Search Results (12,855)

Background: Triple−negative breast cancer (TNBC) remains among the most aggressive and therapeutically unresponsive subtypes due to the absence of ER, PR, and HER2 targets. Casein Kinase II (CK2), a pleiotropic serine/threonine kinase overexpressed in TNBC, represents a compelling target for rational drug design. Methods: Here, we present an AI−integrated benchmarking framework combining virtual drug discovery, molecular dynamics simulations, machine learning−driven QSAR modeling, and quantum−mechanical electronic structure analysis to identify potent CK2 inhibitors from natural product chemical space. Results: A validated XP docking protocol (ROC–AUC = 0.748) screened ~480,000 compounds, yielding seven hits, with superior binding to the reference inhibitor CX−4945. Among these, Anastatin B, 3,4,8,9,10−pentahydroxy−dibenzo−[b,d]pyran−6−one, Rhein, and aloe emodin acetate exhibited highly favorable docking scores (−11.6 to −13.1 kcal mol⁻¹) and stable 200 ns binding dynamics, reflected by RMSD ≤ 2 Å and compact Rg trajectories. MM−PBSA/MM−GBSA analyses confirmed robust thermodynamic stability, while DFT−derived HOMO–LUMO gaps (3.8–4.3 eV) suggested optimal electronic reactivity for kinase inhibition. Machine learning QSAR models demonstrated strong predictive performance, with the best stacking models achieving test R² ≈ 0.69 and consistent cross−validation performance (CV R² ≈ 0.66–0.69), supporting reliable prediction of pIC₅₀ values and prioritization of top−ranked scaffolds. Conclusions: Collectively, this integrative framework bridges AI−based learning and biophysical validation, establishing a reproducible paradigm for de novo CK2 inhibitor discovery in TNBC. Full article

This study aimed to clarify how molecular structure regulates the dissolution and transient gel-layer behavior of polyacrylamide-based dry-powder drag reducers for slickwater fracturing. In the Materials Studio 2020 software, molecular dynamics simulations were performed on five representative homopolymers, including: polyacrylamide (PAM), polyacrylic acid (PAA), poly(2-acrylamido-2-methylpropane sulfonic acid) (PAMPS), poly(N-vinylpyrrolidone) (PNVP), and poly [2-(acryloyloxy)ethyl]trimethylammonium chloride (PDAC). The results show that in pure water, PAA exhibits the strongest thermodynamic driving force with an interaction energy of −1005.5 kcal/mol and the lowest solvation free energy of −373.289 kcal/mol. Quantitative correlation analysis established that solvation energy and hydrogen bond density are primary predictors of macroscopic performance, yielding a correlation coefficient of R² > 0.94. Experiments confirm that optimized AM/AA (7:3) and AM/AMPS (5:5) anionic copolymers achieve stable viscosity within 120 ± 5 s and 160 ± 8 s, respectively, representing a 60% reduction in dissolution time compared to conventional industrial PAM homopolymers. The polarity, charge density, and chain flexibility of functional groups jointly regulate polymer dissolution behavior. Anionic groups significantly improve dissolution performance by enhancing intramolecular electrostatic repulsion and hydration. Full article

Hybrid gas separation technologies combining different physicochemical mechanisms represent a promising approach for the efficient treatment of complex natural gas mixtures. In this work, a hybrid process integrating gas hydrate crystallization and membrane gas separation was investigated for the upgrading of multicomponent natural gas-containing hydrocarbons (C₁–C₄), acid gases (CO₂ and H₂S), and inert components. Polysulfone hollow-fiber membranes were fabricated, and their gas transport properties were experimentally determined using an eight-component quasi-real natural gas mixture under elevated pressure conditions. The obtained mixed-gas permeance values were used as input parameters for the development of a detailed mathematical model of a hollow-fiber membrane module implemented in the Aspen Custom Modeler. The model was applied to simulate membrane separation of both gas- and hydrate-derived streams produced by the gas hydrate crystallizer. Simulation results were analyzed in terms of hydrocarbon composition, acid gas removal efficiency, and hydrocarbon recovery as a function of the stage-cut. The modeling predictions were validated experimentally using a laboratory membrane module integrated with the gas hydrate crystallization unit. Good agreement between the experimental data and simulation results was observed for all major components. The deviation between modeled and experimental concentrations remained small, while the discrepancy in hydrocarbon recovery was higher and reached approximately 10–20%, which is attributed to the cumulative uncertainty of flow rate and composition measurements. These results confirm the adequacy of the developed model. The hybrid process demonstrates strong complementarity between the thermodynamic selectivity of hydrate formation and the transport selectivity of membrane separation, enabling efficient removal of acid gases while maintaining acceptable hydrocarbon recovery. The results indicate that the proposed gas hydrate–membrane hybrid process is a promising strategy for advanced natural gas purification and upgrading. Full article

