Search Results (88)

Liposomes are nanoscale, spherical vesicles composed of phospholipid bilayers, typically ranging from 50 to 200 nm in diameter. Their unique ability to encapsulate both hydrophilic and hydrophobic molecules makes them powerful nanocarriers for drug delivery, diagnostics, and vaccine formulations. Several FDA-approved formulations such as Doxil^® (Baxter Healthcare Corporation, Deerfield, IL, USA), AmBisome^® (Gilead Sciences, Inc., Foster City, CA, USA), and Onivyde^® (Ipsen Biopharmaceuticals, Inc., Basking Ridge, NJ, USA) highlight their clinical significance. This review provides a comprehensive synthesis of how molecular dynamics (MD) simulations, particularly coarse-grained (CG) and atomistic approaches, advance our understanding of liposomal membranes. We explore key membrane biophysical properties, including area per lipid (APL), bilayer thickness, segmental order parameter ($S_{CD}$), radial distribution functions (RDFs), bending modulus, and flip-flop dynamics, and examine how these are modulated by cholesterol concentration, PEGylation, and curvature. Special attention is given to curvature-induced effects in spherical vesicles, such as lipid asymmetry, interleaflet coupling, and stress gradients across the leaflets. We discuss recent developments in vesicle modeling using tools such as TS2CG, CHARMM-GUI Martini Maker, and Packmol, which have enabled the simulation of large-scale, compositionally heterogeneous systems. The review also highlights simulation-guided strategies for designing stealth liposomes, tuning membrane permeability, and enhancing structural stability under physiological conditions. A range of CG force fields, MARTINI, SPICA, SIRAH, ELBA, SDK, as well as emerging machine learning (ML)-based models, are critically assessed for their strengths and limitations. Despite the efficiency of CG models, challenges remain in capturing long-timescale events and atomistic-level interactions, driving the development of hybrid multiscale frameworks and AI-integrated techniques. By bridging experimental findings with in silico insights, MD simulations continue to play a pivotal role in the rational design of next-generation liposomal therapeutics. Full article

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are promising molecular biomarkers widely applied in paleoenvironmental reconstructions, including temperature and pH. However, knowledge of the microorganisms responsible for brGDGT production in marine environments remains limited, which constrains the further development and application of brGDGT-based proxies for reconstructing past marine conditions. In this study, both ‘living’ intact polar lipid-derived brGDGTs (IPL-brGDGTs) and ‘fossil’ core brGDGTs (CL-brGDGTs), together with bacterial community compositions, were analysed in multiple sediment cores collected along a nearshore-to-offshore transect in the East China Sea (ECS). The potential correlations between brGDGT distributions and bacterial community compositions at varying sediment depths across an environmental gradient were also explored. Results revealed that IPL-brGDGTs were predominantly biosynthesised in situ, whereas CL-brGDGTs reflected a mixture of marine autochthonous production and terrestrial inputs. Potential brGDGT-producing bacteria in nearshore environments were primarily composed of chemolithoautotrophic taxa (e.g., Gammaproteobacteria and Dehalococcoidia) and chemoheterotrophic taxa (e.g., Alphaproteobacteria, Bacilli, and Actinobacteria). In contrast, offshore regions were dominated by chemoheterotrophic hypoxic bacteria (e.g., Anaerolineae and Phycisphaerae) and facultatively anaerobic chemolithoautotrophic bacteria (e.g., Gammaproteobacteria and Desulfobacteria). A significant difference in bacterial community composition and IPL-brGDGT distribution was observed at a depth of 17 cm, likely due to physical disturbance in near-surface sediments, such as wave action, tidal forces, and storm events. Variance partitioning analysis (VPA) revealed that the bacterial community composition alone accounted for 14.1% of the variation in IPL-brGDGTs and 6.5% in CL-brGDGTs, further suggesting that the distribution of brGDGTs is primarily influenced by the composition of the bacterial community in the nearshore-to-offshore sedimentary ecosystems of the ECS. These findings regarding the potential biosynthesis of brGDGTs in coastal habitats advance our understanding of the microbial mechanisms that regulate brGDGT distribution in marine ecosystems. Moreover, they emphasise the importance of considering physical disturbance effects when interpreting sedimentary brGDGT records for paleoenvironmental reconstructions in marginal seas, such as the ECS. Full article

