extreme ambient temperatures. Here, a strategy for constructing AFSC with ultrahigh output voltages over a wide temperature range is proposed through the development of organohydrogel electrolytes (OHEs) with excellent water deactivation, which achieve a notable output voltage of 3.0 V, and unprecedented energy densities of 23.16 µWh cm-2 at -40 °C (beyond 25 °C), surpassing the performance of all previously reported symmetric supercapacitors with aqueous electrolytes. Theoretical calculations and experimental analyses show that OHEs can deactivate water to increase the output voltage limit of AFSCs by enhancing intermolecular interactions and regulating inter Helmholtz plane. Meanwhile, it also shows excellent flexibility and cycling stability (80.5% after 20 000 cycles at 25 °C and 97.0% after 50 000 cycles at -40 °C). More importantly, OHEs enable AFSCs switchable output voltages (from 2.5 to 3.0 V), making it possible to operate supercapacitors with high energy density and stability at low temperatures.

High-Entropy Electrolyte Design for Low-Temperature Supercapacitors

Supercapacitors (SCs) are high-power energy storage devices but often experience reduced electrochemical performance at low temperatures, especially below -30 °C, due to the high freezing points of conventional electrolytes. In this study, we introduce a novel high-entropy electrolyte (HEE) for supercapacitors that extends operational capabilities over a wide temperature range. The high entropy of the HEE results in an exceptionally low freezing point of -116 °C. With an increased number of solvent molecules in the cation solvation structures, the HEE exhibits high conductivity (3.9 mS cm-1 at -50 °C) and low de-solvation energy (14.1 kJ mol-1). When incorporated into a carbon-based SC, the HEE enables a capacitance retention of 58 % at temperatures below -30 °C, compared to 25 °C, while conventional single-solvent electrolytes retain only 38 %. Additionally, the HEE provides superior high-rate performance and excellent cycling stability, maintaining 88 % capacitance after 15,000 cycles, compared to 73 % with conventional electrolytes.

Revealing the roles of the solid-electrolyte interphase in designing stable, fast-charging, low-temperature Li-ion batteries

Designing the solid-electrolyte interphase (SEI) is critical for stable, fast-charging, low-temperature Li-ion batteries. Fostering a "fluorinated interphase," SEI enriched with LiF, has become a popular design strategy. Although LiF possesses low Li-ion conductivity, many studies have reported favorable battery performance with fluorinated SEIs. Such a contradiction suggests that optimizing SEI must extend beyond chemical composition design to consider spatial distributions of different chemical species. In this work, we demonstrate that the impact of a fluorinated SEI on battery performance should be evaluated on a case-by-case basis. Sufficiently passivating the anode surface without impeding Li-ion transport is key. We reveal that a fluorinated SEI containing excessive and dense LiF severely impedes Li-ion transport. In contrast, a fluorinated SEI with well-dispersed LiF (i.e., small LiF aggregates well mixed with other SEI components) is advantageous, presumably due to the enhanced Li-ion transport across heterointerfaces between LiF and other SEI components. An electrolyte, 1 M LiPF6 in 2-methyl tetrahydrofuran (2MeTHF), yields a fluorinated SEI with dispersed LiF. This electrolyte allows anodes of graphite, μSi/graphite composite, and pure Si to all deliver a stable Coulombic efficiency of 99.9% and excellent rate capability at low temperatures. Pouch cells containing layered cathodes also demonstrate impressive cycling stability over 1,000 cycles and exceptional rate capability down to -20 °C. Through experiments and theoretical modeling, we have identified a balanced SEI-based approach that achieves stable, fast-charging, low-temperature Li-ion batteries.

Carbocation charge as an interpretable descriptor for the catalytic activity of hydrolytic nanozymes

A universal theory for predicting the catalytic activity of hydrolytic nanozymes has yet to be developed. Herein, by investigating the polarization and hydrolysis mechanisms of nanomaterials towards amide bonds, carbocation charge was identified as a key electronic descriptor for predicting catalytic activity in amide hydrolysis. Through machine learning correlation analysis and the Sure Independence Screening and Sparsifying Operator (SISSO) algorithm, this descriptor was interpreted to associate with the d-band center and Lewis acidity on the nanomaterial surface. On this basis, copper nanoparticles (Cu NPs) were discovered to exhibit significant hydrolytic activity. Further, peptidomic analysis and molecular dynamics simulations showed that Cu NPs demonstrated substrate selectivity. In the presence of water molecules, hydrophobic amino acid residues were driven towards the nanomaterial surface by hydrophobic groups of proteins, leading to the preferential hydrolysis of peptide bonds linked to these residues. This study provided a theoretic framework for predicting highly efficient hydrolytic nanozymes with broad potential applications.

