The Journal of Physical Chemistry B最新文献

The presence and persistence of microplastics (MPs) in the marine environment pose increasing threats to marine organisms and ecosystem health. Environmental monitoring of MPs facilitates assessment of their potential impacts on ecosystems and biota. Although numerous studies have confirmed the widespread presence of MPs pollution in the marine environment, there are still significant differences in the sampling methods and sample quantities used for MPs monitoring. To address these issues, this study investigated the influence of different sampling methods and quantities on the survey results of MPs in the marine environment. The impact of different sample mass on the detection of MPs abundance in sandy and muddy beach sediments of the supratidal, intertidal, and subtidal zones was examined. And the effects of different seawater MPs collection methods (trawl sampling, water collector sampling, and pump sampling) and quantities on MPs abundance detection in seawater were also explored. Results show that the most suitable sample mass for detecting MPs in beach sediments is at least 30 g. Additionally, comprehensive sampling and monitoring of the supratidal, intertidal, and subtidal zones should be conducted to ensure accurate assessment of MPs abundance. Seawater samples were collected via trawl, water collector sampling, and pump sampling to evaluate effects of methods, sample quantities, filter aperture, and sampling depth on the monitoring abundance of MPs. Results show that the optimal sampling parameters are trawl durations at least 10 min and water collector sampling volumes at least 10 L. In the water collector sampling method, the total abundances of MPs after filtration through 48 and 330 μm filters are at the same order of magnitude, indicating that the filtration pore size has no significant effect on the total abundance of MPs. However, the size ranges of retained MPs differ significantly between the two pore sizes. Furthermore, while no significant difference is observed in MPs abundance among different water layers in Leizhou Bay, variations are found in polymer composition and MPs size distribution. This research is helpful in improving the accurate monitoring of MPs in the marine environment.

Liquid-liquid interfacial tension (LL-IFT) and the associated nanoscale interfacial structure are key to an extraction-based separation process. Here, we apply the perturbed-chain statistical associating fluid theory (PC-SAFT)-based classical density functional theory (cDFT) to predict the LL-IFT, interfacial density, and hydrogen-bonding profiles of methanol-n-alkane (n-hexane, n-heptane, and n-octane) mixtures under atmospheric pressure. Each mixture is modeled by using a single temperature-independent binary interaction parameter. To determine the optimal modeling strategy, we systematically assess six combinations derived from four published PC-SAFT parameter sets for methanol and three nonlocal association functionals (i.e., aFMT, aWDA, and iSAFT). The first three parameter sets incorporated the vapor pressure, saturated liquid density, and vapor-liquid interfacial tension (VL-IFT) into the fitting process, with VL-IFT calculated by aFMT, aWDA, and iSAFT for sets 1, 2, and 3, respectively. Parameter set 4 was optimized exclusively to the bulk phase equilibrium data. Notably, despite their superior accuracy in predicting binary VL-IFT, parameter sets 1-3 do not outperform set 4 in predicting binary LL-IFT. Furthermore, both iSAFT and aWDA show good agreement with experimental LL-IFT data, whereas aFMT consistently overestimates the values across all systems and temperatures, irrespective of the methanol parameters used. Although the optimal combination varies by system, the overall performance of the current cDFT framework demonstrates remarkable precision in reproducing LL-IFT. From a structural perspective, monotonic density and hydrogen-bonding profiles with intersection points have been identified: two for density profiles (one per component) and one for hydrogen-bonding profiles.

The SARS-CoV-2 envelope (E) protein is a small viroporin that drives viral assembly, budding, and host interactions, yet its structural organization has remained elusive. Earlier nuclear magnetic resonance spectroscopy studies hint at oligomerization without direct evidence, and the construct lacks the flexible C-terminal region. To bridge this gap, we synthesized the full-length E protein to investigate its oligomeric state. Using size-exclusion chromatography coupled with multiangle light scattering, we demonstrated that the E protein assembles as a stable pentamer in solution. We then reconstituted the E protein into membrane scaffold protein nanodiscs to mimic the lipid bilayer environment for structural analyses by negative-stain electron microscopy and cryo-electron microscopy, which revealed pentamer-like features. Molecular dynamics simulations of the E protein in a nanodisc and a membrane bilayer setting further corroborated the structural flexibility of the C-terminal domain. Collectively, these data present direct evidence that the SARS-CoV-2 E protein assembles as a pentamer in both solution and membrane-mimetic environments. Our results provide a structural foundation for future investigations into the E protein's roles in ion channel activity, membrane remodeling, and virus-host interactions.

