Browse Articles
Discover research articles across all indexed journals
The impact of metaverse-driven design on supply chain resilience and performance: a hybrid PLS-SEM and ANN approach
Orbital energy variations provide a physical basis for the Woodward-Hoffmann rules
A refined general AMBER force field for liquid acetonitrile: Development and validation
GAFF-ACN, a refined general AMBER force field specific for liquid acetonitrile, is introduced aiming for accurately reproducing both the experimental density and static dielectric constant using classical NPT molecular dynamics (MD) simulations. Its parameterization combines a quantum-mechanically derived geometry with restrained electrostatic potential charges in a polarizable continuum model, standard GAFFv2.11 parameters, and enforced molecular linearity. GAFF-ACN reproduces a broad set of macroscopic and microscopic properties of liquid acetonitrile. The density agrees closely with experiment (−0.4% relative error), and the static dielectric constant differs by 13%, representing, to the best of our knowledge, the most accurate dielectric constant reported to date for a fixed-charge model. GAFF-ACN also reproduces (i) key thermodynamic observables, i.e., heat of vaporization, surface tension, isothermal compressibility, and thermal expansion coefficient; (ii) mass transport coefficients, viz. self-diffusion and shear viscosity; and (iii) structural aspects including pairwise radial distribution functions and coordination numbers. All results are obtained using standard simulation protocols, enabling reproducibility and straightforward adoption across common MD packages. GAFF-ACN, therefore, provides an accurate and practical acetonitrile model for the solvation of GAFF-parameterized organic solutes.
Sustainable moisture-driven electricity generation using waste materials
Abstract Atmospheric moisture is an abundant, renewable resource with potential for sustainable energy harvesting. While moisture-material interactions can generate electricity under ambient conditions, most current systems remain expensive, and produce voltages too low for direct use in wearable electronics. To address these limitations, we developed a moisture-driven electric generator (MEG) from waste biomass and recycled materials that convert ambient humidity into electrical output. The MEG integrates wild sugarcane fibers and recycled cigarette-butt cellulose with an upcycled carbon-paste layer, which enhances moisture uptake, ion dissociation, and directional ion migration across asymmetric current collectors. A single unit delivers up to 1.16 V and 16.44 µW cm −3 , operates under ambient humidity conditions, and restores voltage after drying following renewed natural moisture reabsorption. A basic conceptual model is proposed in which moisture adsorption within the hygroscopic composite promotes NaCl ion dissociation and directional ionic transport, leading to interfacial charge separation between asymmetric electrodes and the generation of a measurable potential difference. Scalable series/parallel configurations boost voltage and current, enabling direct operation of low-power electronic devices under maintained humid conditions without external capacitors. This low-cost approach highlights moisture-activated textile composites as sustainable power sources for self-powered and low-power electronic systems.
A microscopic theory of small-droplet adhesion on solid surfaces
Droplets resting on solid surfaces resist lateral forces before sliding; however, the microscopic origins of the sliding criterion and solid–liquid adhesion remain elusive. Here, we develop a microscopic mean-field theory that incorporates the dependence of solid–liquid interfacial energy on the droplet’s body energy and maps the full energy landscape under coupled normal and lateral loads. Within this framework, we propose a sliding criterion within the small-droplet limit, in which the solid–liquid and solid–gas interfacial energies become equal, as a result of the formation of a novel microscopic lubrication-like interfacial state. This criterion explains the observed enhancement of adhesion under increasingly negative normal loads, thereby violating Amontons’ first law. Furthermore, we reveal the microscopic origin of pinning force associated with contact angle hysteresis. Our approach eliminates inconsistencies associated with body–force balance in small droplets and achieves quantitative agreement with counterintuitive experimental observations across diverse conditions.
Development of soil quality index for coastal saline soils of Bangladesh
Four deep-sea technologies that could help detect deadly earthquakes and tsunamis
The influence of diffusing diffusivity on barrier crossing dynamics
This paper investigates how the conventional picture of condensed phase reaction dynamics—based on Kramers’ flux-over-population approach—is modified by the process of “diffusing diffusivity,” the mechanism generally understood to underlie the anomalous behavior of particles in cellular interiors and other complex liquids. The question is addressed here within the framework of a model in which a point particle evolves stochastically in a double-well potential in the presence of thermal fluctuations that are coupled multiplicatively to Ornstein–Uhlenbeck (O–U) noise. The model is used to calculate various measures of barrier crossing statistics, including the survival probability, the mean first passage time, and the mean first passage time distribution, for all of which exact expressions can be derived. It is also found that as a result of the influence of the O–U relaxation timescale on particle motion, these quantities become dynamically heterogeneous. Furthermore, in the disorder-free limit, they yield expressions that are quite different—in general appearance—from the corresponding formulas in Kramers’ analysis, but are still consistent with them nevertheless.
