In the present work, we focus on the space-time isogeometric discretization of a parabolic problem with a nonlocal diffusion coefficient. The existence and uniqueness of the solution for the continuous space-time variational formulation are proven. We prove the existence of the discrete solution and also establish the a priori error estimate for the space-time isogeometric scheme. The non-linear system is linearized through Picard’s method, and a suitable preconditioner for the linearized system is provided. Finally, to confirm the theoretical findings, the results of some numerical experiments are presented.
The development of novel and efficient catalysts is an important theme in heterogeneous catalysis from both fundamental and applied research perspectives. The combustion of fossil fuels, the primary source of energy used today, contributes not only to energy problems but also to ocean acidification and global warming. The fixation of CO2 into value-added chemicals has become an extensive research topic in the scientific community. This study presents a novel approach through the development of a zinc complex immobilized on a base-functionalized mesoporous MCM-48 material, which enhances the activity of carbon dioxide utilization via cycloaddition of CO2 to epoxide. In this study, a zinc complex containing 2,9-dimethyl-1,10-phenanthroline and zinc chloride was synthesized and characterized using powder XRD. When this combination was anchored on a base-functionalized MCM-48 material, it demonstrated efficient catalytic activity for the utilization of carbon dioxide. The structure, phase integrity, shape, thermal stability, and presence of functional groups were determined using various analytical techniques, such as powder XRD, N2 adsorption-desorption, FT-IR, SEM, NMR, and thermogravimetric analysis. The resulting hybrid material was employed to chemically convert carbon dioxide to cyclic carbonates. Owing to the contributions from both the amine groups and the mononuclear zinc complex grafted onto the mesoporous surfaces, the catalytic performance of the hybrid material as a catalyst was found to be superior. A 93% conversion with 90% selectivity was achieved using a zinc complex immobilized on a base-functionalized MCM-48 material. The novelty of this work lies in the design of a zinc complex-immobilized mesoporous structure, providing the unique structural and electronic synergy between the Zn complex and the 3D ordered mesoporous support. This approach showed high catalytic efficiency for a solvent-free, cocatalyst-free hybrid system in CO2 utilization.
Growing concerns over the upward trend of global warming, driven by human-induced CO2 emissions, are fuelling the advancement of carbon capture and storage (CCU) projects around the world. Investigating porous materials, their structure-activity relationship, leads to their application as heterogeneous catalysts. This work aims preparation of a unique hybrid silicoaluminophosphate (SAPO) microporous material with a modified core-shell structure by post-synthetic base functionalization, followed by copper complex grafting. A novel mixed ligand copper (II) complex comprising 2,9-dimethyl-1,10-phenanthroline and copper nitrate was synthesized and characterized by single crystal XRD. The mononuclear Cu-complex encapsulated hybrid silicoaluminophosphate catalyst was characterized by PXRD, N2 adsorption-desorption, FT-IR, TGA, NMR and SEM techniques. Additionally, XPS analyses of modified SAPO-34 and the Cu-complex revealed structural and electronic details of aluminium sites and copper species showing Cu+ binding with SAPO-34, which is essential for epichlorohydrin ring-opening, providing insights into CO2 utilization mechanisms. Catalytic activity of this hybrid material was compared with pure SAPO-34 and SAPO-5, functionalized-only materials and without catalyst. The hybrid materials demonstrated up to 96 % efficiency in CO2 utilization at low pressure and at optimum reaction conditions. The novelty of the work lies in the prepared copper (II) complex and its immobilization in microporous support for excellent catalytic activity in CO2 utilization using minimum amount of catalyst.
With the rapid advancement of wire arc additive manufacturing (WAAM), assessing the sustainability of postprocessing operations such as machining has become increasingly important. This study investigates sustainability-oriented lubrication strategies using mono-nanofluids (MNFs) and hybrid nanofluids (HNFs) formulated from graphene nanoplatelets and hexagonal boron nitride dispersed in jojoba oil. Drilling experiments were performed on WAAM and wrought Ti-6Al-4V alloys to evaluate machinability and environmental performance. The results demonstrated that HNFs considerably improved machining responses, achieving reductions of nearly 30% in power consumption, over 40% in cutting forces, and around 50% in flank wear compared with dry conditions. A cradle-to-gate life cycle assessment (LCA) was further conducted to quantify the environmental impacts associated with each condition. The analysis revealed that machining WAAM components generated higher CO2 emissions and toxicity indicators than wrought machining, primarily due to increased energy demand. Nevertheless, the application of HNFs effectively mitigated these burdens, leading to approximately 35% lower CO2 emissions and notable decreases in human toxicity and resource depletion. Overall, the study highlights how tailored nanofluid lubrication can deliver measurable environmental benefits, bridging machinability improvements with sustainability goals in advanced alloy manufacturing.
The rapid proliferation of IoT devices and the growing need for real-time processing have enhanced the quality of life, but also introduced significant security vulnerabilities. While various security solutions exist to counter malicious activities, many fail to adequately address evolving threats. Consequently, there is a clear need for an intelligent system capable of simultaneously adapting to dynamic cyber risks and improving defensive performance. Keeping this in mind, this study developed an imperative hybrid IDS framework called CNN-MGOA by integrating a convolutional neural network (CNN) and multi-objective grasshopper algorithm (MGOA) to help identify intruders in IoT-based networks. Additionally, to more comprehensively capture the important features of network intrusion and improve the detection performance, the multi-objective grasshopper optimization algorithm (MGOA) is utilized in conjunction with a multi-class support vector machine. In this work, comprehensive experiments are conducted on three up-to-date benchmark datasets, including ToN-IoT, CIDD, and NSL-KDD. Experimental results show that our model achieves high detection accuracy of 99.89