In the rapidly evolving fields of materials science, catalysis, electronics, drug delivery, and environmental remediation, the development of effective substrates for molecular deposition has become increasingly crucial. Ordered mesoporous silica materials have garnered significant attention due to their unique structural properties and exceptional potential as substrates for molecular immobilization across these diverse applications. This study compares three mesoporous silica powders: MCM-41, SBA-15, and SBA-16. A multi-technique characterization approach was employed, utilizing low- and wide-angle X-ray diffraction (XRD), nitrogen physisorption, and transmission electron microscopy (TEM) to elucidate the structure–property relationships of these materials. XRD analysis confirmed the amorphous nature of silica frameworks and revealed distinct pore symmetries: a two-dimensional hexagonal (P6mm) structure for MCM-41 and SBA-15, and three-dimensional cubic (Im$\overline{3}$m) structure for SBA-16. Nitrogen sorption measurements demonstrated significant variations in textural properties, with MCM-41 exhibiting uniform cylindrical mesopores and the highest surface area, SBA-15 displaying hierarchical meso- and microporosity confirmed by NLDFT analysis, and SBA-16 showing a complex 3D interconnected cage-like structure with broad pore size distribution. TEM imaging provided direct visualization of particle morphology and internal pore architecture, enabling estimation of lattice parameters and identification of structural gradients within individual particles. The integration of these complementary techniques proved essential for comprehensive material characterization, particularly for MCM-41, where its small particle size (45–75 nm) contributed to apparent structural inconsistencies between XRD and sorption data. This integrated analytical approach provides valuable insights into the fundamental structure–property relationships governing ordered mesoporous silica materials and demonstrates the necessity of combined characterization strategies for accurate structural determination. Full article

The glass transition temperature (T_g) was a pivotal parameter governing the thermal and mechanical properties of bismaleimide-based polyimide (BMI) resins. However, limited experimental data for BMI systems posed significant challenges for predictive modeling. To address this gap, this study introduced a hybrid modeling framework leveraging transfer learning. Specifically, a multilayer perceptron (MLP) deep neural network was pre-trained on a large-scale polymer database and subsequently fine-tuned on a small-sample BMI dataset. Complementing this approach, six interpretable machine learning algorithms—random forest, ridge regression, k-nearest neighbors, Bayesian regression, support vector regression, and extreme gradient boosting—were employed to construct transparent predictive models. SHapley Additive exPlanations (SHAP) analysis was further utilized to quantify the relative contributions of molecular descriptors to T_g. Results demonstrated that the transfer learning strategy achieved superior predictive accuracy in data-scarce scenarios compared to direct training on the BMI dataset. SHAP analysis identified charge distribution inhomogeneity, molecular topology, and molecular surface area properties as the major influences on T_g. This integrated framework not only improved the prediction performance but also provided feasible insights into molecular structure design, laying a solid foundation for the rational engineering of high-performance BMI resins. Full article

This study developed cholesteric liquid crystal broadband reflective films using zinc oxide nanoparticles (ZnO NPs) and homotriazine UV-absorbing dye (UV-1577) to enhance infrared shielding. Unlike benzotriazole-based UV absorber UV-327, which suffers from volatility and contamination, UV-1577 exhibits superior compatibility with liquid crystals, higher UV absorption efficiency, and enhanced processing stability due to its larger molecular structure. By synergizing UV-1577 with ZnO NPs, we achieved a gradient UV intensity distribution across the film thickness, inducing a pitch gradient that broadened the reflection bandwidth to 915 nm and surpassing the performance of previous systems using UV-327/ZnO NPs (<900 nm). We conducted a detailed examination of the factors influencing the reflective bandwidth. These included the UV-1577/ZnO NP ratio, the concentrations of the polymerizable monomer (RM257) and chiral dopant (R5011), along with polymerization temperature, UV irradiation intensity, and irradiation time. The resultant films demonstrated efficient ultraviolet shielding via the UV-1577/ZnO NPs collaboration and infrared shielding through the induced pitch gradient. This work presents a scalable strategy for energy-saving smart windows. Full article

