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Molecular dynamics study of pervaporation of water–methanol mixtures through graphene nanotubes
By means of extensive molecular dynamics simulations, we explore the concentration enhancement of alcohol from water/methanol solutions induced by the preferential flow of alcohols through single-layer carbon nanotubes of various widths. These nanotubes connect a reservoir containing an aqueous methanol solution with an empty reservoir. Simulations are performed at room temperature and at 398 K. The non-equilibrium stages of these simulations can represent a rough model of the pervaporation process through a hydrophobic membrane channel. Once equilibrium is reached in the receiving reservoir, we observe a substantial increase in alcohol concentration with respect to the value that would correspond to the gas phase in an unconfined vapor–liquid equilibrium at the same temperature. Alcohol yields are particularly high when starting from dilute alcohol solutions and for nanotubes 2–3 times wider than the average adsorbate molecular size.
Thermal neutron cross sections of aluminum and magnesium fluorides and quantum kinetic energy of fluorine
We provide the modeling and experimental validation of thermal neutron cross sections, including scattering and absorption contributions, of aluminum fluoride and magnesium fluoride, as well as MgF2–AlF3 and AlF3–LiF mixtures, in the neutron energy range between 0.6 meV and 1 keV. The neutron scattering properties have been investigated as a function of temperature, between 10 and 370 K, and sample microscopic structure, focusing in particular on differences between powder and sintered samples. Concurrent measurements of neutron transmission, neutron diffraction, neutron resonance capture analysis, and neutron Compton scattering provide a comprehensive picture of scattering and transport properties of these materials. We provide crystallographic information at the atomic scale, the effect of sintering on the grain size at the nanometer scale, the related occurrence of small angle scattering, and the impact of fluorine zero-point nuclear energy at different temperatures, requiring the use of an effective temperature to model its scattering within the impulse approximation. Finally, combining this self-consistent experimental information, we discuss the application of the uncertainty principle to the fluorine single-particle potential.
Hierarchical dynamics of hydronium ions in polymer electrolyte membranes revealed by all-atom molecular dynamics simulations
Understanding the transport dynamics of hydronium ions (H3O+) in polymer electrolyte membranes is critical for improving the performance of polymer electrolyte fuel cells. In this study, we performed all-atom molecular dynamics simulations of hydrated Nafion to investigate the relationship between the H3O+ diffusion mechanisms and the membrane morphology at various water uptakes (λ = 6, 10, and 14). To capture the intrinsic heterogeneity of the water channels and the long-time dynamics, large-scale simulations were required. Therefore, we used a classical H3O+ model that excludes the Grotthuss mechanism. Free energy maps revealed that H3O+ are strongly trapped near SO3− groups, while water molecules (H2O) exhibit broader free energy wells. Conduction path and structural factor analyses indicated that the water channels in Nafion form interconnected tubular networks, whose tube diameter increases with hydration. The simulated structure factors quantitatively reproduced the experimentally observed correlation length of the water channels. Mean square displacement and probability distributions revealed hierarchical dynamics modes for both H3O+ and H2O. The suggested transport processes were the following three modes with different time/spatial scales: (i) localized binding to the sulfonate groups on Nafion, (ii) confined diffusion within the water channels, and (iii) normal diffusion along the water channels. From these findings, the proton diffusion modes in Nafion are highly localized and governed by multiscale mechanisms owing to the membrane morphology and hydration. These results provide a detailed, molecular-level understanding of the proton transport in perfluorosulfonic acid membranes, and they provide valuable insight into optimizing materials for fuel cell applications.
Segregation of lipids to cellular poles
A simple mechanism is suggested for the segregation of lipid domains to the poles of bacterial cells. The cell is modeled as a capsule (or spherocylinder), and the lipids are particles that are confined to the surface of the capsule. The lipid particles interact via a short-ranged Lennard-Jones interaction, and single particles are not curvature sensing in any way. When the temperature is decreased, liquid clusters nucleate on the surface. These domains then segregate to the spherical caps of the capsule. The segregation is scale invariant, i.e., it happens whether the capsule is large, of the order or micrometers, or of the order of tens of nanometers, for particles of size ∼1 nm. We propose that the slight energetic preference for the curved regions between next nearest neighbors is sufficient to promote a large scale segregation. The phenomenon is general, i.e., as long as lipids make domains, e.g., rafts, they are predicted to be preferentially found at the poles.
