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Determining thermodynamic and kinetic conditions for natural gas hydrate formation is an interesting subject for many researches. At the present, suitable information including experimental data and the thermodynamic models of hydrate formation are available which predict the thermodynamic conditions of hydrate formation. Conversely, there is no sufficient study about the kinetics of natural gas hydrate and most of experimental data and kinetic models in the literature are incomplete. Artificial Intelligence (AI) having sub-branches such as artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) has been proved as a novel tool with acceptable accuracy for modeling of engineering systems. Therefore, this paper aims to investigate the kinetics of hydrate formation by predicting the relationship of growth rate of methane hydrate with temperature and pressure using ANN and ANFIS. This goal can also be achieved by solving complicated governing equations while artificial intelligence provides an easier way to accomplish this goal. The result has shown that ANIFS is a more potential tool in predication relationship of kinetics of hydrate formation with temperature and pressure in comparison of ANN in present work.

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Chemical mechanical polishing (CMP) slurries formulated of cerium oxide nanoparticles (CNPs) and polyacrylic acid (PAA) are widely used in shallow trench isolation (STI) application. The critical parameter in slurry formulation is the amount of PAA which determines the extent of its adsorption on CNPs and the excess amount. The adsorption of PAA on CNPs results in a softer surface and prevents defects such as scratches on the wafer surface. The excess PAA selectively adsorbs on silicon nitride, slowing down its etching as compared to silicon dioxide.1 Therefore, the understanding of PAA adsorption on CNPs in a slurry formulation is critical to predict the CMP slurry characteristics. In previous studies the adsorption of PAA on CNPs has been widely studied;2 however there is still a lack of understanding between adsorption behavior of PAA and the interfacial properties of CNPs. Here, we have systematically investigated the PAA adsorption on ceria slurries and correlating it to the CNPs interfacial properties. Three different commercial ceria slurries were obtained having similar hydrodynamic size, but different surface area as determined using BET. The catalytic properties of CNPs is assessed from the ratio of Ce3+/Ce4+  on its surface, determined using XPS.3 The surface functionalization of CNPs is evaluated from FTIR and NMR depicting three different types of organic moieties resulting in different zeta potentials. Lattice strain and grain size are another important parameter which signifies the internal energy of CNPs. These parameters were calculated using Williamson-Hall plots obtained from the XRD curve. The adsorption isotherm of PAA on CNPs is determined by measuring the carbon content in the supernatant separated from the CNPs using centrifugation. The carbon content is measured using TOC analyzer and the amount of PAA adsorbed on CNPs is calculated by mass balance equation. 1.             Ronay, M., Selective polishing with slurries containing polyelectrolytes. Google Patents: 2005. 2.             (a) Saraf, S.; Neal, C. J.; Das, S.; Barkam, S.; McCormack, R.; Seal, S., Understanding the Adsorption Interface of Polyelectrolyte Coating on Redox Active Nanoparticles Using Soft Particle Electrokinetics and Its Biological Activity. ACS applied materials & interfaces 2014, 6 (8), 5472-5482; (b) Kim, S.; So, J.-H.; Lee, D.-J.; Yang, S.-M., Adsorption behavior of anionic polyelectrolyte for chemical mechanical polishing (CMP). Journal of colloid and interface science 2008, 319 (1), 48-52. 3.             Pirmohamed, T.; Dowding, J. M.; Singh, S.; Wasserman, B.; Heckert, E.; Karakoti, A. S.; King, J. E.; Seal, S.; Self, W. T., Nanoceria exhibit redox state-dependent catalase mimetic activity. Chemical communications 2010, 46 (16), 2736-2738.