Accurate species identification of small aquatic insects remains challenging due to their morphological similarities. This study addresses this issue by developing a DNA barcode reference library for the globally distributed Brillia (Diptera: Chironomidae). We analyzed cytochrome c oxidase subunit I (COI) sequences of 241 specimens belonging to 13 Brillia species from 18 countries, including 56 newly generated and 185 publicly available COI barcodes. Our integrated approach included genetic distance analysis, haplotype network construction, and ecological niche modeling. The results revealed remarkable cryptic diversity, with sequences clustering into 30 Barcode Index Numbers and 158 unique haplotypes, most being region-specific. Notably, East Asian and North American populations showed complete genetic distinctness, suggesting long-term isolation. Environmental factors, particularly temperature and precipitation gradients, were identified as key drivers of this diversification. The study also corrected several misidentifications in existing databases. These findings significantly advance our understanding of Brillia diversity and provide a reliable molecular tool for freshwater ecosystem monitoring, with important implications for biodiversity conservation and environmental assessment. Full article

Dewatering and gas production are applied on a large scale in shale gas development. The fundamental mechanisms of water flow in shale nanoporous media are essential for the development of shale oil and gas resources. In this work, we use molecular dynamic simulations to investigate water flow in two different quartz surface ($(10\overline{1}0)\text{-}\alpha$ and $(10\overline{1}0)\text{-}\beta$) nanopores. Results show that the $(10\overline{1}0)\text{-}\beta$ surface exhibits stronger water molecule structuring with a structure arranged in two layers and higher first-layer adsorption density (2.44 g/cm³) compared to the ($(10\overline{1}0)\text{-}\alpha$ surface (1.68 g/cm³). The flow flux under the $(10\overline{1}0)\text{-}\alpha$ surface is approximately 1.2 times higher than that under the $(10\overline{1}0)\text{-}\beta$ surface across various pressure gradients. We developed a theoretical model dividing the pore space into non-flowing, adsorbed, and bulk water regions, with critical thicknesses of 0.14 nm and 0.27 nm for the non-flowing region, and 0.15 nm for the adsorbed region in both surfaces. This model effectively predicts velocity distributions and volumetric flow rates with errors generally below 5%. Our findings provide insights into water transport mechanisms in shale inorganic nanopores and offer practical guidance for numerical simulation of shale gas production through dewatering operations. Full article

Birds are hosts to a diverse assemblage of vector-transmitted haemosporidian parasites. However, the true genetic diversity and many host–parasite interactions are still unknown, particularly in under-represented species groups such as wild doves and pigeons (Columbiformes). In this study, we examined the prevalence and lineage diversity of haemosporidian genera Plasmodium, Leucocytozoon, and Haemoproteus in three species of wild columbids, sampled in Germany. Examinations were performed by applying molecular methods (nested PCR and one-step multiplex PCR) and blood smear examination, and their respective advantages and disadvantages are discussed. In the case of the European Turtle Dove Streptopelia turtur, samples were collected along a west–east gradient throughout Germany, covering migratory birds from the Western and Central-Eastern flyway of this species. Although no infection was detected in the Stock Dove Columba oenas samples, 53% of Turtle Dove and 86% of Common Woodpigeon Columba palumbus harbored a parasite of at least one haemosporidian genus, revealing previously unknown lineage–host interactions. We were not able to demonstrate a correlation between infection status (presence or absence of infection based on PCR results) and parasitemia with condition based on the heterophil to lymphocyte ratio (H/L ratio). Neither lineage occurrence nor prevalence of infection followed any geographically specific patterns. Thus, haemosporidian lineages found in Turtle Doves could not be used as a marker of geographic origin that would allow the tracking of their nonbreeding distribution. Full article

Hopanoids are a series of important lipid biomarkers in the bacterial cellular membranes that are found ubiquitously in different spatial and temporal environments. Squalene-hopane cyclase, a key and prerequisite molecular component of the hopanoid biosynthesis pathway, is encoded by the sqhC gene. To investigate the composition, niche, and distribution of microbial sqhC-containing communities, we analyzed hopanoid producer data and environmental parameters across different ecosystems on the basis of sequencing reads of peat samples from increasing gradient depths across peatland profile C in the Dajiuhu Peatland, as well as data collected from available published papers. The results indicated that the acidic Dajiuhu Peatland harbored mainly Acidobacteria (59.16%) among its sqhC-containing groups. The main composition of hopanoid producers in the peatland was different from that in other ecosystems, with Alphaproteobacteria found in soil (37.78%), cave (48.21%), hypersaline lagoon (34.04%), and marine (32.59%) ecosystems; Betaproteobacteria, Gammaproteobacteria, and Deltaproteobacteria found in reef (100%), acid mine drainage (55.00%), and estuary, mangrove, and harbor (39.66%) ecosystems; and an unknown cluster found in freshwater (29.43%) and hot spring (89.58%) ecosystems. Compared with other phyla or sub-phyla, Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria were the most widespread, occurring in eight ecosystems. Peatland was significantly separated from the other nine ecosystem modules in the occurrence network, and the marine ecosystem had the greatest impact on the eco-network of sqhC microbes. An RDA indicated that pH, DO, salinity, and TOC had significant impacts on sqhC-containing microbial communities across the different ecosystems. Our results will be helpful to understanding the diversity, composition, and distribution of the sqhC community and its response to multiple environmental factors across different ecosystems. Full article