Optical and excited state properties of Au30(S-Adm)18 and Au38S2(S-Adm)20 nanoclusters: Accuracy from TD-DFT and TDDFT + TB approaches
Gold nanoclusters display intriguing photoluminescence properties that are vital for advancements in biomedical imaging and optoelectronic technologies. However, the fundamental mechanisms underlying their emission remain elusive, largely due to the complex relationship of structural and electronic factors intrinsic to the cluster architecture. Here, we investigate the role of photo-induced geometrical and electronic variations in the resulting excited and emissive states for the representative medium-sized atomically precise ligand-protected Au30(S-Adm)18 and Au38S2(S-Adm)20 clusters, by using time-dependent density functional theory (TD-DFT) and TDDFT + TB methods. Our results indicate that approximate methods, integrating a comprehensive DFT ground state description with the tight binding approximation in linear response calculations, serve as a cost-effective alternative for precise predictions of size and ligand effects on the ground and excited states, providing a foundation for extending such analyses to larger systems. The results reveal a strong dependence of optical behavior on cluster size and ligand environment, capturing key features such as Stokes shifts and singlet–triplet energy gaps (∆EST = 0.04–0.16 eV). Both clusters demonstrate efficient reverse intersystem crossing facilitated by thermally activated delayed fluorescence (TADF), attributed to the small difference between potential minima of S1 and T1 states. Furthermore, silver doping is shown to modulate excited-state lifetimes without altering the emission origin, offering precise control over photophysical characteristics. These findings are consistent with experimental observations and provide a rational strategy for the design of nanoclusters with tailored luminescence properties, relevant to applications in sensing, catalysis, and optoelectronic devices.
Two-body fragmentation dynamics of cyclopropane dication following 5.8 MeV/u Ni19+ impact
The dissociation of cyclopropane dication (c-C3H62+) induced by 5.8 MeV/u Ni19+ was investigated using cold target recoil ion momentum spectroscopy. Five primary two-body dissociation channels were identified, comprising three C–C bond cleavage channels and two C–H bond cleavage channels. Analysis of the measured kinetic energy release distributions, supported by ab initio quantum chemical calculations, offers comprehensive insights into the underlying fragmentation dynamics. The results revealed that excited-state fragmentation dynamics and hydrogen migration processes play a dominant role, distinctly differing from the dissociation behavior observed in other small hydrocarbon molecules.
Precise gene editing of pathogenic Lamin A mutations corrects cardiac disease
Mutations in the Lamin A ( LMNA ) gene, which encodes the Lamin A and C proteins, cause severe human diseases collectively known as laminopathies. These conditions are often devastating and lack effective therapies. In this study, we developed precise base editing (BE) strategies targeting the human LMNA gene variants L35P and R249Q, which cause congenital muscular dystrophy (CMD) and dilated cardiomyopathy with conduction defects (DCM-CD), respectively. Induced pluripotent stem cell–derived cardiomyocytes (iPSC-CMs) carrying the R249Q mutation displayed nuclear aberrations, DNA damage, and abnormal Ca 2+ transients. Similarly, L35P iPSC-CMs exhibited abnormal contraction, DNA damage, and reduced Lamin A/C protein expression. We also generated “humanized” mouse models carrying these pathogenic human mutations. R249Q homozygous mice exhibited cardiac conduction abnormalities, cardiac arrhythmias, and premature death. Mice with the homozygous L35P mutation displayed severe muscle-wasting and reduced lifespan, while heterozygous L35P mice displayed DCM. We developed an adenine base editing (ABE) approach for correcting the R249Q mutation and a cytosine base editing (CBE) strategy for the L35P variant. Precise correction of these mutations in iPSC-CMs successfully rescued all of the in vitro abnormalities. Furthermore, delivery of the BE components using adeno-associated virus prevented the pathological phenotypes and extended longevity of mice carrying the LMNA L35P and the R249Q mutations. These results demonstrate the efficacy of ABE and CBE in correcting pathogenic LMNA mutations that cause cardiac disease, highlighting BE as a promising therapeutic approach for human laminopathies.
