Search Results (2,053)

Human cytomegalovirus (HCMV) remains a significant cause of morbidity in immunocompromised patients, necessitating the development of improved antivirals. Using an integrated in silico and in vitro approach, we identified a novel ligand (NL) as a letermovir analog with enhanced binding affinity and reduced cytotoxicity. A pUL56 terminase subunit model generated with AlphaFold 3 was used for the virtual screening of a 15,000-compound library. Among the 73 candidates with structural similarity to letermovir (Tanimoto ≥ 0.6), NL exhibited superior predicted binding affinity (ΔG_bind = −10.7 kcal/mol). In silico toxicity prediction (ProTox 3.0) classified NL as having low toxicity (class 4, LD50 ≈ 1000 mg/kg), which was confirmed in vitro, where NL demonstrated 158-fold less toxic (CC50 = 2.69 mg/mL) in MRC-5 cells than letermovir (0.017 mg/mL). Molecular dynamics simulations over 500 ns revealed that the pUL56-NL complex forms a more thermodynamically stable interaction, with a lower calculated free energy of binding (MMGBSA: −40.89 ± 7.40 kcal/mol vs. −32.76 ± 4.96 kcal/mol) and a narrower free energy landscape. These results establish NL as a promising, low-cytotoxicity candidate with enhanced target engagement, warranting further investigation as a potential anti-HCMV therapeutic. Full article

Helicobacter pylori urease is a key virulence factor and a validated target for anti-infective strategies. In this study, a comprehensive computational workflow was applied to identify potential urease inhibitors through a drug repurposing approach. A curated library was first filtered using permeability-related descriptors and multiparametric scoring. The resulting compounds were evaluated through ensemble and consensus docking across multiple protein conformations and docking engines, followed by XP rescoring, metal–ligand distance analysis, and molecular dynamics simulations. Binding stability and thermodynamic profiles were further assessed using MM-GBSA and well-tempered metadynamics. This integrative strategy led to the identification of several candidate compounds exhibiting favorable docking scores, stable coordination with the catalytic Ni²⁺ center, and consistent binding behavior during molecular dynamics simulations. Notably, selected compounds showed improved relative binding free energy profiles compared to reference inhibitors within the applied computational framework. Overall, this study provides a robust computational pipeline for urease inhibitor identification and highlights repurposed compounds as promising candidates for further experimental validation. Full article

Fungal decay fundamentally alters moisture transport in wood through complex bio-physical coupling mechanisms that remain poorly understood. Brown-rot fungi such as Coniophora puteana (Schumach.: Fr.) P. Karst. degrade wood through chelator-mediated Fenton (CMF) chemistry, producing hydroxyl radicals that depolymerise cellulose and hemicellulose before significant mass loss. This diffusion-dependent process requires elevated moisture content and leads to structural degradation. However, existing models fail to capture the interaction between boundary-driven fungal colonization, decay-induced property changes, and multi-phase multi-Fickian moisture redistribution, particularly the separate evolution of bound- and free-water phases during decay. Here, we present a transport-response bio-hygrothermal finite element model that couples boundary-driven Monod-type fungal colonization kinetics with multi-phase moisture transport (free water, bound water, vapor) in decaying wood. Although fungal biomass evolution is simulated via a reaction–diffusion equation, decay progression is not derived from biomass–substrate interaction but prescribed independently as an experimentally informed input. The model incorporates decay-modified sorption isotherms, permeability evolution, and boundary-driven biomass influx, along with associated moisture transport, into the governing equations. The model is validated against low-field nuclear magnetic resonance (LF-NMR) measurements of C. puteana decay in Scots pine over 35 days. The model successfully reproduces the experimentally observed moisture evolution: a peak free-water content of 50%–70% during weeks 1–2, followed by a progressive decline, while bound water remains remarkably constant despite advancing decay. Monte Carlo uncertainty quantification demonstrates hierarchical parameter control: bound water is governed solely by thermodynamic factors, while free water responds to interacting biological and physical processes. Time-resolved correlation analysis shows a fundamental transition from colonization-dominated (weeks 1–2) to transport-dominated (weeks 3–5) moisture control, quantitatively explaining the experimentally observed shift from accumulation to depletion. This transport-response framework for analyzing moisture behavior under externally defined decay progression establishes quantitative parameter hierarchies that may inform the development of future substrate-coupled bio-hygrothermal models. Full article

