Browse Articles

Synthesis and evaluation of nanohybrid pour point depressants as flow improvers for waxy crude oils

Subgrain boundary energy in Ih ice: A molecular dynamics study using mW and Tip4p/ice potentials

In this work, molecular dynamics simulations were used to calculate the energies of subgrain boundaries in ice Ih bicrystals. Based on the wurtzite structure, a series of symmetric tilt-type bicrystals with a rotation axis of ⟨011̄0⟩ were constructed for misorientations less than 20°. The subgrain boundaries were formed as an arrangement of edge dislocations, taking into account the glide set of basal slip planes to achieve the lowest energy initial configuration. The LAMMPS code was used for the simulations, testing the mW and Tip4p/Ice potentials. The energies were calculated at a temperature of 5 K, taking into account their exponential dependence on the distance perpendicular to the subgrain boundary. The resulting energies fit the Read–Shockley model for the two potentials analyzed, and both their values and those of the elastic constants derived from them agree with those obtained in previous research.

Impact of comorbid diseases and associated factors on tuberculosis treatment outcomes among tuberculosis patients in South West Oromia, Ethiopia: a retrospective cohort study

Thermodynamic discontinuity in evaporating thin solvated nematics

Self-organized patterns of nematic liquid crystals (NLCs) are indispensable in sensing and opto-electronics due to their intricate molecular sensitivities. A molecular-scale investigation of solvated thin NLC films reveals that concurrent discontinuities in NLC–NLC interaction-driven thermodynamic properties, mobility, and rheological responses engender the incipient and necessary conditions for dewetting. This spontaneous dewetting is followed by self-organized morphological evolution, which proceeds through concentration-dependent distinct pathways. These discontinuities arise from an entropic landscape generated by a sufficient fraction of solvent molecules—an effect absent in pure thin NLC films, overcoming a long-standing challenge in achieving controlled self-organized patterns in such systems. The magnitude of these discontinuities governs film instability and the resulting morphological transitions with varying molecular orientations, consistent with the developed continuum-scale theory and previous experimental observations. This continuum framework, developed for the first time for solvated anisotropic systems, incorporates anisotropic contributions of NLC molecules derived from molecular-scale energetics and accurately recovers the characteristic pattern length scales in agreement with experiments. This enables the establishment of a multi-scale, unified framework, as deployed in this study, for solvent evaporation-induced nano/microfabrications.

Effectiveness of estuarine adaptation strategies under future climate conditions

Abstract Estuaries are among the most intensively used aquatic environments, where steep physical and biogeochemical gradients interact with dense human populations and valuable ecosystems. Intense human usage has historically led to ecological degradation and persistent use conflicts, in many temperate estuaries that host major inland ports such as the Elbe estuary in the North Sea. Climate change, with its associated effects on sea levels and global temperatures, represents a significant challenge for the functioning of these systems. The impact on estuarine hydrodynamics, sediment dynamics and water quality is, however, not yet fully understood. In this study, we utilise a coupled physical–biogeochemical model to assess the estuary’s response to an extreme climate scenario, focusing on the evaluation of potential adaptation options that could mitigate the impact of the climate scenario. By comparing changes in key physical and biogeochemical state variables across interventions, we assess whether river engineering measures can alleviate risks such as elevated storm-surge levels, enhanced upstream particulate transport, and climate-driven degradation of ecological and water-quality conditions. Our findings demonstrate that targeted adaptation can effectively mitigate several adverse consequences of sea level rise and warming, emphasising the necessity to integrate climate projections and adaptation pathways into future estuarine management strategies.

Phase diagram and global structure search of bismuth using machine learning potential

Bismuth’s (Bi) unique high-pressure phase behavior has long attracted significant interest. Despite their significance in both technological applications and fundamental research, comprehensive and accurate modeling of these transitions remains challenging. To address this, we developed a neural equivariant potential machine learning potential for Bi with near first-principles accuracy. By integrating this potential with state-of-the-art computational techniques—including the MAGUS crystal structure search algorithm and GPUMD molecular dynamics simulations with enhanced sampling—we systematically explored the phase behavior of Bi under high-pressure and high-temperature conditions. The calculated solid–solid phase boundaries and solid–liquid coexistence line up to 4 GPa show good agreement with previous experimental results. Furthermore, we predict a new competitive phase of Bi with P42/mnm symmetry, which is dynamically stable around 2 GPa and competitive at free energy with the known phase C2/m near the melting line.