The temporal dynamics and statistical properties of air nanobubbles (NBs) in ultrapure water were investigated using nanoparticle tracking analysis (NTA). Statistical analysis of NB lifetimes reveals a strong correlation between bubble size and persistence. The mean bubble diameter increases rapidly from ~100 nm for short-lived detections to a characteristic size of about 500 nm for bubbles surviving longer than 40 frames, after which the size remains approximately constant. The population of detected NBs decreases monotonically with increasing lifetime, approximately following an exponential decay. Temporal analysis of the cumulative population yields a scaling exponent of ~0.6, indicating correlated fluctuations rather than independent stochastic events. Spatial observations confirm that NBs are separated by micrometer-scale distances, excluding direct bubble–bubble interactions. The combined statistical and imaging results support a picture in which NBs behave as dynamically coupled gas domains embedded within localized dissolved-gas microenvironments. The observed non-monotonic population dynamics cannot be explained by irreversible dissolution alone. Instead, the data indicate that NBs undergo continuous cycles of dissolution and replenishment. Within this framework, localized gas micro-domains, potentially exhibiting non-extensive behaviour, evolve dynamically and act as transient reservoirs. Upon bubble disappearance, these regions persist and diffuse, while their interaction may locally restore conditions for nucleation. This mechanism provides a physically consistent explanation for the observed cyclic behaviour and the apparent persistence of NBs beyond classical predictions. Full article

Solar interfacial evaporation is promising for freshwater production, yet thermodynamic energy limits and mass transfer attenuation in high-salinity environments restrict practical applications. To address these challenges, a 3D high-efficiency evaporator is developed by cross-linking a hydrophilic composite gel onto a macroporous sponge scaffold. This spatially decoupled architecture enables fundamental water-state regulation and efficient environmental heat harvesting. Specifically, hydrophilic functional groups in the gel network reduce the equivalent enthalpy of vaporization of water to 1181.8 J g⁻¹. Simultaneously, the 3D columnar structure induces a sidewall cold sink effect to extract additional ambient thermal energy. Through this synergy, the PCPH delivers a remarkable apparent evaporation rate of 8.59 kg m⁻² h⁻¹ under one standard sun. Furthermore, interconnected macropores within the sponge establish excellent convective pathways for rapid ion diffusion. Consequently, the device operated continuously for 8 h in a 10 wt% NaCl solution without significant blockage and decreased key metal ion concentrations in 3.5 wt% simulated seawater by 4 to 5 orders of magnitude. The purified water fully satisfies World Health Organization standards. This study offers an innovative strategy to surpass conventional photothermal bottlenecks and design highly durable water treatment materials. Full article

Periodic charging–storage–discharging induces cyclic variations in temperature and pressure inside the rock cavern, forming a complex thermo-hydro-mechanical (THM) coupling problem that impacts the structural stability and energy storage efficiency of the cavern. In this study, a thermodynamic model of CAES rock caverns incorporating heat exchange and air leakage was established, enabling accurate characterization of temperature and pressure variations in the cavern during charging–storage–discharging. Based on this, the influences of heat transfer coefficient and charging temperature on the thermodynamic process were discussed. The primary reason for the pressure and heat losses during the high-pressure storage stage was analyzed. Finally, a long-term performance simulation of a CAES cavern over a 365-day operation period was conducted. Results indicated that: (1) Temperature, pressure, and air leakage rate all presented a trend of “up-down-down-up”, synchronized with the four operation stages of charging, high-pressure storage, discharging, and low-pressure storage; (2) during high-pressure storage, continuous heat exchange between compressed air and the cavern wall causes a reduction in pressure and temperature. The magnitude of this reduction decreases with increasing heat transfer coefficient but increases with rising charging temperature; (3) after 365 days of operation, the air leakage rate decreased from 10⁻² magnitude to 10⁻³, with increased pore pressure in the surrounding rock reducing the pressure gradient, thereby impeding air leakage from the cavern under the assumption of constant permeability. Full article