Efficient design of desalination and brine management systems, which are central to a water circular economy, requires accurate thermodynamic data such as the osmotic coefficient. This property is key to understanding salt behavior in aqueous solutions, directly impacting the energy efficiency and sustainability of treatment processes. This study presents a predictive framework that combines machine learning with the Gazelle Optimization Algorithm (GOA) to accurately estimate osmotic coefficients for various inorganic salt solutions. The GOA was employed to automatically tune the hyperparameters of two models: Decision Tree (DT) and Gradient Boosting Machine (GBM). Using a comprehensive dataset of 893 samples with 27 salt-specific parameters, the GOA-GBM hybrid model delivered the highest predictive accuracy, achieving an R² of 0.9734 on test data. The GOA-DT model also performed robustly (R² = 0.9260), providing a more interpretable alternative. By creating a reliable tool for predicting osmotic coefficients, this methodology enables more precise process simulation and optimization. This directly supports the development of energy-efficient desalination technologies and informed decision-making for water reuse and resource recovery. The integration of advanced digital tools like GOA with machine learning offers a powerful approach to enhancing process efficiency and environmental safety, contributing directly to the design of sustainable, circular economy-based water treatment solutions for industrial and municipal applications. Full article

This study presents the design, thermodynamic modelling, and numerical optimisation of a medium-scale (100 kW) transcritical CO₂ air-source heat pump water heater (ASHP-WH) intended to deliver 90 °C domestic hot water under sub-zero ambient conditions. A detailed component-sizing methodology was established and implemented in AMESim 2404 using REFPROP-based property calculations, with model convergence confirmed by the mass and energy balance closure. Parametric investigations covering the discharge pressure, refrigerant charge, ambient air temperature, and water outlet temperature were conducted through 140 steady-state simulations. The results show that the system achieved a heating capacity of 100–121 kW with a coefficient of performance (COP) of 2.7–3.3 across −15 °C to +10 °C ambient conditions. The optimal discharge pressure (≈11.2 MPa) and charge inventory (10 ± 2 kg) define a broad operating window that ensures COP stability (±2%) and avoids liquid carry-over. The exergetic efficiency remained above 0.75 throughout the tested climate range. Compared with published laboratory prototypes, the proposed 100 kW module demonstrates a superior performance at harsher sub-zero boundaries, highlighting its potential for commercial hot water and industrial applications. The findings provide actionable guidelines for component sizing, charge management, and adaptive pressure control, and establish a pathway from a numerical prototype to scalable field deployment of medium-scale transcritical CO₂ systems. Full article

Two-dimensional antimonene has recently emerged as a promising electrocatalytic platform; however, its oxygen reduction reaction (ORR) activity and modulation strategies remain largely unexplored. Herein, density functional theory (DFT) calculations are employed to systematically investigate ORR catalysis on antimonene co-doped with transition metal (TM) and nonmetal (C, P) dual atoms. The results reveal that Pd@C–Sb, Pt@C–Sb, and Pd@P–Sb exhibit remarkably enhanced ORR activity, delivering low overpotentials of 0.31 V, 0.32 V, and 0.38 V, respectively, significantly outperforming their single-atom-doped counterparts. Mechanistic analyses demonstrate that nonmetal dopants induce strong synergistic interactions with TM centers, leading to charge redistribution and effective regulation of the TM d-band center, which optimizes the adsorption energetics of key ORR intermediates. Notably, the number of d-electrons of TM atoms is identified as a reliable electronic descriptor governing intermediate binding strength and catalytic activity. Furthermore, ab initio molecular dynamics simulations confirm the excellent thermodynamic stability of the optimized dual-atom catalysts. This work elucidates the atomic-scale origin of synergistic enhancement in dual-atom-doped antimonene and provides a rational design strategy for high-performance ORR electrocatalysts based on two-dimensional main-group materials. Full article

