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  Nano Route, DFT, MEP, and SAR of Pyran-Functionalized Pyrimidinone Hybrids as Potent Larvicides Against Culex Pipiens; Biochemical and Antioxidant Assessments

  (Elsevier B.V., 2027) Farag, Basant; Abdel-Haleem, Mohamed; Chinnam, Sampath; Elhaty, Ismail A.; Abozeid, Shaimaa; Gad, Mohammed E.; Saeed, Aamer; Selim, Abdelfattah; Baz, Mohamed M.

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  In this study, the starting material of the pyran-functionalized pyrimidinone scaffold was synthesized using nanoscience-based techniques. Larvicidal screening was conducted on 3rd larvae of Culex pipiens, the primary vector of West Nile virus according to standard protocols at various concentration levels. Among the synthesized library, derivative 11 emerged as the most potent candidate, displaying the strongest insecticidal power with the lowest LC50 values of 103.19 µg/mL after 24 h and 76.29 µg/mL after 48 h, as well as LC90 values of 248.92 µg/ mL after 24 h and 195.75 µg/mL after 48 h. Biochemical assays revealed that larvae treated with the most potent derivative, 11, showed a notable decrease in important enzymatic activity in comparison to the control. Specifically, the most potent derivative, 11, inhibited AChE activity by approximately 50% and significantly suppressed both α- and β-esterase levels at the LC50 concentration. The study found that treated larvae showed major decreases in both protease and phosphatase digestive enzymes, which resulted in metabolic and digestive process disruptions. The assays revealed that whereas antioxidant enzyme activity declined, lipid peroxidation and total protein carbonyl content increased, which indicated severe oxidative stress and cell damage. The synthesized compounds exhibited promising antioxidant activity, with compound 11 showing the highest radical scavenging potential among the tested derivatives (IC₅₀ = 4.30 µg/mL), approaching the activity of the reference standard ascorbic acid (IC₅₀ = 3.98 µg/mL). Computational DFT studies showed that the synthesized derivatives have moderate electronic stability, with compound 11 displaying the smallest HOMO-LUMO gap and highest softness. The most reactive sites were identified by Fukui, MEP, and Mulliken studies. According to findings, the synthesized compounds are promising bioactive candidates for developing effective multiple roles.

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  Hydrogen-to-Ammonia Green Fuel Production via Haber–Bosch Process in a Solar–Wind Poly-Generation System: Design, Intelligent Optimization, and 4E Analysis

  (Elsevier Ltd, 2027) Alkhatib, Omar J.; Basem, Ali; Abed Balla, Hyder H.; Alanazi, Mohana; Albaijan, Ibrahim; Albalawi, Hind; Ali, H.Elhosiny; Jastaneyah, Zuhair; Fouad, Yasser; Mahariq, Ibrahim

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  Hydrogen offers a pathway to deep decarbonization; however, its storage and transport remain significant barriers for large-scale renewable energy systems. This study proposes a novel hybrid solar-wind poly-generation architecture that couples a two-level thermal cascading structure (supercritical CO2 Brayton and transcritical CO2 Rankine cycles) with a continuously operated water electrolyzer–Haber–Bosch ammonia loop, enabling stable renewable-to-hydrogen-to-ammonia conversion without auxiliary heating. This framework provides a highdensity and safe long-term energy storage solution. A comprehensive 3E analysis, operational CO2 emission avoidance assessment, and multi-objective optimization were performed using an ANN-based surrogate method coupled with the NSGA-II algorithm. Exergy and economic analyses identified the solar field as the main source of irreversibility and cost, contributing to 48% and 60.1%, respectively. The surrogate-based optimization reduced computational time by 97.63% relative to direct simulation (≈38 hours to 54 minutes) while maintaining accuracy. Under optimized operating conditions (solar field area of 5624.32 m2, compressor pressure ratio of 1.93, solar irradiance of 411.29 W/m2, wind speed of 7.98 m/s, and regenerator effectiveness of 0.86), the system achieved an exergy efficiency of 35.86%, ammonia production of 1.09 kg/h, and a payback period of 5.07 years under the adopted economic assumptions. The environmental assessment indicates 167.5 kg/h of CO2 emissions avoided, corresponding to an emission reduction cost of 4.02 $/h. A Dubai-based case study demonstrated conceptual feasibility under steady-state conditions. The results highlight the system’s theoretical potential as an advanced renewable-to-ammonia platform for high-density energy storage and multi-product output, subject to further dynamic validation and detailed engineering.

