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Our results suggest the existence of a binding energy between the vacancies and the impurity species.
The binding energy between the functional groups and the graphene sheets is mainly responsible for these findings.
On the bases of these results, we propose that the activity is controlled by the surface density of defects (for example, stability of Ru cations) rather than the binding energy between the substrate and oxygenated species.
The fact that the order in activity is inverse to the order in stability, suggests that the OER on SRO hkl) is controlled by the surface density of defects (for example, stability of Ru cations) rather than the binding energy between the substrate and the reactants, intermediates and products.
Furthermore, the modification effects of the ASO modification layer with different amino group contents (m:n ratio) were also evaluated in this study by calculating the binding energy between the ASO modifier and natural fiber, and the cohesive energy density and free volume of the ASO modification layer.
All of the available structures of the six scorpion toxins in the Research Collaboratory for Structural Bioinformatics Protein Data Bank determined by NMR were considered during the simulation, which indicated that the conformations of the toxin significantly affect both the molecular recognition and binding energy between the two proteins.
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This result suggested that doping the fluidizable oxygen carrier with Co decreases the metal-support interaction and the binding energies between the metals and the H2 molecules, with this playing a significant role to increase the activity of such material.
The binding energies between the binders and HA are associated with the cohesive energy density and viscosity of each of the binders, which are attributed to functional groups in the binders.
The binding energies between the stacking layers of the superlattices, calculated by the PBE-D2 method, are both relatively small, i.e., 0.277 eV/Ge and 0.195 eV/Si for the Ger/MoS2 and Sil/MoS2 superlattices, respectively (see Table 1).
Second, a 3D search grid was created by the use of AutoGrid algorithm[32] to evaluate the binding energies between the ligands and the EBNA1 receptors.
As we discuss in the Additional file 1, the equilibrium constants K as well as the cooperativity measures C are related to the binding energies between the transcription factors, the promoter and the RNA polymerase.
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