Constant composition expansion (CCE) experiments provide critical relative-volume and density information describing the thermodynamic behavior of reservoir oils and gases under varying pressure. These properties are vital inputs for hydrocarbon reservoir engineering, as they impact how oil and gas move through the reservoir during production. However, the need for specialized personnel, high-end equipment and measures taken to ensure safety in handling high pressure fluids often render the CCE experiments expensive and slow. This work introduces a Local Interpolation Method (LIM), a proximity-informed, end-to-end CCE fluid properties prediction Artificial Intelligence (AI) model that leverages domain expertise and synthetic Pressure–Volume–Temperature (PVT) data archives that mimics the actual data. The AI model generates surrogate CCE behavior for new fluids, thereby reducing the need for completing laboratory CCE measurements when sufficiently similar fluids exist in the available archive and neighborhood support is strong. Each new fluid is embedded in a compositional–thermodynamic descriptor space, and its response is inferred from a small neighborhood of thermodynamically similar fluids. Within this locality, the LIM combines hybrid local interpolation for key scalar properties (such as saturation-point quantities and expansion endpoints) with shape-preserving reconstruction of monophasic and diphasic relative-volume curves, enforcing continuity at saturation and consistency between relative volume, density and compressibility. The workflow operates purely at inference time and does not require case-specific retraining. Application to a curated archive of CCE tests shows that LIM reproduces key CCE features with very good agreement to existing data across a range of fluid types, indicating that proximity-based AI modeling can substantially reduce reliance on new CCE experiments while maintaining engineering-useful agreement for compositional simulation workflows. Under leave-one-out evaluation on 488 CCE tests, mean curve-level Mean Absolute Percentage Error (MAPE) is 0.07% for monophasic relative volume and 0.07% for monophasic density. For well-supported neighborhoods (Tiers 1–3, n = 376), mean MAPE is 0.04% for both, with 2.65% for derived compressibility and 1.78% for diphasic relative volume. The workflow is automated in software to facilitate reproducible inference on operator-owned archives and can reduce turnaround time and laboratory burden in well-supported neighborhoods. The proposed AI model uses available experimental data owned by each operator and does not use others’ data while respecting the data privacy and data ownership. Full article

This study presents a fully integrated process for the flexible conversion of biogenic waste into synthetic natural gas (bio-SNG) and electricity centred on a 100 kWth dual concentric bubbling fluidised bed steam gasifier. The raw syngas is processed in a high-temperature gas cleaning section, and the resulting clean, H₂-rich syngas is directed to three alternative downstream configurations: (i) conventional methanation, (ii) enhanced methanation with external H₂ supplied by a reversible solid oxide cell (rSOC), and (iii) electricity generation via the same rSOC operating in fuel cell mode. The overall process is modelled in Aspen Plus, in which the gasification section is constrained by experimentally derived syngas data, while downstream units are described through thermodynamic and kinetics-based models. Methanation is simulated using a plug-flow reactor model based on validated kinetic expressions, while the rSOC operating in electrolysis and fuel cell mode is modelled using performance parameters of commercial stacks. A plant-wide heat integration strategy based on composite curve analysis is implemented to maximise internal heat recovery and minimise external utilities. The enhanced methanation configuration enables the production of bio-SNG with high methane content (up to 93.3 vol.% dry, N₂-free), with a yield 0.72 kg/kg_Biomass and a fuel efficiency of 70.1%. In electricity production mode, the system reaches an electrical efficiency of 43.1% with complete elimination of auxiliary fuel through thermal integration. These results demonstrate the capability of a single integrated plant to flexibly switch between fuel synthesis and power generation, enhancing adaptability to fluctuating electricity and methane market conditions while maintaining high efficiency. Full article

Accurately determining the thermal transmittance (U-value) of glazing systems plays a pivotal role in building energy conservation. This study establishes an explicit analytical model and conducts a systematic parametric analysis to elucidate the heat transfer mechanisms and key influencing factors governing the U-values of both single-pane and insulating glass. Based on fundamental thermodynamic principles and blackbody radiation laws, numerical iterative models are developed and validated against WINDOW and Fluent software simulations, with deviations consistently below 3.8%. A comprehensive parametric analysis quantifies the effects of glass thickness, cavity width, surface emissivity, and indoor/outdoor heat transfer coefficients. The results reveal that: (1) while U-values decrease approximately linearly with increasing glass thickness, they exhibit a non-monotonic relationship with cavity width, identifying an optimal cavity width of approximately 16 mm for air-filled insulating glass units; (2) surface emissivity exerts the most significant influence on the U-value, with cavity-facing surfaces demonstrating the greatest sensitivity (up to 81% variation), whereas outdoor surface emissivity shows negligible impact; (3) the U-value displays greater sensitivity to variations in the indoor heat transfer coefficient compared to outdoor conditions. Based on the parametric analysis under standard winter conditions, a preliminary design hierarchy is proposed for energy optimization: prioritize Low-E coatings on cavity surfaces, followed by cavity width optimization near 16 mm, and finally consider increasing glass thickness. The validated models and quantitative insights establish a benchmark calculation method for U-value analysis. These findings offer theoretical guidance and a prioritized optimization pathway for the preliminary design of energy-efficient glazing systems, particularly under standard winter conditions. Full article

