Browse Articles
Discover research articles across all indexed journals
Pressure-dependent structure of neat liquid methanol, CH3OH: Molecular dynamics simulations with various united atom-type potentials
Molecular dynamics computer simulations have been conducted on neat liquid methanol, using three different “united atom” (three site) interatomic potentials: TraPPE [Chen et al., J. Phys. Chem. B 105, 3093 (2001)], UAM-I [García-Melgarejo et al., J. Mol. Liq. 323, 114576 (2021)], and OPLS/2016 [D. Gonzalez-Salgado and C. Vega, J. Chem. Phys. 145, 034508 (2016)]. The effects of pressure, between 1 bar and 6 kbar, have been evaluated on total scattering structure factors, partial radial distribution functions, and on collective characteristics such as ring-size distributions and cluster-size distributions. Agreement with experimental density is nearly quantitative for all three force fields, and major trends observed for recent pressure-dependent neutron diffraction data are reproduced qualitatively. In general, the OPLS/2016 force field generates properties that are markedly different from results originating from the other potentials. Pressure effects are hardly noticeable on most partial radial distribution functions and on the distribution of the number of hydrogen-bonded neighbors. On the other hand, collective structural properties, such as cluster- and ring-size distributions, exhibit significant changes with increasing pressure: larger clusters become more numerous, whereas the number of cyclic clusters, i.e., rings, decreases. The self-diffusion coefficient decreases with increasing pressure, and the same is valid for the average lifetime of hydrogen bonds.
Visible Brillouin-quadratic microlaser in a high-Q thin-film lithium niobate microdisk
Wearable sensing for badminton stroke recognition with one-dimensional convolutional neural network
Efficient optimization of low-rank antisymmetric product of geminals wavefunction using the direct Givens rotation method
In our previous study [Kawasaki and Nakatani, Mol. Phys. 123(16), e2449186 (2025)], we proposed the low-rank antisymmetric product of geminals (APG) method, which reconstructs the wavefunction by extracting only the important eigenvalues from the APG wavefunction. However, its practical application was limited by the high computational cost from an orbital optimization process, making higher-rank calculations difficult. In this work, we reformulate the orbital part of the wavefunction using Givens rotation matrices, enabling an analytical treatment of the variational optimization. By combining the low-rank APG with the direct Givens rotation method, we achieved a significant improvement in optimization efficiency. We applied the developed method to small molecular systems and confirmed that it provides high accuracy, while also significantly reducing the computational time compared to conventional methods.
Desaturase-dependent secretory functions of hepatocyte-like cells control systemic lipid metabolism during starvation in Drosophila
Abstract Similar to the mammalian hepatocytes, Drosophila oenocytes accumulate fat during fasting, but it is unclear how they communicate with the fat body, the major lipid source. Using a modified protocol for prolonged starvation, we show that knockdown of the sole delta 9 desaturase, Desat1 (SCD in mammals), specifically in oenocytes leads to more saturated lipids in the hemolymph and reduced triacylglycerol storage in the fat body as well as reduced survival. We further show that the insulin antagonist ImpL2 (IGFBP7 in mammals) is secreted from oenocytes during starvation in a Desat1-dependent manner. Flies with oenocyte-specific knockdown and overexpression of ImpL2 exhibit higher and lower sensitivity to starvation, lower and higher triacylglycerol levels as well as higher and lower levels of bmm during starvation, respectively. Overall, this study highlights the importance of Desat1 in maintaining the proper functioning of oenocytes and the central role of oenocytes in the regulation of fat body lipid metabolism during periods of prolonged starvation.
Sol–gel and co-precipitation synthesized hybrid nanofluids for enhanced CNC turning of AISI 4340 steel: an experimental and machine learning approach
Abstract Machining high-strength alloys, such as AISI 4340 steel, presents significant challenges in terms of surface integrity, production efficiency, and heat dissipation. This study investigated the effects of a novel hybrid nanofluid of copper oxide (CuO) and aluminum oxide (Al 2 O 3 ) nanoparticles to improve CNC turning of AISI 4340 steel. The experiments were conducted under a range of cutting conditions by varying the cutting speed, depth of cut and feed rate, along with the concentration of the hybrid nanofluid. A new methodology for preparing and applying the hybrid nanofluid demonstrated sufficient cooling and lubrication properties, enabling machining tests that improved upon traditional methods. The experimental study indicated that as the cutting speed and feed rate increased, the cutting temperature and surface roughness also increased significantly. Increasing the nanofluid concentration (0.25–0.45%) lowered the tool tip temperature and surface roughness due to increased thermal conductivity and formation of a protective tribological film. However, beyond 0.45% hybrid nanofluid concentration, the performance declined due to increased fluid viscosity and agglomeration of nanoparticles. An Artificial Neural Network (ANN) demonstrated significant predictive accuracy, with coefficients of determination (R 2 ) of 0.864 for tool tip temperature, 0.828 for surface roughness, and 0.942 for material removal rate (MRR). The Genetic Algorithm (GA) determined the optimal nanofluid concentration of 0.4%, cutting speed of 80 m/min, feed rate of 0.07 mm/rev, and depth of cut of 0.4 mm. Experimental data confirmed ANN predictions with an error range of less than ± 2%, and confirmatory trials demonstrated that heat was dissipated, showing improved surface quality and MRR.