While cost-effective austenitic stainless steels like AISI 304 are utilised in intermediate-temperature concentrated solar power (CSP) components, autogenous welding can compromise their structural integrity. This work investigates the corrosion behaviour of autogenous TIG-welded AISI 304 joints exposed to commercial molten solar salt at 550 °C for up to 1350 h under static conditions. Gravimetric and microstructural analyses revealed a stochastic bimodal breakaway oxidation mechanism. After an initial transient passivation regime (0–650 h) attributed to the formation of a protective Fe₃O₄/FeCr₂O₄ bi-layer, a sharp kinetic acceleration occurred. This localized breakdown was synergistically catalysed by trace chloride impurities, which triggered deep pitting along the microsegregated dendritic networks of the weld metal. Furthermore, due to severe X-ray attenuation under massive late-stage oxides, definitive proof of sensitisation was established using the standardised ASTM A262 Practice A topographic evaluation. The appearance of continuous ditch structures only in the heat-affected zone (HAZ) suggests severe intergranular anodic dissolution. This failure is thermodynamically driven by unmitigated residual tensile stresses, highlighting that the long-term reliability of these components is interpreted to be dictated by the localised, asymmetric breakdown of the weldment rather than uniform global oxidation. Full article

We explore a phenomenological extension of the Polyakov–Nambu–Jona-Lasinio (PNJL) model by introducing a curvature-sensitive effective contribution to the Polyakov-loop potential, motivated by the hypothesis that the non-perturbative QCD vacuum in the confined phase may retain a residual sensitivity to cosmic expansion. In a spatially flat FLRW background, this modification reduces to a term proportional to $\alpha {(H/H_0)}^{d}f(\Phi ,{\Phi }^{*})$, which naturally vanishes in the deconfined regime and behaves as an effective dynamical vacuum component at late times, without invoking a fundamental cosmological constant. The construction provides an effective thermodynamic description of the QCD sector within an adiabatic framework and introduces a minimal phenomenological extension characterized by the exponent d and the amplitude parameter $\alpha$. We analyze the cosmological implications at the background level and compare the model with low-redshift observations, including cosmic chronometers, Type Ia supernovae, HII galaxies, and quasars. Using Bayesian Monte Carlo techniques, we constrain the model parameters and compare its performance with the ΛCDM. Our results indicate that the modified PNJL cosmology provides a statistically competitive fit to current data while allowing small departures from the ΛCDM within observational uncertainties. We also investigate the impact of the coupling on the QCD phase diagram and the critical end point. The framework offers a tractable effective approach to connect confinement physics with late-time cosmology and suggests directions for further theoretical development in QCD under curved backgrounds. Full article

Accurate echo classification is crucial for Millimeter-wave Cloud Radar (MMCR) data quality control. Existing approaches, however, often struggle to generalize across complex scenes or lack physical interpretability. Here we propose PhySNet, a physics-guided network that combines thermodynamic priors with spatiotemporal radar features, embedding physical information across the full pipeline from feature extraction to final outputs. Based on the coupling between the lifting condensation level (LCL) and daytime clear-air echo heights, and the lagged correlation between nocturnal clear-air echo heights and their daytime counterparts, we design a physics-constrained gating block (PCGB). The PCGB extracts thermodynamic states and evolution trends from collocated surface observations, generating a clear-air echo probability map that weights the initial radar features. Building on this, we add a parallel regression branch of effective-clutter-height (ECH). This branch fuses thermodynamic features with radar spatiotemporal features, enabling the model to learn to predict the clear-air echo boundary. Finally, we apply an adaptive height filter using the predicted ECH sequence to refine the classification results. Evaluated on a multi-region, multi-season dataset from China, PhySNet achieves a probability of detection (POD) of 98.28% for meteorological echoes and 95.87% for clear-air echoes, outperforming conventional methods. By coupling data-driven learning with physical rules, our approach provides a high-accuracy, interpretable solution for cloud radar clear-air echo identification. Full article