Autism spectrum disorder trios from consanguineous populations are enriched for rare homozygous variants, identifying 32 new candidate genes

SAFT-VR Sum: Equation of state and transport properties from <i>ab initio</i> -derived Sutherland sum potentials

In recent decades, Statistical Associating Fluid Theory (SAFT) has advanced both in the accuracy of the underlying perturbation theory and in the representation of increasingly realistic molecular interactions. In this study, we extend the SAFT-VR Mie equation of state to a general sum of Sutherland potentials with temperature- and density-dependent prefactors, termed SAFT-VR Sum. A generalized Sutherland sum offers extensive flexibility in representing molecular interactions. We demonstrate this ability by regressing a sum of Sutherland potentials to reproduce the binary interactions of argon and neon from ab initio simulations. With three-particle effects approximated via the Axilrod–Teller–Muto potential and quantum nuclear effects incorporated via Feynman–Hibbs corrections, the resulting molecular model accurately captures thermophysical properties such as the viscosity and phase-equilibrium densities within 0.2% and the second virial coefficient within 1% above 15 K. The SAFT-VR Sum equation of state, with monomer interactions built solely from molecular information, predicts most thermodynamic properties of argon and neon within 2%, although properties such as the isochoric and isobaric heat capacities and the second virial coefficient are less accurately described by the perturbation theory. To probe the limits of the SAFT framework, we examine a potential with two force minima. For this potential, the α-function hypothesis commonly used in the development of perturbation theories fails to capture the second order perturbation term. Overall, a sum of Sutherland potentials provides sufficient flexibility for effective molecular descriptions to reproduce thermophysical properties of simple molecules within the experimental uncertainty. Advancing the thermodynamic perturbation theory thus offers a promising path to further drive SAFT toward experimental accuracy.

Photosynthetic pigments in developing seeds of Acer platanoides and Acer pseudoplatanus

Abstract Photosynthetic pigments were investigated in developing and dried seeds of desiccation-tolerant (orthodox) Acer platanoides and desiccation-sensitive (recalcitrant) Acer pseudoplatanus . The seeds of both Acer species contained higher levels of chlorophyll a (Chl a ), particularly in cotyledons, than chlorophyll b (Chl b ). Chl a and Chl b peaked during seed morphogenesis, then declined. The decrease was greater in A. platanoides (eight-fold) than in A. pseudoplatanus (three-fold) seeds. The total chlorophyll content was greater in the A. pseudoplatanus seeds only for three weeks, before morphogenesis began in the A. platanoides seeds. Photosystem II activity suggested that photosynthesis might be more active in A. pseudoplatanus at the initial and final stages of seed development. The carotenoid (Car) content increased during the initial stages of A. pseudoplatanus embryo development. The Car/Chl ratio indicated a significant contribution of Car to the pigment pool during the initial stages of development and drying in Acer seeds. The chlorophyll autofluorescence signal was irregular and diffuse in the embryonic axes of mature Acer seeds; however, a spherical and compact autofluorescence signal was reported in A. platanoides cotyledons. Differences in photosynthetic pigments are discussed in relation to seed orthodoxy and recalcitrance, as potentially influencing the longevity variations associated with seed categories.

Introducing a dielectric bath embedding theory for embedded electronic structure calculations in heterogeneous catalysis

