The investigation of methane storage at the Ni-MOF-74 material: a periodic DFT calculation.

To develop a high-performance methane storage material, an understanding of the mechanism and electronic interactions between methane and the material is essential. In this study, we performed detailed theoretical analyses to investigate the methane storage capacity of Ni-MOF-74 using a large-scale periodic DFT code CONQUEST. In a single pore of the unit cell, we considered three possible sites, iSBU, L, and P sites, where iSBU is the inorganic secondary building unit with a metal center, and L is the linker consisting of the organic building unit, while the P site is the vacuum site in the center of the pore. It shows that the methane molecule adsorption possesses the largest methane molecule adsorption energy on the iSBU site. Our calculations indicate that both C-HO and weak agostic interactions exist between the methane molecule and the iSBU site. The adsorption energy of one methane molecule on the iSBU site is in good agreement with previous experimental and theoretical studies. The calculation of the stepwise methane molecule adsorption shows that the first six methane molecules can first occupy the iSBU sites via C-HO and weak agostic interactions. The second six methane molecules are adsorbed on the remaining L sites, where the C-Hπ interaction becomes important, leading to the synergistic effect together with the C-HO interaction to enhance the adsorption energy of the methane molecule. Finally, it can adsorb up to sixteen CH4 molecules in a single pore of a unit cell at Ni-MOF-74. Moreover, we conducted DOS and EDD analyses, which clearly show that the interactions play a vital role in the adsorption of a methane molecule on Ni-MOF-74, especially the C-HO interaction.

Cloud-Based Breast Cancer Prediction Empowered with Soft Computing Approaches.

The developing countries are still starving for the betterment of health sector. The disease commonly found among the women is breast cancer, and past researches have proven results that if the cancer is detected at a very early stage, the chances to overcome the disease are higher than the disease treated or detected at a later stage. This article proposed cloud-based intelligent BCP-T1F-SVM with 2 variations/models like BCP-T1F and BCP-SVM. The proposed BCP-T1F-SVM system has employed two main soft computing algorithms. The proposed BCP-T1F-SVM expert system specifically defines the stage and the type of cancer a person is suffering from. Expert system will elaborate the grievous stages of the cancer, to which extent a patient has suffered. The proposed BCP-SVM gives the higher precision of the proposed breast cancer detection model. In the limelight of breast cancer, the proposed BCP-T1F-SVM expert system gives out the higher precision rate. The proposed BCP-T1F expert system is being employed in the diagnosis of breast cancer at an initial stage. Taking different stages of cancer into account, breast cancer is being dealt by BCP-T1F expert system. The calculations and the evaluation done in this research have revealed that BCP-SVM is better than BCP-T1F. The BCP-T1F concludes out the 96.56 percentage accuracy, whereas the BCP-SVM gives accuracy of 97.06 percentage. The above unleashed research is wrapped up with the conclusion that BCP-SVM is better than the BCP-T1F. The opinions have been recommended by the medical expertise of Sheikh Zayed Hospital Lahore, Pakistan, and Cavan General Hospital, Lisdaran, Cavan, Ireland.