Smartphone use in a large US adult population: Temporal associations between objective measures of usage and mental well-being
Smartphones are essential tools in daily life yet concerns persist about their potential effects on mental health and well-being. Research on these relationships often relies on subjective measures of smartphone use, which can lack validity. These studies are also often limited by small, homogenous samples and the inability to differentiate between types of smartphone activities (e.g., social media vs. other applications). In this study, we analyzed 250,000 d of smartphone usage data from 10,099 adults, using objective measures of smartphone usage collected over 4 wk. We examined normative usage patterns and the temporal relationships between smartphone use and mood. Our findings reveal weak or null associations between smartphone use and mood, both cross-sectionally and longitudinally, and at both individual and group levels. Demographic factors, such as age and gender, were stronger predictors of mood than smartphone use. Statistically significant results that were observed showed that younger adults demonstrated a stronger association between social media use and lower mood cross-sectionally, but not longitudinally. Non-social app use was linked to lower mood in between-subjects analyses, but better mood in within-subjects analyses. However, all effects were small (or null), indicating minimal practical significance. These findings suggest that smartphone use has a limited or negligible impact on mood and well-being over a 4-wk period and do not support claims of a strong or causal relationship between smartphone use and mental well-being across this timeframe.
Climate change impacts on tropical cyclone–induced power outage risk: Sociodemographic differences in outage burdens
This research investigates the projected risks of future climate trends on tropical cyclone–induced power outages in the Gulf and Atlantic coast of the United States, focusing on the disproportionate impacts on vulnerable populations and the economic burdens associated with such events. Our methodology integrates four well-documented models to estimate changes in power outage rates, sociodemographic inequities, and economic costs due to tropical cyclone projections. Synthetic tropical cyclones were generated using data from seven global climate models (GCMs), used to compare power outage risks at the census tract level along two periods: hindcast (1995–2014) and late-century (2071–2100) using the SSP5-8.5 scenario. The late-century results from each model were scaled to align with a global warming scenario of 3 °C. We evaluated the uncertainty of these projections by considering the agreement among the GCMs outage projections. Results highlight a significant increase in power outage risks and high agreement in northern Florida, Georgia, the mid-Atlantic, and the North Atlantic coast. Distributional impact analyses indicate higher outage risks for Hispanic, non-White, and low-income populations, while economic projections show annual costs rising from $6.2 billion in the hindcast to over $11 billion for the 3 °C scenario. The findings highlight the need for adaptive strategies and equitable resource allocation to mitigate these growing risks due to future climate projections.
Performance and statistical characteristics of ferronickel slag geopolymer concrete
Abstract This study investigates the feasibility of substituting river sand with ferronickel slag (FNS) in geopolymer concrete (GC) through comprehensive experiments on mixtures with varying water-to-binder ratios, aggregate-to-binder ratios, and FNS replacement levels. Key properties evaluated include workability (16–30 cm slump, classified as high-fluidity concrete), compressive strength, splitting tensile strength, microstructure, and compressive strength statistics. Results indicate that increasing FNS replacement reduces slump, while higher aggregate-to-binder ratios or lower water-to-binder ratios further decrease workability. FNS-GC exhibits significant early-age strength development, achieving 72% and 94% of standard 28-day compressive strength at 3 and 14 days, respectively, with peak compressive strength observed at 33% FNS content. Similarly, splitting tensile strength (3.2- 4.4 MPa) reaches 75% and 93% of its 28-day value by 3 and 14 days, peaking at 33% FNS replacement and showing positive correlation with compressive strength. Statistically, compressive strength displays considerable variability (coefficient of variation: 0.07–0.17), maximized at 33% FNS replacement. The strength distribution aligns with normal, log-normal, and Weibull functions, with the Weibull distribution providing the optimal fit for characterizing compressive strength behavior.