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  Integrated Biomass–Geothermal System for Decarbonized Ammonia Production Enhanced by a Supercritical CO2 Cycle: A Data-Driven Approach

  (Elsevier Ltd, 2027) Slimene, Marwa Ben; Basem, Ali; Farouk, Naeim; Hasan, Mohd Abul; Khlifi, Mohamed Arbi; Islam, Saiful; Atamuratova, Zukhra; Mukhitdinov, Otabek; Khudoynazarov, Egambergan; Mahariq, Ibrahim

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  Ammonia’s emergence as a carbon-free energy carrier calls for pathways that decarbonize both its synthesis and the electricity that powers it. This work proposes a hybrid biomass–geothermal platform for electricity and ammonia production. The configuration couples a gas turbine fueled by biomass-derived syngas with a supercritical CO2 Brayton cycle, a dual-flash geothermal configuration, and an ammonia synthesis loop. A comprehensive exergy accounting is performed alongside an environmental and techno-economic evaluation. Exergy destruction is dominated by the gas turbine and gasification train (70.6%, 4148.95 kW), followed by the geothermal subsystem (1188.14 kW) and the supercritical CO2 cycle (297.5 kW). A multidimensional parametric analysis is conducted to evaluate the sensitivity of system performance indicators to key decision variables. The split ratio of the supercritical CO2 cycle exhibits a non-monotonic influence on system behavior: both efficiency and net power generation increase until a split ratio of 0.74, beyond which thermodynamic mismatches reduce system performance. Under favorable geothermal temperature and pressure conditions, normalized CO2 emissions decrease to 28.47 kg/GJ. Conversely, increasing the gas turbine inlet temperature raises the levelized cost of electricity by up to 41%, while simultaneously reducing efficiency and increasing emissions. To efficiently explore the design space, artificial neural network surrogate models are integrated with a multi-objective particle swarm optimization algorithm. The optimal solution yields an exergy efficiency of 54.68%, an ammonia production rate of 592.9 kg/day, and a levelized cost of electricity of 6.47 cents/kWh. Scenario analysis indicates that the project net present value ranges from $2.29 million under conservative price assumptions to $6.53 million under favorable market conditions.

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  Lng/H2-Rich Syngas Centric Quad-Generation Plant with Hydrogen-Ready Self-Sustaining Feeder; Economic Evaluation, Cuckoo Search, and Mlp Neural Networks

  (Elsevier Ltd, 2027) Li, Yonghui; Basem, Ali; Abed Balla, Hyder H.; Khlifi, Mohamed Arbi; Zhang, Haibo; Alhumaid, Saleh; Alsharif, Nasser; Abdullaev, Sherzod; Fouad, Yasser; Mahariq, Ibrahim

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  The rising global demand for sustainable energy, coupled with the urgent need for low-carbon hydrogen production, presents a significant challenge for modern energy systems. Existing multi-generation frameworks often suffer from inefficient thermal resource utilization and high computational costs during optimization. To address these gaps, this study proposes a novel, thermodynamically integrated liquefied natural gas (LNG)-based quadgeneration system that synergistically combines LNG cold energy recovery with steam methane reforming, gas turbine power, steam Rankine and Kalina cycles, and a proton exchange membrane electrolyzer. To ensure highefficiency performance, a surrogate-aided optimization plan, utilizing a Multi-Layer Perceptron model tuned with Cuckoo Search algorithm, is developed to navigate the complex decision space. Simulation results demonstrate that the optimized system achieves a remarkable exergy efficiency of 56.50% and a primary energy saving ratio of 34.43%, while maintaining competitive specific CO2 emissions of 0.297 kgCO2/kWh. Economically, the optimal configuration yields a total unit cost of product of 10.84 $/GJ and an annual profit of 31.01 M $, with a dynamic payback period (DPP) of 3.34 years. Comprehensive sensitivity and uncertainty analyses revealed that LNG flow rate and gas turbine outlet pressure are the primary drivers of overall system performance. These findings indicate that the proposed framework offers a robust, technically feasible, and economically viable solution for industrial-scale quad-generation, providing a scalable pathway for enhancing resource efficiency and promoting sustainable hydrogen production in future energy infrastructures.

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  Neural Network-Based Framework for Fatigue Life Prediction of Natural Rubber Under Thermo-Oxidative Preaging Spectra

  (Elsevier Ltd, 2027) Hijazi, Ala; Al-Dahidi, Sameer

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  Thermo-oxidative (TO) preaging significantly influences the fatigue behavior of natural rubber (NR). However, existing artificial neural network (ANN) models are generally limited to isothermal aging conditions and cannot directly represent multi-temperature preaging spectra encountered in practical applications. This study develops a framework for extending ANN-based fatigue-life predictors trained exclusively using isothermal preaging data to multi-temperature aging histories. An optimized physics-informed neural network (PINN) is employed as the underlying fatigue-life predictor, incorporating a physics-based constraint to preserve the expected power-law behavior of the generated S-N curves. Two spectral-aging representations are investigated. The first converts the aging spectrum into an equivalent isothermal condition using weighted-mean-temperature or Arrheniusbased equivalent-time formulations. The second introduces an incremental-degradation representation in which the individual aging steps are evaluated sequentially while accounting for the preceding aging history. The optimized PINN achieves mean absolute percentage errors (MAPE) of 16.5% and 14.8% for unseen data within the training conditions and left-out isothermal aging conditions, respectively. For spectral preaging, the Arrhenius-based equivalent-time method substantially outperforms the weighted-mean-temperature approach. The proposed incremental-degradation representation provides the highest accuracy, with a MAPE of 6.13%, while preserving the sequential nature of the aging process and avoiding the need for a global reference temperature. Its predictions are also in close agreement with the experimental S-N curve and compare favorably with the analytical Neuhaus model, without requiring explicit determination of temperature and displacement dependent activation-energy parameters. The proposed methodology therefore provides a practical approach for extending isothermally trained ANN-based fatigue-life models to multi-temperature preaging histories.

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