Nucleation computer simulation

The entropically driven disorder-order transition in hard-sphere fluids was originally discovered in computer simulations [58, 59]. The development of colloidal suspensions behaving as hard spheres (i.e., having negligible Hamaker constants, see Section VI-3) provided the means to experimentally verify the transition. Experimental data on the nucleation of hard-sphere colloidal crystals [60] allows one to extract the hard-sphere solid-liquid interfacial tension, 7 = 0.55 0.02k T/o, where a is the hard-sphere diameter [61]. This value agrees well with that found from density functional theory, 7 = 0.6 0.02k r/a 2 [21] (Section IX-2A). 

Other computer simulations were made to test the classical theory. Recently, Ford and Vehkamaki, through a Monte-Carlo simulation, have identified fhe critical clusters (clusters of such a size that growth and decay probabilities become equal) [66]. The size and internal energy of the critical cluster, for different values of temperature and chemical potential, were used, together with nucleation theorems [66,67], to predict the behaviour of the nucleation rate as a function of these parameters. The plots for (i) the critical size as a function of chemical potential, (ii) the nucleation rate as a function of chemical potential and (iii) the nucleation rate as a function of temperature, suitably fit the predictions of classical theory [66]. 

Many other efforts were made to test the validity of the nucleation theories using computer simulations [68,69], and droplet formation was studied utihzing mean-field theory or density fimctional analysis [70-72]. 

Computer simulations combined with experiments have also shown that one can deduce from the fractal dimension the nature of nucleation and growth of particles and what chemical and physical mechanisms control the formation of particle aggregates. We consider this briefly before proceeding to other topics. 

Computer simulation using lattice models and energy landscape theory using abstract models predict that the fastest folding of small proteins should occur without intermediates and by an extended nucleation process. Stable intermediates slow... 

Computer simulations provide a means of examining the early stages of hydrate formation (nucleation) on a molecular level (Baez and Clancy, 1994 Radhakrishnan and Trout, 2002 Moon et al., 2003, 2005). Computer simulation has also been applied to study hydrate dissociation (Baez and Clancy, 1994 English and MacElroy, 2004) and the effects on dissociation kinetics of external electromagnetic fields (English and MacElroy, 2004). 

Hydrate nucleation is the process during which small clusters of water and gas (hydrate nuclei) grow and disperse in an attempt to achieve critical size for continued growth. The nucleation step is a microscopic phenomenon involving tens to thousands of molecules (Mullin, 1993, p. 173) and is difficult to observe experimentally. Current hypotheses for hydrate nucleation are based upon the better-known phenomena of water freezing, the dissolution of hydrocarbons in water, and computer simulations of both phenomena. Evidence from experiments shows that nucleation is a statistically probable (not deterministically certain see Section 3.1.3) process. 

Using Equations 3.3a and b, Englezos et al. (1987a) calculated the critical radius of methane hydrate to be 30-170 A. In comparison, critical cluster sizes using classical nucleation theory are estimated at around 32 A (Larson and Garside, 1986), while computer simulations predict critical sizes to be around 14.5 A (Baez and Clancy, 1994 Westacott and Rodger, 1998 Radhakrishnan and Trout, 2002). 

The temporal evolution of twins has been investigated with an aid of computer simulation by Wild et al, [84]. Figure 6.1 shows a result of a twin evolution on a (111) face of a cubo-octahedral crystal, assuming that a =1.75. Note that time t is in arbitrary units, and the crystal size is normalized. It is seen that a small twin on a (111) face at t = 0 laterally increases the area, reaches the adjacent (100) faces, and induces a secondary nucleation on the (100) faces. According to the computer simulation, there are three types of twin evolutions, as shown in Figure 6.2. The types and characteristics of the twins shown in Figure 6.2 are summarized below. Note that the definition Tjijk here is different from that of Section 5.5. 

An appealing approach to the study of nucleation is to observe it directly in a computer simulation using the method of molecular dynamics. It is evident that one cannot closely mimic experimental conditions, since computer time scales extend only over tens to hundreds of picoseconds. Thus deep quenches carried out at high quench rates are necessary to form supercooled liquids that have some reasonable chance of nucleating. Under such conditions glass formation is also observed, and in fact the first observation of nucleation was a chance event, although since that time more systematic studies have been carried out. 

NUCLEATION OF TWIN BOUNDARIES FOR RAPID TEMPERATURE QUENCH COMPUTER SIMULATION STUDIES... 

J. S. van Duijneveldt and D. Prenkel (1992) Computer-simulation study of free-energy barriers in crystal nucleation. J. Chem. Phys. 96, pp. 4655-4668... 

Computer simulations were also used to show that the crystallization nucleus is more likely to form in the subsurface than in the bulk phase of the water slab. This result can have far reaching atmospheric implications. It has been suggested that formation of an ice nucleus at the interface would be hampered by contamination of the surface by organic surfactants. The effect of the adsorbed material will surely propagate towards the subsurface as well, however it will be smaller than in the topmost layer. Therefore, the anthropogenic emissions should have an effect on the radiative balance of the Earth atmosphere. This effect should, however, be smaller than predicted using the assumption of surface nucleation. 

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