Even after decades of electronic structure theory development, accurate modeling of reactions at metallic surfaces remains challenging. The most widely used method, i.e., density functional theory, can yield quantitatively and qualitatively inaccurate descriptions of electronic structures and reaction kinetics, motivating the use of higher-level correlated wavefunction (CW) theories, which better capture electron correlation effects. Quantum embedding theories allow advanced CW methods to be applied to a local region that interacts with its environment through, for example, an embedding potential optimized within density functional embedding theory (DFET). To ensure the accuracy of embedded electronic structure calculations, the local region of interest in the presence of the optimized embedding potential must reproduce the behavior of the original full system. Here, we introduce a polarizable embedding scheme that couples an external local potential to provide attractive and repulsive interactions, i.e., similar to the foundation of DFET, with a dielectric bath to reproduce polarization in response to the electric field of the cluster. Particularly, the time-consuming step within DFET, i.e., the optimized effective potential process, is replaced by a physics-informed approach to generate the embedding potential, significantly reducing the computational cost. We evaluate our polarizable embedding scheme using the Cu(111) surface and confirm that our approach outperforms the standard DFET in predicting the Fermi level, charge states, and binding strength of multiple adsorbates. We anticipate that this more efficient and robust embedding scheme could accelerate the mainstream use of embedded correlated wavefunction theory in the heterogeneous catalysis community.

A two-stage framework for cost-sensitive predictive maintenance using deep learning, GANs, and risk-aware clustering

Abstract Predictive maintenance (PdM) has seen significant advances through machine learning, yet its practical deployment remains challenged by data scarcity, system complexity, and uncertainty in cost-related decisions. The majority of current PdM strategies are concerned with enhancing Remaining Useful Life. Prediction accuracy of (RUL) in isolation and with maintenance scheduling as a problem (secondary or fixed). This study proposes a component based, decision oriented predictive maintenance (PdM) approach that links Remaining Useful Life (RUL) to optimization of maintenance. The two-stage framework proposed anticipates the component-specific RUL prediction where Long Short-Term Memory (LSTM) models was used to predict the Remaining Useful Life (RUL) of individual components. To address sparsity in failure data, Wasserstein Generative Adversarial Networks Gradient Penalty (WGAN-GP) were utilised to fill in run-to-failure sequences, stabilizing downstream modeling. In the second step, similar components in terms of Remaining Useful Life (RUL) degradation are clustered together by Density-Based Clustering Space (DBSCAN), which allows opportunistic maintenance. A decision on maintenance is then optimized cost-conscious grid search which works on fitted RUL distributions and a normalized. Not only based on point RUL estimates, but on a risk proxy. Empirical experimentation across multiple industrial components of a water bottling plant system indicates that the proposed approach continually reduces corrective failures and normalized maintenance costs as opposed to non-clustering approaches such as random choice and fixed choice of maintenance point. Sensitivity analysis also indicates that the optimal maintenance levels be consistent over a vast spectrum of cost assumptions, which emphasizes the resilience of the structure in economic uncertainty. Overall, this study contributes a robust and scalable maintenance literature that combines data augmentation, component based clustering, and risk aware optimization. This helps advance predictive maintenance practices into a more practical, cost aware decisions.

Data-driven prediction of ionic conductivity in solid-state electrolytes with machine learning and large language models

Solid-state electrolytes (SSEs) are attractive for next-generation lithium-ion batteries due to improved safety and stability, but their low room-temperature ionic conductivity hinders practical application. Experimental synthesis and testing of new SSEs remain time-consuming and resource-intensive. Machine learning offers an accelerated route for SSE discovery; however, composition-only models neglect structural factors important for ion transport, while graph neural networks are challenged by the scarcity of structure-labeled conductivity data and the prevalence of crystallographic disorder in crystal structures (CIFs). Here, we train two complementary predictors on the same room-temperature, structure-labeled dataset (n = 499). A gradient-boosted tree regressor model using stoichiometric descriptors alone achieves a test MAE of 1.108 in log(S/cm); adding geometric descriptors (combined MAE = 1.172) does not lower the test error but reveals complementary structural information through Shapley Additive exPlanations, which shows that stoichiometric descriptors, particularly the oxygen ratio, dominate feature importance (seven of the top ten features), with three geometric descriptors (density, Lmax, and Lmin) also contributing meaningfully. In parallel, we fine-tune large language models (LLMs) using compact text prompts derived from CIF metadata (formula with optional symmetry and disorder tags), avoiding direct use of raw atomic coordinates. Notably, while Mistral-7B achieves the lowest absolute error [MAE = 0.798 in log(S/cm)], Qwen3-8B demonstrates the best overall ranking performance (SRCC = 0.849) using formula and disorder information, eliminating the need for numerical feature extraction from CIF files. Together, these results show that global geometric descriptors improve tree-based predictions and enable interpretable structure–property analysis, while LLMs provide a competitive low-preprocessing alternative for rapid SSE screening.