Effects of solvation structure, aggregation, and dynamic heterogeneity in highly concentrated electrolytes
Understanding ion transport in highly concentrated electrolytes requires establishing clear connections between local structural organization and dynamical response. Here, we performed molecular dynamics simulations of lithium and sodium bis(fluorosulfonyl)imide and fluoroborate salts (Li[FSA], Na[FSA], and LiBF4) dissolved in sulfolane and 3-methylsulfolane, over a temperature range of 350–450 K and solvent mole fractions between 0.5 and 0.91. Structural analyses show that heterogeneities in these mixtures reach a maximum near solvent mole fraction of 0.67 (corresponding to a solvent-to-salt ratio of 2:1), where the proportions of solvent and anion oxygen atoms in the cation coordination shell become comparable. At this composition, the mixtures exhibit extensive cation–anion and cation–sulfone aggregates, and the prepeak in the total x-ray structure factor (indicative of nanosegregation) reaches maximum intensity. Dynamical properties, characterized by time-correlation functions between cations and anions and between cations and solvent molecules, display non-exponential relaxation behavior. The stretching exponent shows an inflection at the same composition identified in the structural analyses. These combined structural and dynamical signatures identified a critical composition window separating vehicular and hopping transport mechanisms in sulfolane and 3-methylsulfolane. The observed crossover provides a microscopic framework for understanding concentration-dependent conductivity in sulfone-based electrolytes and offers a design guideline for tuning ion mobility through the control of the solvent-to-salt ratio.
Spermine modulation of Alzheimer’s Tau and Parkinson’s α-synuclein: implications for biomolecular condensation and neurodegeneration
Abstract Spermine, a pivotal player in biomolecular condensation and diverse cellular processes, has emerged as a focus of investigation in aging, neurodegeneration, and other diseases. Despite its significance, the mechanistic details of spermine remain incompletely understood. Here, we describe the distinct modulation by spermine on Alzheimer’s Tau and Parkinson’s α-synuclein, elucidating their condensation behaviors in vitro and in vivo. Using biophysical techniques including time-resolved SAXS and NMR, we trace electrostatically driven transitions from atomic-scale conformational changes to mesoscopic structures. Notably, spermine extends lifespan, ameliorates movement deficits, and restores mitochondrial function in C. elegans models expressing Tau and α-synuclein. Acting as a molecular glue, spermine orchestrates in vivo condensation of α-synuclein, influences condensate mobility, and promotes degradation via autophagy, specifically through autophagosome expansion. This study unveils the interplay between spermine, protein condensation, and functional outcomes, advancing our understanding of neurodegenerative diseases and paving the way for therapeutic development.
Fully automated detection and identification of CSF shunt valves using YOLOv8 and a class-based reference image assignment as a safety mechanism
Abstract The study aimed to develop and evaluate an algorithm based on the YOLOv8x framework to automatically detect and identify cerebrospinal fluid (CSF) shunt valves. This approach seeks to streamline the diagnostic process identifying shunt valve types and pressure levels. A retrospective cohort of 2701 anonymized radiographs comprising six types of CSF shunt valves was used. Data augmentation techniques such as flipping, scaling, and mosaic augmentation were applied during training to enhance robustness. The dataset was split into 80% training and 20% testing subsets as part of a 5-fold cross-validation. Validation was conducted on a separate test set of 295 images using metrics such as mean Average Precision (mAP) at intersection over union thresholds of 50% (mAP50) as well as precision, recall, and F1-scores as metrics. Additionally, a class-based reference image assignment system was used to link the detected valves with the corresponding manufacturer images. These paired images were then independently reviewed by two radiologists to assess the accuracy of the algorithm’s classifications. The algorithm achieved a weighted mAP50 of 0.884 and a weighted average F1-score of 94.8%. High F1-scores were observed for Codman Certas (99.6%) and Codman Hakim (99.6%), with lower scores for less common valves like proGAV (30.8%). Radiologists were able to identify both correct and incorrect classifications made by the algorithm with 100% accuracy, due to the integrated safety mechanism. This safety mechanism relies on the fully automated linking of detected valves with the corresponding manufacturer images. In Conclusion the automated system demonstrated high efficiency in detecting and classifying CSF shunt valves, significantly simplifying the diagnostic workflow. Moreover, the integration of a robust safety mechanism ensures that potential misclassifications are identified and corrected.