The growing need to mitigate climate change has accelerated the development of Carbon Capture, Utilization, and Storage (CCUS) technologies, where the safe transport of supercritical CO₂ (sCO₂) through pipelines is a key challenge. The flow behavior in such systems is strongly influenced by phase-change processes under transient conditions such as decompression and heat transfer and is further complicated by the presence of impurities (e.g., N₂, CH₄, and Ar). These impurities modify thermodynamic properties and phase boundaries, thereby affecting the overall flow dynamics. In this study, the influence of impurities on leakage, mass flow rate, and decompression wave propagation in sCO₂ pipelines is investigated using computational fluid dynamics (CFD) simulations. A real-fluid model (RFM) implemented in the CONVERGE CFD solver is employed, with a tabulation-based approach to accurately capture thermodynamic and transport properties across multiphase regimes. The simulations were validated against available experimental data and performed for varying impurity concentrations to assess their impact on key flow variables, including pressure, temperature, and wave speed. Although simplifying assumptions were used, the results are in fairly good agreement with experimental observations and provide a better understanding of the phase behavior induced by impurities during transient decompression. Additionally, the effects of outlet geometry, pipeline configuration, and the choice of equation of state are examined, highlighting their influence on the predicted flow response. The validity of the RFM modeling framework is further demonstrated by simulations of a large-scale pipeline configuration representative of industrial conditions, which will serve as a benchmark for future improvements. Full article

The presence of source rock with a high concentration of kerogen is not a sufficient condition for petroleum formation, as maturation requires specific thermodynamic conditions. In this study, the artificial maturation of organic matter was investigated through hydrothermal treatment simulating the vaporization–condensation zones associated with in situ combustion and steam-assisted recovery processes. The experiments were conducted under an inert nitrogen atmosphere at 250–350 °C to reproduce oxygen-depleted thermal environments where hydrothermal reactions dominate. The results demonstrate that the bitumoid yield increases with temperature, reaching a maximum of 4.44 wt.% at 300 °C, followed by a decline at 350 °C due to secondary cracking. At the same time, gas generation increases significantly, with a more than five-fold rise in total gas yield between 250 and 350 °C. In parallel, the H/C atomic ratio of kerogen decreases from 1.17 in the initial sample to 0.52 at 350 °C, indicating progressive aromatization and advanced catagenetic transformation. These changes are accompanied by the conversion of high-molecular-weight kerogen into resins, asphaltenes, and subsequently lighter hydrocarbons. The study provides experimental evidence for the effectiveness of hydrothermal processes in inducing kerogen transformation under inert conditions, offering insights into the mechanisms governing artificial maturation in unconventional reservoirs. Full article

Waste tyres, with their high carbon content and heating value that is greater than that of coal and biomass, present a potential feedstock for energy recovery. Similarly, automotive paint sludge (APS) is a hazardous waste rich in volatile and inorganics compounds, making it difficult to dispose of safely, but it also has potential for thermochemical conversion. Gasification is a thermochemical process which can turn such wastes into syngas, a mixture mainly composed of carbon monoxide and hydrogen that can be utilized to generate power and produce liquid fuels. To deal with challenges of single feedstock gasification, co-gasification combines two or more feedstocks, taking advantage of synergistic interactions to enhance syngas yield and overall efficiency. In this work, Aspen Plus simulation software is used to develop a model for the co-gasification of waste tyres and automotive paint sludge. Sensitivity analysis was performed with the aim of investigating and optimizing the overall process conditions of waste tyre and APS co-gasification. This study investigated the effect of air (ER) and water feed (SFR) and blend ratios on the adiabatic reaction temperature, product gas composition and heat value of the product syngas. Optimal operating ranges were identified as ER = 0.35–0.40 and SFR = 1.0–1.2 for tyre gasification, ER ≈ 0.50–0.55 for APS-only gasification, and ER = 0.40–0.48 with SFR = 0.8–1.0 for co-gasification blends. Adiabatic temperatures under recommended conditions were typically 700–800 °C. The LHV of syngas decreased with increasing ER, SFR, and APS fraction, falling from ~13 MJ/kg for tyre gasification to below 10 MJ/kg for APS-rich cases due to oxidation and dilution by CO₂ and ash. No positive synergistic effect in syngas quality was observed under thermodynamic equilibrium conditions. APS primarily acted as an ash-rich, low-carbon diluent, reducing CO concentration, heating value and adiabatic temperature. However, potential catalytic interactions from APS mineral matter, which are not represented in the equilibrium model, may produce synergistic effects in practical gasifiers. Full article

