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Rough substrates were modeled to have a Gaussian autocorrelation function [21], which is a simple model often used in many fields of surface physics [22].
Accordingly, gaseous substrates were modeled to dissolve more readily in the lower portion of the column.
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Potential and current density distributions, due to ohmic potential losses in thin (non-photo) anodes on poorly conducting fluoride-doped tin oxide coated glass substrates, were modelled.
Adhesive substrates are modeled by the Lennard Jones potential and a coarse grained contact model.
Equilibrium shapes of sessile liquid drops over horizontal substrates are modeled using smoothed particle hydrodynamics (SPH).
Subsequently, the process of adhesion or deadhesion of cells from substrates was modeled via peeling tests that are familiar in engineering design but is made more complicated by the biological interface and geometry involved [30], [31].
In the case where a reaction R was explicitly controlled, the effect of increasing speed of the reaction on the steady-states of substrates was modeled by adding a negative influence from the node v(R) to the node quantity(M) of the substrates.
The 3′-terminus of the primer DNA strand and β,γ-substituted dNTP substrate were modeled by 3′-hydroxy tetrahydrofuran and β,γ-substituted methyl triphosphate, respectively, in a complex with a Mg2+ ion and three explicit water molecules.
The fertility of the new substrates is modelled by multilinear regressions for the main soil properties.
In this model, glutathione reductase [ GR, eqn (20)] was modelled with an irreversible, two-substrate generic rate expression [ 30], whereas GSH-oxidation and the reduction of the deglutathionylated substrate were modelled with mass-action kinetics [eqns (20)–(22)] as described previously [ 20].
The coating and substrate are modeled as an infinite layer and a semi-infinite substrate respectively.
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