Artificial intelligence enabled behavior modeling and dual-task performance analysis of cloud-native software with fused multi-source heterogeneous data
Abstract To address the challenges of heterogeneous multi-source data, inadequate collaborative modeling of temporal and topological features, and low efficiency in dual-task optimization in performance prediction and bottleneck localization for cloud-native microservice systems, this paper proposes a Multi-Modal and Multi-Scale Temporal Propagation model (M $$^3$$ TP). The model achieves unified representation of logs, time-series metrics, and call-chain data through a Multi-modal Heterogeneous Embedding Module, simultaneously captures dynamic evolutionary patterns and service topological dependencies via a Temporal-Graph Joint Learning Module, and implements joint optimization of performance prediction and bottleneck localization using a Dual-Task Collaborative Decoding mechanism. Experiments on two public datasets, GAIA and PetShop, demonstrate that the M $$^3$$ TP model achieves $$R^2$$ scores of 0.95 and 0.96 for performance prediction, F1 scores of 0.93 and 0.94 for bottleneck localization, and inference latencies as low as 7.1ms and 6.5ms, respectively, outperforming 8 baseline models including LSTM, Informer, and GAT. Ablation studies validate the effectiveness of each core component, and case studies confirm the model’s capability in capturing performance fluctuations and identifying root-cause services accurately. The proposed model can effectively support cloud-native AIOps and provide a solid technical foundation for proactive monitoring and fault diagnosis of microservice systems.
XMCQDPT2-fidelity transfer-learning potentials and a wavepacket oscillation model for ultrafast photodynamics
The accurate simulation of photochemical reactions requires methods that capture nonadiabatic transitions through conical intersections between different electronic states. While machine-learning interatomic potentials (MLIPs) offer a promising route to efficient nonadiabatic molecular dynamics, their training for excited states is often limited by the prohibitive cost of generating extensive datasets at a sufficiently high level of quantum chemistry theory. Here, we explore strategies for developing MLIPs that achieve multistate multireference perturbation theory accuracy, systematically comparing single-state, multi-output, multi-state, transfer learning, and delta-learning architectures. We show that transfer learning (TL) from CASSCF to XMCQDPT2 provides the best balance of accuracy and computational efficiency, dramatically reducing the amount of expensive reference data required. This methodology is validated on the methaniminium cation, whose complete photodissociation landscape, including S1 branching into photoisomerization and a direct H2-loss pathway mediated by a recently discovered conical intersection, is captured at the XMCQDPT2/SA(3)-CASSCF(12,12) level. We show that final product yields are largely independent of training strategy, whereas the TL model produces distinct ultrafast population dynamics compared to the randomly initialized model. Finally, we develop a wavepacket oscillation model for fitting excited-state population dynamics, which quantitatively reproduces the ultrafast non-exponential decay and extracts state- and channel-specific lifetimes, directly linking quantum transition probabilities to classical rate constants.
Triple-polarization mechanically-reconfigurable printed dipole antenna with integrated balun for X-Band LP/RHCP/LHCP modes
DNA-shredding CRISPR enzyme takes aim at cancer cells
On the arrangement of pyridinium cations in one-dimensional PyPbBr3 perovskite-related compound: DFT calculations and Raman spectroscopy
Solving the problem of the arrangement of pyridinium cations in one-dimensional PyPbBr3 perovskite-related compound, the possible configurations with different orientation of the pyridinium cations relative to the anionic framework were theoretically proposed. Their structural characteristics and formation energy values were estimated using density functional theory (DFT) calculations. The calculated and experimental Raman spectra were compared in the wide spectral range, and the correspondence between the observed spectral lines and the characteristic modes in the calculated vibrational spectra of the PyPbBr3 models was established. The presence of several orientations of Py cations in the PyPbBr3 sample at room temperature was suggested.
