Classifications schematic representation

Fig. 1. Schematic representation showing the basis for classification of toxic effects into local and systemic by single or repeated exposures.

Figure 14 Schematic representation of the electronic effects in mixed-valent species according to the Robin-Day classification. Here the charge has been assumed to be positive as in ferrocenium ions...

Figure 10 is a schematic representation of this classification. The vertical lines represent excitation from the highest filled,... 

Figure 12-1. Classification of O-H - O hydrogen bonding situations of different strengths and schematic representation of corresponding potential energy curves along the O-H stretching coordinate.

This schematic representation is of course simplified, rather like the suggested classification of magnesite and other basic refractories published by Biggar and Weymouth (1976) on the basis of phase composition in the system MgO — CaO —.4I2O3—Si02- The latter authors distinguish the following materials ... 

Fig. 1 Schematic representation of modes of immobilization of enzymes. (Dotted lines indicate potential alternative uses/ classifications.)...

A precise definition of the flowability of a powder is only possible with several numbers and curves, derived from a family of yield loci of the powder (measured with a shear cell) - see section 4 for further detail. Jenike23 proposed a simpler classification, according to the position of one point of the failure function (at a fixed value of the unconfined yield strength, say 5 lbf (22.3 N) with the Jenike shear cell, i.e. 3112 Pa or 65 lbf/ft2) with respect to the flow factor line (straight line through the origin, at a slope l///where//is the flow factor) - see Fig. 8 for a schematic representation of this. 

This categorization of vibrational modes characteristic of a crystal is not too dissimilar to the classification that would be made for an isolated molecule containing N atoms. However, one must keep in mind that in a crystal the total number of quantized vibrational states (or phonons ) is actually 3NM where M is the number of primitive cells in the crystal. Each phonon has a characteristic energy, (q), for a given wave vector q = 27t/X, where X is the wavelength of the quantized plane wave in the crystal. When coy(q) is plotted as a function of wave vector, the familiar dispersion curve distribution of vibrational states of a crystal is obtained where /(= 1. . . 3N) labels the branches. A schematic representation is shown in Figure 1 for a KN3 type azide for two directions in the... 

Figure 3.1 Schematic representation of adsorption isotherms of (a) ii and (b) iV types according to BDDT classification.

Table 1. Schematic representation of the Infrared Group Frequencies classification...

The aim of this classification was to unquestionably classify any calorimeter within only one main class. The relationship existing between the sample temperature and that of the surrounding thermostat seems to be an appropriate criterion . For that purpose, a clear distinction is made between two parts of the calorimeter, namely (i) the sample together with the container with which it is in good thermal contact and (ii) the surrounding thermostat. To make this distinction clear, these two distinct parts are shown on the schematic representation of 11 types of calorimeters. This finally leads to the following classification ... 

Figure 1 Schematic representations of the CFTR and the effects of common mutations on expression, (a) Organization of the wild-type CFTR. CFTR is a transmembrane protein of 1480 residues that contains 12 transmembrane-spanning regions, revealing six predicted extracellular loops. An extended loop between transmembrane-spanning regions 6 and 7 contains two nucleotide-binding folds and a region designated the R domain, (b) Schematic based on the proposed classification of CFTR mutations by Welsh and Smith (29).

Figure 1 Schematic representation of general SP classifications (a) linear main-chain SPs (b) multivalent macromolecular assemblies and (c) hierarchical supramolecular assemblies.

Figure 2 Schematic representation of classifications of noncovalent interactions that have been used to develop SPs (a) self-complementary one-component binding, (b) complementary two-component binding, and (c) complementary three-component binding.

Figure 3.3 Schematic representation of the pore size distribution of different siiicate-based materials and their classification according to the iUPAC nomenclature.

Figure 2. Schematic representation of the characterization of the folding pattern of a space curve r(t), associated with a configuration Rj. The left-hand-side drawing represents the sphere S enclosing the space curve r. To the right we indicate how the sphere can be subdivided into regions according to the shape classification of the space curve. Each of the letters indicates a different shape type of the shape descriptor (e.g., a new knot vector K).

Carbonate Fuel Cell), and SOFC (Solid Oxide Fuel Cell). An exception to this classification is the DMFC (Direct Methanol Fuel Cell) which is a fuel cell in which methanol is directly fed to the anode. The electrolyte of this cell is not determining for the class. Table 1.1 compares the different types of fuel cell systems [2, 5-8]. A schematic representation of a fuel cell with reactant and product, and ions flow directions for these types of fuel cells are shown in Figure 1.2 [6]. 

Figure 3 Schematic representation of the channel and carrier mechanism and classification of the different types of transport.

Figure 27.2 Classifications of biosensors schematic representation with various combinations of physical and biological elements.

Figure 10.1 Classification of microelectrodes (A) random array, (B) ordered array, (C) paired electrode, schematic representation of a double band assembly, (D) interdigitated array, schematic presentation of IDA electrodes vertically arranged (E) linear array, (F) three-dimensional array, Utah electrode array (reprints from reference (28)). (for colour version see colour section at the end of the book).

FIGURE 36.2 Schematic representation of a multilayer network. For waste plastic classification, spectral reflectance values provide inputs (IN) into the network. Two hidden layers HIDl and HID2 are used, composed of a variable number of nodes. The ontpnts of the network (OUT), indicate the resin from which the spectrum was collected. 

As the word implies, photopolymerizations are polymerization reactions that take place under the specific stimulus of light. From an organizational point of view, the various types of photopolymerizations can be classified according to the classical mechanistic and kinetic designations that are used to distinguish between polymerization reactions in general. Figure 1 shows a schematic representation of the basic classifications of photopolymerizations. These basic classifications form the outline for the major divisions within this chapter. 

FIGURE 3.1 Schematic representations of the general classification of porous solids. A typical construction procedure for MOFs is given in the bottom panel [7]. (Reproduced from Li, J. R. et al., Chem Rev 112, 869-932 (2012), with permission). 

Figure 2. Schematic representation of the structural classification of galectins according to Hira-bayashi and Kasai [2] monomeric (A) or dimeric (B) prototype galectins, tandem-repeat galectins (C), and chimeric galectins (D)...

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