During natural gas production and transportation, multi-stage pressure regulation is often required to meet downstream pressure demands, resulting in substantial waste of residual pressure energy at high-pressure wellheads. This study focuses on high-pressure natural gas at the wellhead of the XC gas well in western Sichuan. Based on thermodynamic and exergy analysis, Aspen HYSYS was employed to simulate residual pressure power generation processes, and a systematic comparison was conducted between single-stage and multi-stage expansion schemes. Under operating conditions of an inlet pressure of 20 MPa, an inlet temperature of 70 °C, and a flow rate of 50 × 10⁴ m³/d, the influence of operating parameters on power generation performance was analyzed. The results indicate that power output increases with increasing natural gas flow rate and inlet temperature but decreases with increasing outlet pressure. Under large pressure differential conditions, single-stage expansion is unable to meet the requirements of high-pressure wellhead residual pressure power generation due to excessive temperature drop and limitations in existing expander performance. On this basis, two-stage, three-stage, and four-stage expansion power generation processes were further developed, and the effects of intermediate pressure selection on power output, heating demand, and pressure energy recovery efficiency were systematically examined. The results show that operating under equal expansion ratio conditions enhances pressure energy utilization. By comprehensively comparing power generation performance, heating power requirements, and economic feasibility, the two-stage expansion scheme was identified as the most favorable option under the investigated operating conditions, providing a practical reference for process design and engineering applications of high-pressure natural gas wellhead residual pressure power generation. Full article

Recycling spent lithium-ion batteries (LIBs) is critical for resource sustainability and carbon neutrality. This work presents a green strategy in which NaA zeolite is used to preferentially recover lithium from leachate of spent NCM111 batteries, combined with temperature-regulated stepwise separation of transition metals. Benefiting from the distinct hydrated ionic radii and charge density between Li⁺ and divalent metal ions, NaA zeolite selectively adsorbs Ni²⁺, Co²⁺ and Mn²⁺, leaving Li⁺ in the raffinate. Under optimized conditions, two-stage adsorption achieves 95.6%, 96.7% and 99.7% removal of Ni²⁺, Co²⁺ and Mn²⁺, respectively, with 11% Li⁺ co-adsorption. Thermodynamic analysis reveals that the adsorption process is endothermic and thermodynamically spontaneous. The interaction strength between metal ions and NaA zeolite follows the order Ni²⁺ > Co²⁺ > Mn²⁺, and ion exchange is identified as the dominant mechanism. It is determined that 96.8% of Mn²⁺ can be recovered at 0 °C, followed by the desorption of 93.5% of Co²⁺ at 90 °C, and the sequential separation of Mn, Co and Ni is realized. Three consecutive adsorption–desorption cycles demonstrate the acceptable reusability of the Ni-loaded NaA adsorbent. High-purity Li₂CO₃ (purity 96.7%, yield 93.5%), MnO₂ (purity 99.3%, yield 98.4%) and Co₃O₄ (purity 98.8%, yield 97.6%) are obtained from the corresponding solutions. This approach provides a scalable closed-loop pathway for full-component recovery of valuable metals from spent LIBs. Full article

The removal of excessive amounts of antibiotics from water systems is of great benefit due to their adverse effects on the ecosystems, living organisms and the persistent increase in antibiotic resistance cases. This study was focused on the adsorption of vancomycin from a simulated aqueous medium using seaweed, a sustainable and low-cost adsorbent. Also, the work focuses on assessing the influence of surface modification on adsorption behaviour and determining if chemical treatment provides improvements over untreated seaweed. In particular, chemically modified seaweed and raw (non-modified) seaweed were assessed as adsorbents suitable for removing traces of vancomycin from water, as gauged from the results of High-Performance Liquid Chromatography (HPLC). In addition, Scanning Electron Microscopy (SEM), Fourier Transform Infrared spectroscopy (FT-IR) and the pH point of zero charge (pHpzc) were used to measure the surface characteristics of these adsorbents. The degree of antibiotic adsorption was evaluated as a function of different factors, including the pH, adsorbent dosage, contact time, ionic strength and initial concentration of vancomycin. Thermodynamic parameters, such as the enthalpy change (∆H°), the entropy change (∆S°) and the free-energy change (∆G°), were calculated. The FTIR analysis indicates that functional groups, such as carbonyl and hydroxyl groups, were involved in the adsorption process, and their modification influenced adsorption behaviour. It was observed that the adsorption of vancomycin by the modified seaweed was slightly lower (±94%) compared to the level achieved for the raw seaweed (±97%). These figures were obtained with an initial concentration of vancomycin of 25 µg/mL, a pH of the aqueous solution of 7.0, an adsorbent dose of 0.2 g and a contact time of 120 min. The results showed that untreated seaweed exhibited slightly higher adsorption efficiency than the treated seaweed, suggesting that chemical modification might not have enhanced adsorption performance. The thermodynamic parameters suggested that the adsorption process was exothermic and that adsorption was favourable for the untreated seaweed and less favourable for the treated seaweed. Regeneration studies showed a decrease in adsorption efficiency over repeated cycles. Although the adsorption capacity is lower than that of advanced nanomaterials, the use of seaweed offers an advantage in terms of low cost, availability and environmental sustainability. The comparable efficiency of the modified and untreated seaweed adsorbent suggests that seaweed adsorbents can be used as viable bio-adsorbents for the decontamination of water. Full article