Soft tissue effects of aesthetic and conventional twin block appliances in the treatment of skeletal class II malocclusion: a randomized controlled trial
Feasibility of intratracheal tracheostomy sealing: anatomical adaptation and biocompatibility of a second-generation prototype in cadaveric and porcine models
Abstract After decannulation, air leak through the tracheostoma can impair cough and voice. We evaluated a second-generation temporary intratracheal sealing disc that seals the stoma from within. Two preclinical sub-studies were performed: Sub-study 1 cadaveric deployment to assess anatomical fit, seal integrity, and removability; and Sub-study 2, a 7-day porcine implantation with anatomical compatibility and blinded histology. In cadavers, the sealing disc maintained airway patency and sealed the stoma, withstanding peak intratracheal pressures up to 180 cmH 2 O; removal through a near-healed opening required approximately 5 N traction. In the porcine model, no airway obstruction or clinical distress was observed, and histology (Phase Two) showed no excess inflammation or foreign body reaction versus sham. The sealing disc provided intraluminal sealing of the tracheostomy with feasible insertion and removal characteristics and a favorable short-term tissue response in preclinical models. These findings support clinical feasibility testing of this approach to maintain an intact airway dynamic during early post-decannulation healing.
Multistage prediction approach of EVs charging performance in smart transportation systems by deep learning technique
Abstract Electric Vehicles (EVs) are increasingly recognized as a fundamental component of intelligent transportation systems within smart city frameworks. Therefore, several studies in recent decades have been trying to improve the performance of EVs to maximize the benefits from their connection to the network. Machine Learning (ML) and data-driven methods are used for analyzing EV charging behavior to maintain significant improvements in the prediction and scheduling fields. Although many of these studies have relied on historical charging data to predict the EVs’ State of Charge (SoC) and Charging Available Time (CAT), influential features have often been overlooked. These features are represented in real-time distance, road characteristics (road type, traffic pattern, and events data), and weather data. This study proposes a novel multistage approach, based on a Feedforward Deep Neural Network (FDNN) that combines historical charging data with these influential features to predict both SoC and CAT. The proposed approach outperforms existing literature with SMAPE scores of 0.00044, 0.00018 and 0.00014, 0.00012 for initial, required SoC and CAT predictions, respectively. Through comparative analyses with prior studies on the same dataset, this research highlights substantial improvements in predictive accuracy. It underscores the significance of integrating influential features for the precise prediction of EV charging behaviors within smart transportation systems.
Highlighting zoonotic importance of synanthropic fleas through microbiome analysis
Feasibility of automatic screw planning via transformer-based shape completion from RGB-D imaging
Heterosis and combining ability analysis of Phytophthora capsici induced root rot resistance and horticultural traits in Capsicum annuum
RCA 1-binding glycans as a marker of Batrachochytrium salamandrivorans infection intensity at early stages of pathogenesis
Abstract The fungal skin disease chytridiomycosis threatens global amphibian biodiversity. Among the two causative pathogens, Batrachochytrium dendrobatidis and B . salamandrivorans , the latter has decimated several European salamander populations. However, responses to exposure vary notably across species and individuals, suggesting that understanding the mechanisms behind these variations could enable successful mitigation in surviving populations. A recent study reported that the concentration of epidermal galactose predicts B . salamandrivorans susceptibility at the species level. Here, we used comparative lectin histochemistry with nine lectins to observe lectin staining brightness in the skin and examine its association with the susceptibility of four urodelan species with differential responses to B . salamandrivorans . Subsequently, we exposed Pleurodeles waltl individuals to B . salamandrivorans to assess whether this correlation extends to the individual level. In our study, the degree of labelling with the lectin, Ricinus communis agglutinin I (RCA 1), emerged as the distinctive marker of susceptibility in that resistant species showed minimal RCA 1 binding, while susceptible species exhibited higher levels. Results of the infection trial further demonstrated that an increase in staining brightness correlates with steeper infection slopes at initial stages of infection. This demonstrates RCA 1-binding glycans as a biomarker for individual susceptibility and presents the basis for marker-assisted selection in P. waltl .