Solvation layer effects on lithium migration in localized High-Concentration Electrolytes: Analyzing the diverse antisolvent Contributions

Localized high-concentration electrolytes (LHCEs) offer a new methodology to improve the functionality of conventional electrolytes. Understanding the impact of antisolvents on bulk electrolytes is critical to the construction of sophisticated LHCEs. However, the mechanism of how antisolvent modulates the electrochemical reactivity of the solvation structure in LHCEs remains unclear. In this work, the key correlation between the physicochemical properties of antisolvents and their corresponding Lithium-ion battery (LIBs) systems has been elucidated by comprehensive multiscale theoretical simulations combined with experimental characterizations. Nine antisolvents (chain ethers and cyclic non-ethers) are investigated in a typical lithium bis(fluorosulfonyl)imide/1,2-dimethoxymethane (LiFSI/DME) system. It is highlighted that the relative molecular masses of antisolvents in the same class are positively correlated with the density. The viscosity of a liquid mixture consisting of DME and antisolvent in the same class is positively correlated with the magnitude of the interaction energy between them. Additionally, the self-diffusion coefficient of Li+ is also positively correlated with the sum of the interaction energies between Li+-DME and Li+-FSI-, which is also affected by the class of antisolvent. These results provide deep insights into the behavior and properties of LHCEs, which help to advance the design of high-performance LIBs.

Synthesis of New Cationic Dicephalic Surfactants and Their Nonequivalent Adsorption at the Air/Solution Interface

The interfacial behavior of aqueous solutions of newly synthesized 2-alkyl-N,N,N,N',N',N'-hexamethylpropan-1,3-ammonium dibromides with decyl, dodecyl, and tetradecyl alkyl chains was investigated both experimentally and theoretically. The results of the surface tension measurements were described using the modified surface quasi-two-dimensional electrolyte (mSTDE) model of ionic surfactant adsorption, which was supported by molecular dynamics simulations. Our contribution encompasses the design, synthesis, and characterization of a novel class of dicephalic-type cationic surfactants, branched on a methine motif, possessing two symmetric trimethylammonium groups, which constitute a double-head extension of the standard alkyltrimethylammonium salts of the single-head, single-tail structure. The convenient synthetic route and final purification steps allowed for the high-yield, high-purity production of the surfactants. Dicephalic-type surfactants demonstrated lower surface activity and higher critical micelle concentration values when compared with their single head-single tail counterparts. That can be attributed primarily to the presence of strong electrostatic repulsive forces within the bulky, double-charge headgroups and significant counterion condensation. Furthermore, molecular dynamics simulations demonstrated a propensity for the desorption of surfactants from the interface, even in diluted solutions, which constrained the attainable surface concentration and resulted in a lower reduction in surface tension. The mSTDE model of adsorption provided an excellent description of the experimental surface isotherms with a concise set of parameters. The model's predictive power was demonstrated by the studies of the effect of inorganic salts on the surface activity of investigated surfactants. Our unique approach enabled us to gain a theoretical explanation of the newly devised surfactants' behavior at the water/air interface.

Detergent induced structural perturbations in peanut agglutinin: insights from spectroscopic and molecular dynamic simulation studies

The three dimensional structure of a protein is very important for its structure. Studies relating to protein structure have been numerous and the effect of denaturants on proteins can help understand the process of protein folding and misfolding. Detergents are important denaturants and play important roles in various fields. Here we explored the effect of sodium dodecyl sulphate (SDS) and cetyltrimethylammonium bromide (CTAB) on the structure of peanut agglutinin (PNA). The protein was purified from its natural source and impact of SDS and CTAB was studied by circular dichroism, intrinsic fluorescence, 8-anilino-1-napthalenesulfonic acid, molecular docking and molecular dynamics simulation. Pure peanut agglutinin showed a trough at 220 nm and positive ellipticity peak at 195 nm, specific for lectins. Results from the experimental and simulation studies suggest how oppositely charged detergents can interact differently and lead to varied structural perturbations in PNA. Both the surfactants induce all α protein-like circular dichroism in the protein, above its critical micelle concentrations, with significant change in accessible surface area that became more hydrophobic upon the treatment. Major interactions between the surfactants and protein, resulting in PNA conformational rearrangement, are electrostatic and van der Waals interactions. However, CTAB, a cationic surfactant, has similar effects as anionic surfactant (SDS) but at significantly very low concentration. Though the effects followed same pattern in both the surfactant treatment, i.e. above respective CMC, the surfactants were inducing all α protein-like conformation in PNA.