Compound extreme events of rapid warming and air pollution driven by foehn over the northern slope of the middle tianshan mountains
Quantum scattering pathway of the cofactorless spin-forbidden O2 addition to DPA-CoA
Certain oxidases and oxygenases catalyze the spin-forbidden incorporation of O2 into organic substrates without requiring a cofactor. A general mechanism for this reaction remains elusive; intriguingly, these enzymes accomplish this without relying on specialized catalytic machinery. Here, we report nonadiabatic quantum scattering calculations on a simplified model to investigate the nuclear quantum effects governing these processes. Our model includes six singlet and three triplet states, explicitly accounting for spin–orbit coupling. Our results indicate that the degree of spin-forbiddenness corresponds to a kinetic hindrance of only ≈2.45 kcal/mol at room temperature, slightly lower than the value predicted by nonadiabatic transition state theory (NA-TST). However, the discrepancy between the scattering results and NA-TST predictions is minor, validating the use of NA-TST for this class of reactions. Furthermore, our calculations account for the possible formation of singlet O2, a reactive oxygen species (ROS). We find that once this channel opens, singlet O2 is produced with significant probability, suggesting that ROS could be generated via the stabilization of singlet O2 within the protein cavity.
CNN-driven recognition of fluvial terrace sequences along the middle Yangtze River and its neotectonic implications
Perspective on a challenge: Predicting the photochemistry of cyclobutanone
This Perspective is part of a Special Topic that explored the maturity of nonadiabatic molecular dynamics for predicting photochemical processes. In 2023, a prediction challenge was issued to the community of computational photochemists to simulate the photochemistry of cyclobutanone, photoexcited at 200 nm, and the resulting time-resolved mega-electronvolt ultrafast electron diffraction (MeV-UED) signal. The challenge attracted 15 theoretical predictions from more than 70 researchers, employing a wide range of strategies for electronic structure and nonadiabatic molecular dynamics to predict the time-resolved MeV-UED signal before the experiment had been conducted at SLAC (Stanford, USA). The MeV-UED instrument at Shanghai Jiao Tong University was also used to provide a second independent time-resolved MeV-UED signal for the photochemistry of cyclobutanone. This Perspective discusses the various approaches and strategies used by the participants to predict the photochemistry of cyclobutanone. This work also summarizes the strengths and weaknesses of various methods used for photoexcitation, electronic structure, nonadiabatic dynamics, and calculation of observables, as agreed by the participants during a CECAM workshop dedicated to the results of the challenge and organized in Lausanne in April 2025. This Perspective also collects all the predicted time-resolved MeV-UED signals into a single figure, together with the experimental signal. The challenge (i) demonstrated the qualitative predictive power of nonadiabatic molecular dynamics and (ii) underscored the impact of electronic-structure theory on the outcome of the excited-state dynamics and the need for its careful benchmarking. This effort allowed the community to share practical strategies to perform nonadiabatic dynamics (discussed in the present Perspective) and constitutes a “calibration” exercise for computational photochemistry.
Scalable B–B Single-Bond Formation via Boron-Centered Carboranyl Radicals: Boryl-Carborane for Boron Neutron Capture Therapy Applications
Dynamics near equilibria in the Jupiter-Europa system using the Lie-series technique
Abstract Investigating the dynamics near planetary satellites is crucial for space mission design. This work investigates the dynamics around the equilibrium points in the Jupiter-Europa system in the restricted three-body problem framework. The current dynamical model incorporates perturbations due to the oblateness of both Jupiter and Europa, as well as the equatorial ellipticity of Europa. We constructed the equations of motion under the effects of the perturbations considered. Then, we applied the Lie-integration method to solve the equations of motion and derive an analytical approximate solution. Also, we used the software package CVODE to validate our results. The results confirmed close agreement between the Lie series and the numerical approaches. Using the Lie-series method, we applied different initial conditions to generate horseshoe and tadpole orbits around the equilateral points in both the classical circular restricted three-body problem and the perturbed model. In addition, we investigated the linear stability of the Jupiter–Europa system and found that it remains stable up to a critical mass ratio of $$\mu _c=0.0249882790$$ . Furthermore, we used the zero-velocity surfaces to analyze the accessibility condition in the Jupiter-Europa system. We found that the spacecraft transit between the primaries remains possible until the Jacobi constant exceeds the value 3.00382022.