Colorimetric detection of edible oil oxidation using PAN–Congo red nanofiber mats

Abstract Lipid oxidation significantly compromises the safety, nutritional quality, and shelf life of edible oils, while conventional analytical methods are costly, time-consuming, and unsuitable for real-time monitoring. This study presents a novel nanofiber-based colorimetric sensor for rapid visual detection of lipid oxidation in soybean oil (SBO) and extra virgin olive oil (EVOO) under accelerated storage conditions (70 °C for 35 days). Polyacrylonitrile (PAN) nanofiber mats containing Congo red dye (CR, 0.0025%, 0.005%, and 0.01%, w/w) were fabricated via solution-blowing spinning and characterized using scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). Oxidative deterioration was evaluated through conjugated diene (CD) and triene (CT) values, para-anisidine value (p-AnV), total polar compounds (TPC), volatile oxidation compounds, Rancimat induction period (IP), FTIR analysis, and cytotoxicity assays. Among the developed sensors, Mat_III (0.01% CR) exhibited the highest sensitivity, showing distinct color responses with ΔE values of 12.83 ± 0.20 in SBO and 9.83 ± 0.15 in EVOO, and rapid response times of 2.94 s and 4.71 s, respectively. After 35 days, CD, CT, and TPC increased to 3.83%, 0.96%, and 12.17% in SBO and 2.49%, 0.62%, and 7.83% in EVOO, while IP values decreased markedly, particularly in SBO, from 7.89 h to 2.04 h. Overall, these sensors offer a low-cost, rapid, and user-friendly approach for real-time oil oxidation monitoring.

Molecular architecture of block-polymer brushes for underwater oil droplet catch-and-release: A constant-pH hybrid Monte Carlo molecular-dynamics study

We quantify how block-brush architecture controls the underwater catch-and-release of oil droplets by poly([2-(methacryloyloxy)ethyl]trimethylammonium chloride) (PMETAC)–poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA) diblock polymer brushes, mounted on a generally oleophilic substrate surface. Using a Martini 3 coarse-grained model combined with a constant-pH hybrid Monte Carlo Molecular-Dynamics (MC/MD) scheme, we vary grafting distance d ∈ {2.4, 3.0, 4.0} and the length of the pH-responsive PDMAEMA block LD ∈ {25, 50, 75}, while keeping the permanently charged PMETAC underlayer fixed (LM = 50). In its neutral, deprotonated state, PDMAEMA collapses into laterally heterogeneous domains; shorter LD and lower grafting density yield more numerous, smaller islands. When oil droplets are captured by the PDMAEMA block, their wrapping by the polymer and their deformation strengthen with increasing LD and with decreasing d, consistent with droplet–brush contact statistics. Upon acidification (PDMAEMA is positively charged), the release of oil droplets is generally robust and proceeds in near synchrony with PDMAEMA protonation: the MC/MD cycle at which polymer–droplet contacts vanish closely follows the cycle at which the degree of protonation reaches unity. Release slows with increasing LD and accelerates with decreasing grafting density, while strongly wrapped architectures retain more residual oil within the brush after detachment of the majority. A distinct failure case emerges at low grafting density and long PDMAEMA (d = 4.0 nm, LD = 75), where the droplet penetrates the brush and establishes persistent contact with the oleophilic substrate, preventing pH-triggered detachment. These results provide architectural guidelines for designing responsive brushes that balance robust capture with reliable release.

A social media driven model for evaluating coupled flood damage and resilience at a fine scale

Initial conditions for surface hopping trajectories from the VSCF-Wigner distribution