Drug delivery to solid tumors is challenged by multiple physiological barriers arising from the tumor microenvironment, including dense extracellular matrix, cellular heterogeneity, hypoxic gradients, and elevated interstitial fluid pressure. These features hinder the uniform distribution and accumulation of therapeutics, reducing treatment efficacy. Despite their widespread use, conventional two-dimensional monolayer cultures fail to reproduce these complexities, contributing to the poor translational predictability of many preclinical candidates. Three-dimensional multicellular tumor spheroids have emerged as more representative in vitro models that capture essential features of tumor architecture, stromal interactions, and microenvironmental resistance mechanisms. Spheroids exhibit spatially organized regions of proliferation, quiescence, and hypoxia, and can incorporate non-tumor cells to mimic tumor–stroma crosstalk. Advances in spheroid analysis now enable detailed evaluation of drug penetration, cellular migration, cytotoxic response, and molecular gradients using techniques such as optical and confocal imaging, large-particle flow cytometry, biochemical viability assays, and microfluidic integration. By combining physiological relevance with analytical accessibility, spheroid models support mechanistic studies of drug transport and efficacy under tumor-like conditions. Their adoption into routine preclinical workflows has the potential to improve translational accuracy while reducing reliance on animal models. Full article

Although heavy oil remains a crucial energy source, its high viscosity makes its utilization challenging. We have performed an interpretable analysis of the relationship between the molecular structure of digital oil and its viscosity using molecular dynamics simulations combined with machine learning. In this study, we developed three “digital oils” to represent light, medium, and heavy oils in consideration of their composition and molecular structure. Using molecular dynamics (MD) simulations, we calculated the density, self-diffusion coefficient, and viscosity of these digital oils at various temperatures (323–453 K). The accuracy of the simulation results was demonstrated by their good fit to the experimental data. We further explored the correlation between interaction energy and viscosity. As interaction energy increased, molecular attraction strengthened, resulting in greater friction between molecules and a higher viscosity of the digital oil. Cluster analysis revealed that, compared with the other two oils, the heavy oil contained rod-shaped molecular aggregates in greater quantity and larger clusters. Additionally, we computed the radial distribution functions of the SARA (saturates, aromatics, resins, and asphaltenes) components; among molecular pairs, aromatics and resins showed the largest interaction energy and were the most tightly bound, contributing to increased viscosity. To more effectively predict the viscosity of digital oils, we integrated four machine learning (ML) techniques: linear regression, random forest, extra trees, and gradient boosting. Post-hoc analysis coupled with SHapley Additive exPlanations (SHAP) was applied to interpret how macroscopic and microscopic features influence the viscosity and to identify the contributions of individual molecules. This work presents a novel and efficient method for estimating the viscosity of digital oils by combining MD simulations with ML approaches, offering a valuable tool for quick and cost-effective analysis. Full article

In a hypergravity environment, the complex stress conditions and the change in gravity field intensity will significantly affect the interaction force inside solid- and liquid-phase materials. In particular, the driving force for the relative motion of the phase material, the interphase contact interaction, and the stress gradient are enhanced, which creates a nonlinear effect on the movement mode of the phase material, resulting in a change in the material’s behavior. These changes include increased stress and contact interactions; accelerated phase separation; changes in stress distribution; shear force and phase interface renewal; enhanced interphase mass transfer and molecular mixing; and increased volume mass transfer and heat transfer coefficients. These phenomena have significant effects on the synthesis, structural evolution, and properties of materials in different phases. In this paper, the basic concepts of hypergravity and the general rules of the effects of hypergravity on the synthesis, microstructure evolution, and properties of materials are reviewed. Based on the development of hypergravity equipment and characterization methods, this review is expected to broaden the theoretical framework of material synthesis and mechanical property control under hypergravity. It provides theoretical reference for the development of high-performance materials under extreme conditions, as well as new insights and methods for research and application in related fields. Full article