Rational design of oxygen-vacancy-rich MnO nanoparticles anchored on meteorite crater-like carbon skeleton for high-performance aqueous zinc-ion battery cathodes
Aqueous zinc ion batteries (AZIBs) have garnered significant attention as promising candidates for large-scale energy storage systems, owing to their high energy density, cost-effectiveness, inherent safety, and environmental sustainability. In this study, we introduce a novel composite material, oxygen-deficient manganese oxide integrated with a meteorite crater-like carbon skeleton (Ov-MnO/mC), synthesized via a unique micro-explosion reaction. This innovative synthesis method not only generates a distinctive meteorite crater-like carbon framework but also introduces oxygen vacancies into the MnO structure. When employed as a cathode in AZIBs, the Ov-MnO/mC composite demonstrates remarkable electrochemical performance without the addition of manganese salts to the electrolyte. In particular, at a current density of 0.2 A g−1, the cathode achieves a high specific capacity of 282.6 mAh g−1 and maintains a capacity of 122.0 mAh g−1 even at a high rate of 5 A g−1. Moreover, the cathode exhibits excellent cycling stability, retaining 198.9 mAh g−1 after 80 cycles at 0.2 A g−1 and 63.2 mAh g−1 after 920 cycles at 1 A g−1. The underlying energy storage mechanism is thoroughly investigated using in situ Raman spectroscopy and ex situ XRD analysis, providing valuable insights into the structural evolution of the composite during charge–discharge processes. This research not only advances the development of high-performance manganese oxide-based cathodes but also presents a scalable and practical approach for designing efficient energy storage materials.
Author Correction: Intensification of extreme cold events in East Asia in response to global mean sea-level rise
Enhancing workplace productivity with secure AI using federated contrastive learning model for performance optimization
Ion solvation under gigapascal pressure
Ion solvation in a range of gigapascal (GPa) pressure is of great significance for high-pressure chemical synthesis and circulation of matter within the Earth’s interior. We perform neutron scattering (NS) experiments and molecular dynamics simulations of deuterated aqueous solutions of MCl (M = Li, Na, K, Rb, and Cs) at 0.1 MPa and 0.7 GPa/298 K. An empirical potential structure refinement method analyzes the NS data. Upon compression to 0.7 GPa, the outer-shell water molecules enter the nearest neighbor of ions and the solvated ion clusters become denser. The hydration factor and static hydration number, based on the orientation distribution of the water dipole in the first solvation shell, show that compression weakens the strength of ionic hydration. Compression suppresses the diffusion of ions, particularly those of structure-breaking ions. The average residence time of water molecules indicates that under compression, the exchange rate of water molecules in the solvation shell of the structure-making ion (Li+) and the bulk water molecules is faster. In contrast, the effect of pressure on the exchange rate of water molecules in the solvation shell of the boundary ion (Na+) and the structure-breaking ions (K+, Rb+, Cs+) and the bulk water molecules can be ignored.
High-resolution national mapping of natural gas composition substantially updates methane leakage impacts
Type-I hot corrosion behavior of Cr-modified slurry aluminide coating superalloy Rene 80
Interplay of distinct modes of charge regulation on poly-acid ionization and conformation
We adapt the Edwards–Muthukumar theoretical framework for a single polymer chain to investigate the interplay between proton binding and counterion condensation for poly-acids. We find that changes to pH enable non-monotonic transitions between anti- and conventional polyelectrolyte behaviors. In the former, the net charge and the overall dimensions increase with increasing salt concentration, while the converse is true for conventional polyelectrolytes. The polymeric nature and local solvent polarization drive significant pKa shifts when compared to the values of reference monoacids. These pKa shifts are enhanced in semi-flexible chains.
Gradient descent in materia through homodyne gradient extraction
Abstract Deep learning, a multilayered neural-network approach inspired by the brain, has revolutionized machine learning. Its success relies on backpropagation, which computes gradients of a loss function for use in gradient descent. However, digital implementations are energy hungry, with power demands limiting many applications. This has motivated specialized hardware, from neuromorphic CMOS and photonic tensor cores to unconventional material-based systems. Learning in such systems, for example via artificial evolution, equilibrium propagation, or surrogate modelling, is typically complicated and slow. Here, we demonstrate a simple gradient-extraction method based on homodyne detection, enabling gradient descent directly in physical systems without the need for an analytical description. By perturbing parameters with sinusoidal waveforms at distinct frequencies, we robustly obtain gradient information in a scalable manner. We illustrate the method in reconfigurable nonlinear-processing units and argue for broad applicability. Homodyne gradient extraction can in principle be fully implemented in materia, facilitating autonomously learning material systems.
A novel fault diagnosis method based on EEWT-EWLTSA and improved deep ELM
Decoherence of Morse oscillator in the presence of dissipationless environment
The much-studied Morse oscillator (MO) is couched here in the context of an open quantum system, in which the interaction with the quantum environment, however, is taken to commute with the subsystem Hamiltonian. The result is decoherence sans dissipation because of dephasing in the off-diagonal elements of the reduced density operator. The analytical results are numerically computed for a range of parameters for different attributes of decoherence. Finally, a comparison is made for the corresponding harmonic system in order to highlight the significance of anharmonicity in the MO as far as dependencies on the temperature and the environmental coupling are concerned.