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A sum-frequency generation vibrational spectroscopy studies on buried liquid/liquid interfaces of CCl4/[C<i>n</i>mim][TFSA] (<i>n</i> = 4 and 8) hydrophobic ionic liquids

The liquid/liquid interfaces of room-temperature ionic liquids (RTILs) play a pivotal role in chemical reactions owing to their characteristic microscopic structure, yet the structure of hydrophobic liquid/RTIL interfaces remains unclear. We studied the structure at the liquid/liquid interfaces of carbon tetrachloride (CCl4) and 1-alkyl-3-methylimidazolium bis(trifluoromethanesulfonyl)amide ([Cnmim][TFSA]; n = 4 and 8) RTILs using infrared–visible sum frequency generation (SFG) vibrational spectroscopy. A comparison of the SFG spectra of the CCl4/RTIL and air/RTIL interfaces revealed that the solvation of the alkyl chains of the [Cnmim]+ cations by CCl4 reduces the number of gauche defects in the alkyl chain and the interface number density of the cation at the CCl4 interface. The orientational change of the [TFSA]− anion and concomitant increase in the area it occupies at the CCl4 interface was observed to be greater than that at the air interface. This is accompanied by the expansion of the space among the alkyl chains of the cations to be solvated by CCl4. The structural change of the CCl4 interface from the air interface can be attributed to the solvophilic effect of CCl4 on the alkyl chains of the cations at the CCl4/[Cnmim][TFSA] interface. This is in contrast with the solvophobic effect of CCl4 on the Langmuir film at the CCl4/water interface. This phenomenon is caused by the loosely packed alkyl chains of the cations at the RTIL surface and the flexible anion–cation arrangement owing to the weak basicity and acidity of the ions in the RTILs.

Antitumor immunostimulatory effect via cell-killing action of a novel extracorporeal blood circulating photodynamic therapy system using 5-aminolevulinic acid

Magic-NOVEL: Suppressing electron–electron coupling effects in pulsed DNP

Pulsed dynamic nuclear polarization (DNP) enhances the nuclear magnetic resonance sensitivity by coherently transferring electron spin polarization to dipolar coupled nuclear spins. Recently, many new pulsed DNP techniques such as NOVEL, TOP, XiX, TPPM, and BEAM have been introduced. Despite significant progress, numerous challenges remain unsolved. The electron–electron (e–e) interactions in these sequences can severely disrupt the efficiency of electron–nuclear (e–n) polarization transfer. In order to tackle this issue, we propose the magic-NOVEL DNP method, utilizing Lee–Goldburg decoupling to counteract e–e coupling effects. Our theoretical analysis and quantum mechanical simulations reveal that magic-NOVEL significantly improves the transfer efficiency of DNP, even at shorter e–e distances. This method offers a new perspective for advancing pulsed DNP techniques in systems with dense electron spin baths. Furthermore, we demonstrate the effectiveness of phase-modulated Lee–Goldburg sequences in improving pulsed DNP transfer.

An experimental study on water purification performance of modified volcanic rock ecological concrete

Systematic analysis of biomolecular conformational ensembles with PENSA

Atomic-level simulations are widely used to study biomolecules and their dynamics. A common goal in such studies is to compare simulations of a molecular system under several conditions—for example, with various mutations or bound ligands—in order to identify differences between the molecular conformations adopted under these conditions. However, the large amount of data produced by simulations of ever larger and more complex systems often renders it difficult to identify the structural features that are relevant to a particular biochemical phenomenon. We present a flexible software package named Python ENSemble Analysis (PENSA) that enables a comprehensive and thorough investigation into biomolecular conformational ensembles. It provides featurization and feature transformations that allow for a complete representation of biomolecules such as proteins and nucleic acids, including water and ion binding sites, thus avoiding the bias that would come with manual feature selection. PENSA implements methods to systematically compare the distributions of molecular features across ensembles to find the significant differences between them and identify regions of interest. It also includes a novel approach to quantify the state-specific information between two regions of a biomolecule, which allows, for example, tracing information flow to identify allosteric pathways. PENSA also comes with convenient tools for loading data and visualizing results, making them quick to process and easy to interpret. PENSA is an open-source Python library maintained at https://github.com/drorlab/pensa along with an example workflow and a tutorial. We demonstrate its usefulness in real-world examples by showing how it helps us determine molecular mechanisms efficiently.