Lipopolysaccharides (LPS), as critical components of the outer membrane (OM) of Gram-negative bacteria, play essential roles in maintaining bacterial integrity and mediating environmental interactions. All-atom molecular dynamics (AA-MD) simulations provide detailed insights into LPS behavior at atomic resolution, but they remain computationally limited in accessing biologically relevant time scales. Coarse-grained (CG) models, such as Martini 3, offer a computationally efficient alternative while retaining sufficient accuracy for key biophysical properties. Although Martini 3 has been widely applied to proteins and phospholipids, only a few LPS models have been developed within this framework, limiting its utility for bacterial OM studies. To address this gap, we developed and validated CG parameters for LPS from multiple medically relevant pathogens, including Escherichia coli and Salmonella enterica, as well as two ESKAPE pathogens, Klebsiella pneumoniae and Pseudomonas aeruginosa. Our approach leverages the transferability of Martini parameters: we parametrized 57 unique disaccharide units using the Bartender tool, which automates CG-to-AA mapping and parametrization. These parameters were then combined and manually refined to accurately reproduce the complex dynamics of complete LPS molecules. We conducted extensive AA and CG simulations of asymmetric bilayers composed of phospholipids in the inner leaflet and LPS in the outer leaflet allowing detailed comparisons between the two for key structural and dynamic properties. The close agreement between the CG and AA simulations demonstrates the accuracy and robustness of our transferable parameter set, providing a valuable tool for simulating Gram-negative bacterial OMs at larger scales and longer time scales.

Developing new technology in membrane desalination is crucial for addressing the global water crisis. Reverse osmosis (RO) membranes exhibit numerous advantages, such as high efficiency, cost-effectiveness, environmental sustainability, etc. In this work, we observe an abnormal RO phenomenon for the first time in dipalmitoylphosphatidylcholine (DPPC) bilayers under the stimuli of terahertz (THz) waves. Our RO model contains two DPPC bilayers that divide the saline and aqueous solutions. Surprisingly, under specific field strength and frequency, we observe considerable net water flow from the saline solution chamber, crossing the bilayers, to the aqueous solution chamber, which suggests a new RO phenomenon in a highly controllable fashion. The mechanism for this abnormal RO process is that in THz waves, some ions can strip off their hydration shells and directly adsorb onto the lipid heads, resulting in local aggregation of head groups. This creates large gaps between some lipids and loose membrane structures in the saline solution region, breaking the structural symmetry in bilayers that facilitates the RO permeation. The reduced potential of mean force (PMF) barriers, ion hydration number, ion density behavior, and membrane structure strongly support our explanation of the RO mechanism. Our findings shed light on a complete new mechanism of RO for biological membranes, and breaking the membrane structural symmetry provides a potential new pathway for the design of RO membranes.

With the development of contemporary electronic devices toward miniaturization and integration, polymer composites with efficient heat dissipation are in increasing demand for thermal management. In this study, a star-shaped castor-oil-based modifier (CO-IPMSA) was synthesized and grafted onto boron nitride (BN) to obtain modified BN (ICBN). The resulting ICBN/natural rubber (NR) composites exhibit a tensile strength of 20.44 MPa, an elongation at break of 1451.99%, and a thermal conductivity of 0.933 W·m^-1·K^-1, corresponding to increases of 19.18%, 76.74%, and 55.76%, respectively, compared with the BN/NR composite (17.15 MPa, 821.53%, and 0.599 W·m^-1·K^-1). Molecular dynamics (MD) simulations were conducted to quantify interfacial interactions and microstructural characteristics (e.g., binding energy and fractional free volume), thereby providing mechanistic insight into the improved compatibility after modification.

We briefly discuss herein the molecular mechanism for the cycloaddition of CO₂ to ethylene oxide in a wide variety of ionic liquids (ILs) that include classical ILs based on ammonium, imidazolium, and pyridinium cations and combinations that incorporate (poly)-nuclear ILs as well as ILs based on inorganic complexes. We show that even with strong structural differences, all five cases examined share a common activation step characterized by the formation of a precursor complex followed by its activation toward a common rate-determining-step transition state structure. We additionally show that all these cases can nicely be described within a common quantum mechanical model based on the conceptual density functional theory. It is found that the dominating effect of the activation hardness, Δη^‡, drives the intrinsic reaction barrier in all five cases examined, a result that seems to be governed by the maximum hardness principle, an empirical rule that appears to still remain valid for its application in the chemistry in complex liquids: a promising and stimulating result.