In trajectory surface hopping (TSH) simulations, initial conditions are typically generated using harmonic Wigner distributions, which assume independent harmonic normal modes. While this assumption fails for anharmonic systems, it remains unclear under which conditions harmonic Wigner sampling becomes unreliable in photochemical simulations and whether anharmonicity alone is a sufficient criterion for reconsidering the use of harmonic Wigner sampling. In the present study, we introduce a sampling strategy based on vibrational self-consistent field (VSCF) theory to construct a VSCF Wigner quasiprobability distribution that incorporates anharmonic effects while retaining mode separability. Analytical expressions are derived in both harmonic and distributed Gaussian bases, enabling the implementation in TSH simulations. The method is applied to malonaldehyde and methyl hydroperoxide, which exhibit moderate and strong anharmonicity, respectively. For malonaldehyde, VSCF-based Wigner sampling and harmonic Wigner sampling yield similar results, indicating that harmonic Wigner sampling remains reliable despite the anharmonicity. In methyl hydroperoxide, where torsional motion significantly influences the excited-state character, VSCF Wigner sampling yields results comparable to those of the quantum thermostat approach while offering a computationally efficient and systematically improvable route to initial-condition sampling.

Prevalence and associated factors of pseudoexfoliation syndrome among cataract patients attending comprehensive specialized hospitals in Northwest Ethiopia

The XPS of azines: A comparative study

A detailed analysis is presented of the X-ray Photoelectron Spectroscopy (XPS) of thin films of three azines: pyrazine, pyridine, and pyrimidine. This includes not only the binding energies of the various core ionizations but also their intensities. A major focus is to compare our theoretical predictions with our measured XPS for N(1s) and C(1s) as a basis for assigning the features and for justifying the broadening parameters that must be applied to the theoretical results. The C(1s) XPS of pyridine and pyrimidine are significantly broadened because of unresolved XPS for their inequivalent C atoms. The extent of the binding energy (BE) shifts and the XPS intensities for the unique C atoms, which are responsible for this broadening, are obtained from the theory. The additional broadening parameters to be applied to the theory to enable comparison with measured XPS are discussed in terms of lifetime broadening, experimental resolution, and BE shifts in different layers of the thin film. A novel feature of our analysis is that we have investigated and justified the broadenings necessary to make of the calculated BEs in order to fit the observed XPS spectra. The results presented have general value for extracting chemical and physical information from XPS.

The extraordinary importance of self-avoiding behavior in two-dimensional polymers: Insights from large-deviation theory

Some recent work pointed out the usefulness of taking a large-deviation perspective when trying to extract anything resembling a macroscopic order parameter from a computer simulation. In this paper, we note that the end-to-end distance of polymers is such an order parameter. The presence of long-ranged excluded volume interactions leads to significant qualitative differences between the conformations of two- and three-dimensional polymers, some of which are difficult to quantify in computer simulations of realistic (off-lattice) polymer models. However, we show here that phenomena such as the greatly enlarged non-Hooke’s-law elasticity present in 2D are straightforward to extract from simulation using a large-deviation framework—even though simulating that nonlinearity is tantamount to simulating a fourth order susceptibility. The large-deviation perspective includes both a set of thermodynamic-like tools suitable for studying finite-size systems and a realization that an accurate description of the system’s average behavior needs to be consistent with how improbably large fluctuations would behave in that system. The latter is key because strong correlations are absent in this asymptotic large fluctuation regime, so the regime’s far-reaching effects can be analytically incorporated into the analysis of simulation data. That, in turn, allows us to direct the efforts of simulations away from difficult-to-sample rare-event domains. We illustrate this point with two- and three-dimensional Monte Carlo simulations (and exact results) on two models of a single isolated polymer chain: a chain of linked hard spheres, which has long-ranged excluded volume effects, and a discretized worm-like chain, which does not.

Solid-angle based nearest-neighbor algorithm adapted for systems with low coordination number

Nearest-neighbor identification is central to the analysis of local structure in condensed matter systems. The solid-angle-based nearest-neighbor (SANN) algorithm is widely used, offering a parameter-free and computationally efficient alternative to cutoff- or Voronoi-based methods. Unfortunately, however, in systems with low coordination numbers, SANN tends to identify many particles as neighbors that are outside the nearest neighbor shell. Here, we propose a solution to this problem. In particular, we propose a geometric modification, the “inscribed circle modification,” that resolves systematic overcounting in low-coordination lattices without introducing free parameters. We benchmark the modified SANN algorithm against Voronoi and the original SANN algorithm in crystalline, quasicrystalline, and heterogeneous systems and demonstrate that it provides robust and low-cost neighbor identification across both two and three dimensions.