Unveiling the photocatalytic and antimicrobial activities of star–shaped gold nanoparticles under visible spectrum

Abstract This study reports on the facile development of star-shaped gold nanoparticles via seed-mediated growth protocol. Gold nanostars (AuNSTs) demonstrated average particle size of 48 nm using transmission electron microscopy (TEM). Chemical composition of AuNSTs was verifired using energy dispersive X-ray spectroscopy (EDX) mapping. AuNSTs demonstrated high optical response under visible spectrum, with maximum absorption at 685 nm, using UV-Vis spectroscopy. Therefore AuNSTs could be involoved into photocatalytic reaction under visible spectrum. AuNSTs demonstrated superior performance in degradation of rhodamine B dye (RB), and disinfection of some pathogenic bacteria. AuNSTs offered enhanced removal efficiency against rhodamine B dye (82.0 ± 0.35% in 135 min) under visible irradiation. Remarkably, under proper conditions of pH = 9, approximately 94 ± 0.55% of a 10 ppm RB solution was effectively photodegraded after 135 min; this could be ascribed to the strong electrostatic attraction between negatively charged AuNSTs surface and positive RB contaminant. This superior photocatalytic activity of AuNSTs could be correlated to high interfacial charge transfer efficiency for Au, and enhanced charge pair separation under visible spectrum. Additionally, AuNSTs exhibited potential antibacterial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus). AuNSTs demonstrated substantial antibacterial activity via disk diffusion and microbroth dilution tests with zones of inhibition and minimum inhibitory concentrations (MIC) for E. coli (20.0 ± 0.54 mm, 1.25 µg/ml) and S. aureus (23.0 ± 0.35 mm, 0.625 µg/ml), respectively. In conclusion, AuNSTs demonstrated efficient dye removal capabilities along with significant antimicrobial activity against gram-positive and gram-negative bacterial strains.

Dimensional confinement and superdiffusive rotational motion of uniaxial colloids in the presence of cylindrical obstacles

In biological systems such as cells, the macromolecules, which are anisotropic particles, diffuse in a crowded medium. In the present work, we have studied the diffusion of spheroidal particles diffusing between cylindrical obstacles by varying the density of the obstacles as well as the spheroidal particles. Analytical calculation of the free energy showed that the orientational vector of a single oblate particle will be aligned perpendicular, and a prolate particle will be aligned parallel to the symmetry axis of the cylindrical obstacles in equilibrium. The nematic transition of the system with and without obstacles remained the same, but in the case of obstacles, the nematic vector of the spheroid system always remained parallel to the cylindrical axis. The component of the translational diffusion coefficient of the spheroidal particle perpendicular to the axis of the cylinder is calculated for the isotropic system, which agrees with analytical calculation. When the cylinders overlap such that the spheroidal particles can only diffuse along the direction parallel to the axis of the cylinder, we can observe dimensional confinement. This was observed by the discontinuous fall of the diffusion coefficient, when plotted against the chemical potential both for a single particle and for a finite volume fraction. The rotational diffusion coefficient quickly reached the bulk value as the distance between the obstacles increased in the isotropic phase. In the nematic phase, the rotational motion of the spheroid should be arrested. We observed that even though the entire system remained in the nematic phase, the oblate particle close to the cylinder underwent a flipping motion. The consequence is that when the rotational mean squared displacement was calculated, it showed a super-diffusive behavior even though the orientational self-correlation function never relaxed to zero, showing this to be a very local effect.

Strategies for enhancing deep video encoding efficiency using the Convolutional Neural Network in a hyperautomation mechanism

Trajectory analysis of anomalous dynamics in optical lattice

We apply the trajectory formulation to analyze the anomalous dynamics of cold atoms in an optical lattice. The phase space probability density function of cold atoms, their dynamics, and the mechanism of dynamic evolution from an initial Gaussian distribution to a power-law distribution are analyzed. The results of the trajectory formulation are in good agreement with the previously reported experimental results for the exponent of position variance for a long time and the position–momentum correlation. The self-similar natures of trajectories in phase space are found for Lévy distributions. Our results unify the raw moments that can be expressed as the summation of a number of independent, identically distributed variables and the anomalous dynamics, which holds promise for an intuitive interpretation anomalous behavior and their kinetic mechanisms from initial Gaussian to anomalous distributions for a long time.

