Ensemble structure

By far the most common methods of studying aqueous interfaces by simulations are the Metropolis Monte Carlo (MC) technique and the classical molecular dynamics (MD) techniques. They will not be described here in detail, because several excellent textbooks and proceedings volumes (e.g., [2-8]) on the subject are available. In brief, the stochastic MC technique generates microscopic configurations of the system in the canonical (NYT) ensemble the deterministic MD method solves Newton s equations of motion and generates a time-correlated sequence of configurations in the microcanonical (NVE) ensemble. Structural and thermodynamic properties are accessible by both methods the MD method provides additional information about the microscopic dynamics of the system. 

Figure 13.8 Schematic operation of a two-station rotaxane as a controllable molecular shuttle, and idealized representation of the potential energy of the system as a function of the position of the ring relative to the axle upon switching off and on station A. The number of dots in each position reflects the relative population of the corresponding coconformation in a statistically significant ensemble. Structures (a) and (c) correspond to equilibrium states, whereas (b) and (d) are metastable states. An alternative approach would be to modify station through an external stimulus in order to make it a stronger recognition site compared to station A.

Active Ensemble Structures for Selective Oxidation Catalyses at Surfaces... 

Molecular-level design of catalytic ensemble structures on surfaces in a controllable manner, based on new chemical concepts and strategies regarding composition or structure, provides a promising opportunity for the development of novel and efficient catalysts active for selective oxidation. Novel strategies and concepts for the creation of active ensemble structures on flat and porous surfaces may emerge from self-assembly and in situ transformation of precursors immobilized on the surfaces, with the aid of in situ characterization by sophisticated physical techniques [1-6]. 

The obtained optimally superimposed complexes are automatically annotated in terms of the complex composition homo- and hetero-multimeric receptors, catalytic metal ions as well as cofactors and their analogs are automatically identified based on the consistency of each of these features throughout the ensemble. Compositional and conformational differences between the individual ensemble structures are recorded. Where applicable, symmetry neighbors are generated and taken into account. 

Once we have computed the final alignment, we are ready to calculate a consensus structure from this alignment. Here, we explicitly use structure information for the calculation of the alignment. Hence, the calculation of the consensus structure should be based on these ensemble structures. 

Many discussions of natural kinds in the philosophical literature treat the macroscopic as the domain of common sense and the microscopic as the domain of science. Johnson (1997), for example, makes this claim in his distinction between chemical kinds and manifest kinds. Although I will not be saying much about chemical treatments of the macroscopic properties of water, these are very important. While there may be such a thing as a manifest kind, it is important to see that chemical kinds can also be individuated at the macroscopic level in virtue of the ensemble structures of substances. 

Fig. 6 Ensemble structural fitting to RDCs. Agreement between experimental and back-calculated RDCs for a one-state ensemble (left) and a three-state ensemble (right). Data include NH, NC, and phenyl CH (the latter two are normalized to NH). RDCs are collected in positive and negative gels. (Reprinted with permission from [78])...

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