Starch synthesis is critical for crop yield and quality and is regulated and coordinated by a series of key enzymes encoded by starch synthesis-related genes (SSRGs). Although this process is well characterized in many crops, the genomic location and expression patterns of SSRGs in wheat remain unclear. Here, we performed a genome-wide analysis and identified 78 SSRGs in wheat, classified into the AGPase, SSS, GBSS, SBE, and DBE subfamilies. SSRGs within each subfamily showed conserved motifs and domain organization. RNA-seq analysis indicated that most SSRGs are expressed during early grain development. We further examined genetic variation in SSRGs across wheat and its progenitors using re-sequencing data. Diploid wheat showed greater genetic differentiation and diversity than tetraploid and hexaploid wheat. Five SSRGs exhibited significant haplotype differences between emmer wheat and common wheat; emmer wheat displayed diverse haplotypes, whereas common wheat showed a single dominant haplotype. Finally, starch characteristics differed between emmer wheat and common wheat in amylose content and thermodynamic properties, while viscosity, crystal structure, and morphology were largely similar. Overall, this study systematically characterizes SSRGs in wheat and provides insights for improving starch quality. Full article

Oxide perovskites with simultaneously large band gaps and high-static dielectric constants are of considerable interest for advanced microelectronics, dielectric devices, and energy storage applications, yet their discovery remains challenging because electronic insulation, lattice polarizability, and thermodynamic accessibility are strongly coupled and often mutually competitive. Here, we develop a physics-guided multitask learning framework for the joint prediction of the band gap and static dielectric response in chemically constrained single-perovskite oxide ABO₃ compounds. To ensure data fidelity and physical comparability, the learning space is strictly restricted to simple oxide ABO₃ perovskites from the Materials Project, while mixed-fidelity band gaps, heterogeneous dielectric definitions, and chemically inconsistent samples are excluded. The model integrates role-aware A-/B-site descriptors, perovskite-specific geometric and structural features, multitask prediction of Eg, εtotal, εelectronic, and εionic, explicit physical consistency constraints, auxiliary candidate classification, ranking learning, and reliability-aware screening with uncertainty and out-of-distribution control. Under B-site-grouped cross-validation, the framework achieves 97.4% accuracy, Recall of 96.5%, and an F1 score of 96.1%, while maintaining robust transferability on the independent JARVIS validation set. The results show that high-gap/high-k candidates occupy a chemically non-random subspace governed by B-site-centered electronic–lattice coupling, and that physically consistent multitask learning substantially improves both predictive coherence and candidate enrichment. More broadly, this study establishes a data-consistent, physics-constrained, and transferable paradigm for the intelligent discovery of functional oxide dielectrics. Full article

Given the increasing demand for low-phosphorus molten iron in high-value-added steel production and the rising phosphorus content in raw materials caused by the use of high-phosphorus ores in blast furnaces, the traditional converter single-slag process faces challenges such as high dephosphorization pressure, high slag consumption, and unstable endpoint control. This study systematically investigates the process principles and key influencing factors of the converter double-slag method (MURC process) as an efficient pretreatment technology for molten iron. Through thermodynamic analysis combined with industrial tests, the core process parameters affecting dephosphorization efficiency were identified, including temperature, slag basicity (R), iron oxide (T.Fe) content, and bottom-blowing stirring intensity. The results show that the optimal temperature during the dephosphorization stage is 1350–1400 °C, with slag alkalinity controlled at 1.6–2.0 and T.Fe content maintained at 19–23%. During the decarburization stage, the optimal temperature is 1620–1640 °C, and the final slag alkalinity should be increased to above 3.5. After applying the optimized “low-high-low” oxygen supply pattern and enhanced bottom-blowing stirring (0.04–0.20 Nm³/(t·min)), significant improvements were achieved in industrial practice on 180-t and 60-t converters. Lime consumption was reduced by more than 30%, the average endpoint phosphorus content decreased by approximately 0.005%, the phosphorus removal rate remained stable at above 90%, and the oxygen content in molten steel at the endpoint decreased by 50–100 ppm. This study provides a systematic theoretical basis and practical guidance for efficient and stable dephosphorization using the converter double-slag process. Full article