Therapeutic experience and key techniques of tubeless percutaneous nephrolithotomy

Out-of-phase ELDOR spectroscopy: A precise tool for investigating structure and dynamics of charge-transfer states in organic photovoltaic blends

We developed a technique allowing the direct observation of photoinduced charge-transfer states (CTSs)—the weakly coupled electron–hole pairs preceding the completely separated charges in organic photovoltaic (OPV) blends. Quadrature detection of the electron spin echo (ESE) signal enables the observation of an out-of-phase ESE signal of CTS. The out-of-phase Electron–Electron Double Resonance (ELDOR) allows measuring electron–hole distance distributions within CTS and its temporal evolution in the microsecond range. The technique was applied to OPV bulk heterojunction blends of different donor polymers, including the benchmark polymer P3HT and the high-performance polymer PCDTBT, with the fullerene PC61BM acceptor. The corresponding electron–hole distance distributions were obtained using the Tikhonov regularization. It was found that not only the dipolar interaction but also the exchange interaction contributes to the formation of the out-of-phase ELDOR signal. By varying the delay time after photoexcitation, we observed CTSs at different stages of charge separation. The initial distribution of the electron–hole distances for different blends correlates with their photoelectric conversion efficiency, with shorter average thermalization distances found for the blends of PC61BM with the less efficient regiorandom polymer P3HT. Spin-selective recombination of the CTS was unambiguously demonstrated for the blend of regioregular P3HT with PC61BM. It produces characteristic features in the out-of-phase ELDOR trace for small “dipolar” evolution times. These data allow us to estimate the CTS recombination rate for a certain distance between the electron and the hole within the CTS. The proposed method can be used to probe CTS in a variety of OPV active layer materials.

Ensemble learning based sustainable approach to rebuilding metal structures prediction

The theory of Barlow packings: Basic properties and cohesive energies from exact lattice summations within the sticky hard-sphere model

The theory of periodic Barlow multi-lattices (X1X2…XN)∞ with Xi ∈ {A, B, C} and Xi ≠ Xi+1 of stacked two-dimensional hexagonal close-packed layers is presented and used to derive exact lattice sum expressions in terms of fast converging Bessel function expansions for inverse power potentials. We describe in detail the mathematical properties of Barlow sphere packings and demonstrate that only two basic lattice sums are required to describe all periodic packings. For the sticky hard-sphere model with an attractive inverse power law potential, we find a linear correlation between the cohesive energies of different Barlow packings and the face-centered cubic packing fraction. We introduce an efficient algorithm for enumerating the unique periodic Barlow sequences for any given period N. The theory and lattice sums introduced here pave the way for the future treatment of Barlow multi-lattices.

Association between behavioral and sociodemographic factors and high subjective health among adolescents: a nationwide representative study in South Korea

Active learning of molecular data for task-specific objectives

Active learning (AL) has shown promise to be a particularly data-efficient machine learning approach. Yet, its performance depends on the application, and it is not clear when AL practitioners can expect computational savings. Here, we carry out a systematic AL performance assessment for three diverse molecular datasets and two common scientific tasks: compiling compact, informative datasets and targeted molecular searches. We implemented AL with Gaussian processes (GP) and used the many-body tensor as molecular representation. For the first task, we tested different data acquisition strategies, batch sizes, and GP noise settings. AL was insensitive to the acquisition batch size, and we observed the best AL performance for the acquisition strategy that combines uncertainty reduction with clustering to promote diversity. However, for optimal GP noise settings, AL did not outperform the randomized selection of data points. Conversely, for targeted searches, AL outperformed random sampling and achieved data savings of up to 64%. Our analysis provides insight into this task-specific performance difference in terms of target distributions and data collection strategies. We established that the performance of AL depends on the relative distribution of the target molecules in comparison to the total dataset distribution, with the largest computational savings achieved when their overlap is minimal.

Tackling misinformation in mobile social networks a BERT-LSTM approach for enhancing digital literacy

Abstract The rapid proliferation of mobile social networks has significantly accelerated the dissemination of misinformation, posing serious risks to social stability, public health, and democratic processes. Early detection of misinformation is essential yet challenging, particularly in contexts where initial content propagation lacks user feedback and engagement data. This study presents a novel hybrid model that combines Bidirectional Encoder Representations from Transformers (BERT) with Long Short-Term Memory (LSTM) networks to enhance the detection of misinformation using only textual content. Extensive evaluations revealed that the BERT-LSTM model achieved an accuracy of 93.51%, a recall of 91.96%, and an F1 score of 92.73% in identifying misinformation. A controlled user study with 100 participants demonstrated the model’s effectiveness as an educational tool, with the experimental group achieving 89.4% accuracy in misinformation detection compared to 74.2% in the control group, while showing increased confidence levels and reduced decision-making time. Beyond its technical efficacy, the model exhibits significant potential in fostering critical thinking skills necessary for digital literacy. The findings underscore the transformative potential of advanced AI techniques in addressing the challenges of misinformation in the digital age.