The deterioration of stone heritage in urban environments is mainly the product of sources of air pollution like vehicular traffic and domestic heating. The results of these phenomena usually manifest as acid rain and particulate patinas, acting on the surface of stone monuments to form the so-called “black crusts”, a typical stone degradation product, mainly composed of gypsum. The aims of this study were to investigate the extent of these phenomena on the decorative apparatus of the frontal façade of Gesù Nuovo Church, in the historical centre of Naples (Italy). Preliminary diagnostics consisted of XRD and FTIR to analyse the composition of stone materials and inquire about previous restorations. The chemical characterization of black crusts was performed, using a diverse array of techniques, to highlight how different compounds are distributed along a vertical gradient and considering the proximity of specific sources of pollution (vehicle engine ignition, incense combustion, domestic heating products). Finally, molecular biology techniques were employed to identify the organisms which typically dwell in this formation and speculate about their contribution to the degradation of stone. Full article

Fungi are one of the major components of the eukaryotic microbial community in marine ecosystems, playing a significant role in organic matter cycling and food web dynamics. However, the diversity and roles of fungi in marine sediments remain poorly documented. To elucidate the diversity and spatial distribution of fungal communities in the marine sediments of an estuary–coast continuum across three distinct salinity regions in Zhanjiang Bay, China, the variations in fungal diversity, abundance, community structure, and distribution in the sediments were investigated through the application of high-throughput amplicon sequencing using the internal transcribed spacer (ITS) primers. Additionally, the FUNGuild database was employed to assess the potential functional traits of fungi. A total of 1242 ASV sequences, affiliated to 144 genera and five phyla, were identified. Ascomycota (68.97%) and Basidiomycota (6.41%) were the dominant fungal groups, together accounting for 75.38% of the total relative abundance of the fungal community. Significant differences were observed in the α-diversity indices (Shannon index and richness) and β-diversity of fungal communities across the three distinct salinity regions. The fungal molecular network exhibited primarily positive species interactions, with notable structural differences across salinity gradients. The low-salinity group had a large network with high modularity; the medium-salinity group a small, simple network with high centralization, and the high-salinity group a compact, moderately complex network. Symbiotrophs, saprotrophs, and pathotrophs, being the three trophic types with the highest proportions, were estimated based on ITS. A redundancy analysis (RDA) indicated that salinity was the primary factor influencing the distribution of Ascomycota communities, while the distributions of Basidiomycota, Chytridiomycota, Mucoromycota, and Rozellomycota were more strongly affected by environmental factors such as chlorophyll a, chemical oxygen demand (COD), pH, and temperature. Our work provides new scientific data on the diversity, composition, and distribution of fungal communities in Zhanjiang Bay, which helps to understand the biodiversity of fungi in the estuary–coast ecosystems. Full article

In this study, molecular dynamics (MD) simulations were employed to compare the effects of different solidification conditions on the solidification behaviour, stress distribution, and degree of crystallization of iron. The results indicate significant differences in nucleation and microstructural evolution between the two solidification methods. In the homogeneous temperature field, the solidification of iron is characterized by instantaneous nucleation. The BCC phase surged at 1431 K followed by the phenomenon of latent heat of crystallization. As the temperature continued to decrease, the percentage of the BCC phase continued to increase steadily. Eventually, the atoms aggregated to form a crystal nucleus and grow outward to form polycrystalline structures. During gradient solidification, continuous nucleation of iron leads to a slow increase in the BCC phase. From the initial stage of solidification, the solid–liquid interface moves in the direction of higher temperature and is accompanied by a higher stress distribution. Furthermore, increasing the temperature gradient, particularly the cooling rate, accelerates the transformation efficiency of iron in the gradient solidification process. In addition, increasing the cooling rate or temperature gradient reduces the residual stress and crystallinity of the solidified microstructure. It is worth noting that an increased temperature gradient or cooling rate will produce higher residual stress and uneven microstructure in the boundary region. This study provides an atomic-level understanding of the improvement in the solidification performance of iron. Full article