Accurate prediction of CO₂ diffusion in multicomponent fluids is crucial for efficient enhanced oil and gas recovery (EGR) and carbon capture, utilization and storage (CCUS) operations. Conventional experimental methods struggle to accurately reproduce diffusion processes under reservoir conditions and provide limited insight into molecular-scale mechanisms. Therefore, a detailed microscopic understanding of CO₂ diffusion in complex fluids is urgently needed. In this study, the diffusion behavior and underlying mechanism of the CO₂-CH₄-H₂O system under reservoir temperature and pressure conditions were explored using both experimental techniques and molecular dynamics (MD) simulations. The results indicate that at 354.15 K, the diffusion coefficients follow the order D_CH4 > D_CO2 > D_Fick and decrease with increasing pressure. Higher CO₂ concentrations and water content lead to a reduction in D_CO2. Gravity exhibits a relatively minor influence, slightly enhancing D_CO2 while marginally reducing D_CH4. Near the critical point, a significant decrease in the thermodynamic factor indicates drastic changes in thermodynamic properties. Furthermore, the presence of water promotes CO₂ enrichment at the gas-water interface, consequently reducing both D_CO2 and D_CH4. This work provides valuable insights into bulk-phase transport in multicomponent aquifer systems under reservoir conditions and offers theoretical support for optimizing gas injection strategies and improving the efficiency of EGR and CCUS processes. Full article

Snowpack dynamics in continental climates are important for water-resource monitoring and snow water equivalent (SWE) estimation, yet the response of the snow depth–snow water equivalent (SD-SWE) relationship to changing thermodynamic and accumulation forcing remains insufficiently understood. This study develops a process-based framework to evaluate how moderate perturbations in air temperature and precipitation influence snowpack evolution and depth–mass coupling in representative snow regimes of northeastern Kazakhstan. SNTHERM (the Snow Thermal Model) simulations were combined with regression analysis, ANCOVA diagnostics, and bulk-density evaluation under controlled delta-change perturbations of air temperature (±1–2 °C) and precipitation (±5–10%). The results show that the SD-SWE relationship remains approximately linear within the tested perturbation range (R² ≈ 0.78–0.84), although its parameters are partially sensitive to precipitation-driven accumulation. Temperature perturbations mainly affect melt timing, seasonal persistence, and snow-density redistribution, whereas precipitation modifies snowpack mass and overburden, enhancing mechanical compaction and increasing the regression slope. These findings indicate that snow density is a key integrative state variable linking energy balance, phase change, and compaction processes. Under the tested conditions, snow depth remains a physically consistent proxy for SWE, although the conclusions are limited by the one-dimensional model structure, reanalysis-based forcing, and restricted observational coverage. Full article

The efficient recovery of low-enthalpy geothermal resources $(T\le 150$ °C) faces significant thermodynamic limitations due to thermal mismatch in evaporators when pure fluids are utilized. This study investigates low-GWP zeotropic mixtures (Pentane/Isobutane), optimized using the NSGA-II algorithm, to enhance both the efficiency and operational resilience of Organic Rankine Cycles (ORCs). The isothermal behavior of conventional fluids limits exergy recovery and increases the Levelized Cost of Energy (LCOE). To address this, an advanced simulation tool, “ORC Master Suite”, was developed and validated against recent literature. Exergetic efficiency and LCOE were simultaneously optimized under strict Pinch Point constraints. Results show that the low-GWP zeotropic mixture of Pentane/Isobutane (70/30% w/w) achieves a 15–25% increase in exergetic efficiency compared to pure fluids, mainly due to the temperature glide, which reduces irreversibilities. Despite the increase in required heat transfer area and the strict capital expenditure penalties associated with ATEX safety protocols for highly flammable hydrocarbons, the LCOE remained competitive against the reference fluid. Overall, low-GWP zeotropic mixtures not only improve thermodynamic performance but also exhibit higher operational resilience to geothermal source fluctuations, making them a promising and sustainable alternative for future geothermal power plants. Full article