Controlling crystal planes of biomass-derived carbon based Mo2C NPs and the electrochemical performance

The electrochemical property of Mo2C nanoparticles (NPs) depends on the structure and crystal planes. Herein, Mo2C nanoparticles were prepared and dispersed on carbon nanosheets by the construction of a biomass-derived carbon precursor, and the exposed dual crystal planes were also controlled by optimal conditions. The structure, compositions, and morphology of the carbon-based Mo2C were characterized, and the Mo2C NPs were well dispersed on the carbon nanosheets. The electrochemical study shows that optimal Mo2C exhibits excellent electrochemical properties for the oxidation of nicotine compared with other materials in the broad linear range of 0.2–300 μM. In particular, it displays a remarkable oxidation ability for the low-concentration nicotine (0.2–5 μM), and the detection limit is about 0.17 μM. Furthermore, the exposed dual crystal planes of Mo2C play a critical role in the oxidation. Notably, this characteristic of Mo2C NPs makes it possible to detect nicotine from the extracted solution and be used for chip electrodes to detect nicotine quickly via wireless response, which exhibits actual application prospects for portable detection. The results indicate that the as-prepared Mo2C material could be effective and low-cost for nicotine analysis in the sectors of health management and medical fields.

AL161431.1 is identified as a biomarker for bladder cancer progression and immunotherapy response

Amorphous-dominated MgO hollow spheres enhanced fluoride adsorption: Mechanism analysis and machine learning prediction

Amorphous-dominated magnesium oxide hollow spheres (A-MgO) were prepared using a spray-drying method in this study. These hollow spheres exhibited excellent sphericity, large specific surface areas, and abundant porosity. A-MgO exhibited outstanding fluoride adsorption properties, with a maximum adsorption capacity of 260.4 mg/g. When the pH value was less than 8, the fluoride removal percentage remained more than 87.4%. Moreover, the removal percentage remained above 75% even after five application cycles. In addition, the research revealed that SO42−, CO32−, and PO43− exerted a more pronounced effect on fluoride removal, whereas coexisting ions such as Br−, Cl−, NO3−, and HCO3− had minimal impact on this process. An in-depth analysis of the adsorption mechanism demonstrated that the process of fluoride adsorption by A-MgO involves various synergistic mechanisms, such as electrostatic adsorption, ion exchange, oxygen vacancy adsorption, physical adsorption, and pore filling. To predict the fluoride ion adsorption performance of A-MgO under complex conditions, a high-performance machine learning model, GBDT-S, was developed using hyperparameter optimization. The R2 of 0.99 and 0.80 for the training and testing datasets, respectively, with the RMSE of 3.26 and 3.89. Interpretative analysis using SHapley Additive exPlanations technology indicated that reaction time, PO43− concentration, and pH were key factors influencing the fluoride ion removal percentage.

Pre-stack seismic inversion for reservoir characterization in Pleistocene to Pliocene channels, Baltim gas field, Nile Delta, Egypt

AbstractThe Nile Delta, North Africa’s leading gas-producing region, was the focus of this study aimed at delineating gas-bearing sandstone reservoirs from the Pleistocene to Pliocene formations using a combination of pre-stack inversion and rock physics analysis. This research employed seismic inversion techniques, including full-angle stack seismic volumes, well logs, and 3-D with rock physics modeling to refine volumes of P-wave velocity (Vp), S-wave velocity (Vs), and density. Traditional seismic attributes, such as far amplitude, proved insufficient for confirming gas presence, highlighting partial angle stacks, integrated the need for advanced methods. Extended Elastic Impedance (EEI) analysis was used to predict fluids and identify lithology in clastic reservoir environments. The EEI approach facilitated the determination of optimal projection angles for key petrophysical properties such as porosity, shale volume, and water saturation. This method was applied to the middle Pliocene (Kafr El Sheikh Formation) and the Pleistocene (El Wastani Formation), revealing promising drilling sites. In the Kafr El Sheikh Formation, porosity ranged from 16 to 29%, shale volume from 21 to 40%, and hydrocarbon saturation from 25 to 90%. The study concludes that integrating pre-stack seismic inversion with EEI significantly enhances the likelihood of identifying gas-bearing sands while reducing exploration risks. The improved POS for the Pleistocene anomaly gas bearing sand (from 49 to 69%) and the middle Pliocene anomaly (from 46 to 66%) underscores the effectiveness of this approach in the Baltim Field, Offshore Nile Delta, and supports further drilling and development wells.