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MF model

In this chapter, the DMC method will be introduced and its advantages and disadvantages will be analyzed. A detailed comparison with mean field (MF) modeling will be made. After that, a few models that are important to heterogeneous catalysis will be discussed. [Pg.738]

In the mean-field models, a first-order phase transition is observed for values of Y which are less than 10% below the value obtained by Ziff, Gulari and Barshad [60,61]. The main failure of the MF model is that it does not predict the second-order phase transition that is observed in the simulations. This is due to the complete neglection of spatial correlations. [Pg.763]

Figure 2.4. (a) Effect of finite COad surface hopping rate, /cdiff, on the shape of current transients. For high mobility, the kinetic MC simulations approach the transients of the MF model. With decreasing mobility a more pronounced current tailing arises at long times (parameters used in calculations tot = 0.3, kox = ls — 0.2 s fcf = 50 s and kox = 0.5 s ). (b) Potential dependence of fitted kinetic parameters for small nanoparticles (1.8 nm diameter). Transfer coefficients for nucleation, OHadformation, and recombination are indicated. [Pg.57]

As conclusion to this section, we have stressed the influence of the heterogeneous surface structure on catalyst activity. Two complementary model approaches have been presented that could be used to establish the effective value of 7 . Kinetic MC simulations provide the most versatile tool for exploring structure-reactivity relations. It allows incorporating detailed surface structures and finite mobilities of adsorbates. In the limit of high surface mobilities of adsorbates, an analytical MF model could replace the stochastic description. The MF model still accounts for the heterogeneous surface structure. It is, thus, more realistic for real catalyst systems in fuel cells than homogeneous surface models. [Pg.58]

The simplest CDLG MF model described above can be used as a starting point for generalizations along many different directions. One obvious direction is towards CDLG models of systems with more than one species. A general projection operator formalism exists for generating static lattice-gas Hamiltonians of such systems. Their... [Pg.263]

The excitations of the crystal that originate from the MF model may correspond with strongly anharmonic translational vibrations or librations of the molecules they may even correspond with hindered- or free-rotor states. They remain single particle excitations, however, which do not show any dispersion (i.e. wave vector dependence) in their frequencies. The simplest manner to obtain this dispersion is by the so-called Exciton Model or Tamm-Dancoff Approximation [76]. From the crystal ground state, which is a product of (known) MF states ... [Pg.412]

When the MF model is further separated by Eq. (42) the MF excitations are either translational (T) or librational (L). The indices k and k which label the rows and columns of the matrix 0(q) run over both types of excitations. The elements 0 q) and 0 q) correlate the translational and rotational vibrations of the individual molecules the resulting lattice vibrations are the translational phonons and the librons. These are coupled by the elements and (g), which contain the translation-... [Pg.414]

ReveUin R, Thome J. (2008) A theoretical model for the prediction of the critical hat flux in heated micro-channel. Int. J. Heat and Mass Transfer 51 1216-1225 Roach GM, Abdel-Khahk SI, Ghiaasiaan SM, Dowling MF, Jeter SM (1999) Low-flow critical heat flux in heated microchannels. Nucl Sd Eng 131 411 25 Robinson AJ, Judd RL (2001) Bubble growth in a uniform and spatially distributed temperature field. Int J Heat Mass Transfer 44 2699-2710... [Pg.323]

A model developed by Leksawasdi et al. [11,12] for the enzymatic production of PAC (P) from benzaldehyde (B) and pyruvate (A) in an aqueous phase system is based on equations given in Figure 2. The model also includes the production of by-products acetaldehyde (Q) and acetoin (R). The rate of deactivation of PDC (E) was shown to exhibit a first order dependency on benzaldehyde concentration and exposure time as well as an initial time lag [8]. Following detailed kinetic studies, the model including the equation for enzyme deactivation was shown to provide acceptable fitting of the kinetic data for the ranges 50-150 mM benzaldehyde, 60-180 mM pyruvate and 1.1-3.4 U mf PDC carboligase activity [10]. [Pg.25]

It was proposed that a Diels-Alder cyclization occurred during a polyketide synthase assembly of the bicyclic core of Lovastatin by Aspergillus terreus MF 4845." In vitro Diels-Alder cyclization of the corresponding model compounds generated two analogous diastere-omers in each case, under either thermal or Lewis-acid-catalyzed conditions (Eq. 12.37). As expected, the Diels-Alder reaction occurred faster in aqueous media. The cyclization half-life in chloroform at room temperature is 10 days while in aqueous media at either pH 5 or 7, the half-life drops to two days. [Pg.398]

The Doi-Edwards roptation model thus predicts that the width of1 the modulus plateau varies as the square of the molecular weight, or, in comparing different polymers that have different Mr values, as (M/Mf)1. Another way of stating this is to say that the monomeric friction factor has been increased by the factor Furthermore, since in general =... [Pg.94]

The cu-bonding model provides a more complete and fundamental description of hypervalent molecules that are often interpreted in terms of the VSEPR model.144 In the present section we examine some MX species that are commonly used to illustrate VSEPR principles, comparing and contrasting the VSEPR mnemonic with general Bent s rule, hybridization, and donor-acceptor concepts for rationalizing molecular geometry. Tables 3.32 and 3.33 summarize geometrical and NBO/NRT descriptors for a variety of normal-valent and hypervalent second-row fluorides to be discussed below, and Fig. 3.87 shows optimized structures of the hypervalent MF species (M = P, S, Cl n = 3-6). [Pg.293]

The occurrence of the set-up procedure in period i is denoted by the binary variable Wi (0 = no, 1 = yes). The production costs per batch are denoted by p = 1.0 and the cost for a set-up is y = 3.0. Demands di that are satisfied in the same period as requested result in a regular sale Mi with a full revenue of a = 2.0 per unit of product. Demands that are satisfied with a tardiness of one period result in a late sale Mf with a reduced revenue of aL = 1.5 per unit. Demands which are not satisfied in the same or in the next period result in a deficit Bf with a penalty of a = 0.5 per unit. The surplus production of each period is stored and can be sold later. The amount of batches stored at the end of a period is denoted by Mf and the storage costs are a+ =0.1 per unit. The objective is to maximize the profit over a horizon of H periods. The cost function P contains terms for sales revenues, penalties, production costs, and storage costs. For technical reasons, the model is reformulated as a minimization problem ... [Pg.187]

Here we will discuss two scenarios for the proto-neutron star cooling which we denote by A and B, where A stands for cooling of a star configuration with SC whereas B is a scenario without SC. The initial states for both scenarios are chosen to have the same mass Mi(A) = A(l>) for a given initial temperature of T = 60 MeV. The final states at T = 0, however, have different masses Mf(A) / Mf(B) while the total baryon number is conserved in the cooling evolution. The resulting mass differences are AM (A) = 0.06 M , A M(B) = 0.09 M and AM (A) = 0.05 Me, A M(B) = 0.07 M for the Gaussian and Lorentzian models, respectively. [Pg.348]

Daroczi B, Kari G, McAleer MF, Wolf JC, Rodeck U, Dicker AP (2006) In vivo radioprotection by the fullerene nanoparticle DF-1 as assessed in a zebrafish model. Clin. Cancer Res. 12 7086-7091. [Pg.18]

Gorodeski GI, Romero MF, Hopfer U, Rorke E, Utian WH, and Eckert RL [ 1994] Human uterine cervical epithelial cells grown on permeable support—a new model for the study of differentiation and transepithelial transport. Differentiation 56 107-118... [Pg.358]


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