Depending on the initial photoexcitation of the donor or acceptor phase, different efficiencies of charge generation can be observed in organic solar cells. We investigate the origin of this dichotomy by simulations based on quantum-quantum-classical embedded GW-Bethe-Salpeter equation of conversion dynamics from localized to charge-transfer (CT) excitations at the interface of a diketopyrrolopyrrole (DPP) polymer and fullerene. Specifically, we determine the excitonic energy levels, their electronic couplings, and the reorganization energies for the respective conversion processes within Marcus theory. Our calculations yield a variety of CT-type excitations of different characters with the lowest integer CT excitations of relevance for charge generation separated by 0.30 eV. Further analysis reveals that the activation barrier for conversion to the lowest CT state is significantly higher (0.25 eV) for the polymer LE than for the fullerene LE (0.05 eV), leading to a preferred population of the higher, less strongly bound CT state from the photoexcited donor. From a population dynamics model, we find that, indeed, on the time scale of one picosecond after the respective excitation, the donor excitation leads to the formation of a CT excitation with on average 0.16-0.27 eV lower electron-hole binding energy, providing a pathway to faster charge separation.

This study employs coarse-grained molecular dynamics (CGMD) simulations to investigate the dynamic interaction mechanisms between hydrophobic silica nanoparticles (H-SiO₂ NPs) and the cationic surfactant cetyltrimethylammonium chloride (CTAC) in an aqueous environment. A coarse-grained model based on the MARTINI 2.2 force field was constructed, and simulations were performed for 250 ns at 298 K in a 20 × 20 × 20 nm³ system containing one H-SiO₂ NP, 1200 CTAC molecules, 1200 Cl^- counterions, and 64,428 water beads. Key findings reveal that CTAC molecules assemble onto the nanoparticle surface via a stepwise mechanism of "monomer adsorption-micelle migration-cluster attachment," ultimately forming a core-shell structure: an H-SiO₂ NP core surrounded sequentially by CTAC hydrophobic tails and hydrophilic headgroups. Radial distribution function (RDF) analysis demonstrates spatial segregation between CTAC tails (peak at 0.5-2 nm) and headgroups (peak at 4 nm), confirming their oriented alignment. The adsorption quantity progressively increased, exhibiting a stepwise transition at 205 ns due to micellar cluster attachment, culminating in approximately 350 CTAC molecules covering a surface area of 110 nm² (20% coverage). A continuous decrease in system potential energy verifies the spontaneity of the adsorption process. The resulting H-SiO₂ NP/CTAC aggregate attained a hydrated diameter of 5.5 nm, with partial exposure of hydrophobic C2 beads indicating incomplete surface coverage. By elucidating the dynamic adsorption pathway and core-shell formation mechanism of CTAC on hydrophobic nanoparticles, this CGMD study provides a theoretical foundation for optimizing nanofluid stability in petroleum extraction, designing high-efficiency heat-transfer working fluids, and enabling pollutant adsorption remediation.

A stable solid electrolyte interphase (SEI), formed by the reductive decomposition of electrolytes at the anode surface, is crucial for ensuring the safety and long-term performance of lithium metal and post-lithium metal batteries. Conventionally, the lowest unoccupied molecular orbital (LUMO) energy of an ion-solvent complex has been used as the primary descriptor for predicting reductive decomposition kinetics and electrolyte stability. In this work, we critically evaluate this assumption and show that LUMO energies do not exhibit a direct linear correlation with reductive decomposition kinetics. Instead, the relationship between LUMO levels and reductive stability is inherently nonlinear across diverse electrolyte chemistries, limiting the general applicability of LUMO-based screening. To address this limitation, we developed machine learning (ML) models that use multiple structural and electronic structure parameters as input features and free energy barriers as the target quantity. The models are trained on free energy barriers obtained from density functional theory (DFT) calculations for 200 ion-solvent complexes spanning a wide range of lithium metal battery (LMB) and post-LMB electrolytes. These nonlinear models were found to significantly outperform traditional linear approaches based solely on the LUMO energy, yielding more accurate predictions of the reductive stability of electrolytes. Our findings highlight the need for multifeature, nonlinear models to capture the complexity of electrolyte reactivity and offer a computational framework to accelerate the rational design of stable electrolytes for next-generation battery technologies.