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nanostructures; this chapter, however, generally only provides an overview of carbon structures, such as fullerenes and nanotubes. It is worth noting that there are R&D e orts underway to integrate nanoscience and electronics. From an electronic switching perspective, nanotechnology devices operate faster than larger mesoscopic components (the mesoscopic scale ranges from 10 to 10,000 nm, that is, 0.01 10 m). Today s computers use conventional electronics that require costly fabrication techniques; molecular electronics has the potential to shrink devices to the nanoscale with improvements in the power consumption and speed pro les. Techniques have already been developed for the production of structures on a molecular level by suitable sequences of chemical reactions or lithographic techniques (e.g., the latter allows one to manipulate individual atoms on surfaces using a variant of the atomic force microscope*). These and other techniques have been used to manufacture, for example, high-density data storage devices. One is also interested in developing nanocomputers: computers whose fundamental components measure only a few nanometers in size; at press time state-of-the-art computer components are no smaller than about 40 100 nm. Researchers believe that nanoscale devices may lead to computer chips with billions of transistors, instead of millions which is the range in today s semiconductor technology [93, 94, 95]. This may eventually allow one to develop quantum computers. Quantum computers and memory devices are expected to have applications in cryptography and information technology within a few years [45, 96]. These applications will be explored in 5 (nanophotonics) and 6 (nanoelectronics).

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(Continued) Careful analysis of deconvoluted CDA-FZeb mass spectra revealed three peaks separated by 264 Da, the mass of FDHU, which were not found in spectra of the protein alone:

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The method validation experiments should be well planned and laid out to ensure ef cient use of time and resources during execution of the method validation. The best way to ensure a well-planned validation study is to write a method validation protocol that will be reviewed and signed by the appropriate person (e.g., laboratory management and quality assurance). The validation parameters that will be evaluated will depend on the type of method to be validated. Analytical methods that are commonly validated can be classi ed into three main categories: identi cation, testing for impurities, and assay. Table 3 lists the ICH recommendations for each of these methods. Execution of the method validation protocol should be carefully planned to optimize the resources and time required to complete the full validation study. For example, in the validation of an assay method, linearity and accuracy may be validated at the same time as both experiments can use the same standard solutions. A normal validation protocol should contain the following contents at a minimum: (a) (b) (c) (d) (e) Objective of the protocol Validation parameters that will be evaluated Acceptance criteria for all the validation parameters evaluated Details of the experiments to be performed Draft analytical procedure

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Since the objectives are of a con icting nature, it is hard to de ne an optimal solution. Even if one could de ne strict optimality, there is no way of nding an optimal solution in reasonable time. In practice the planners are satis ed with a suboptimal solution.

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In fact, conduction currents are non-reciprocal in all active devices of interest. Sometimes it is possible or desirable that the conduction currents also be expressed in terms of the charges inside the device. If this is the case, the conduction currents are expressed as Ic1 = Ic1 (V1 , V2 ) = ki Q1 (V1 , V2 ) + kmr Q2 (V1 , V2 ) Ic2 = Ic1 (V1 , V2 ) = kmf Q1 (V1 , V2 ) + ko Q2 (V1 , V2 ) (3.18)

Both degree of solvation and deviation from a compact spherical shape increase Rs and hence f so that unexpected o values for s20,w can be used to detect and quantify solvation and shape effects. The analysis of more complicated experimental systems such as interacting systems (e.g. macromolecule-ligand) and multi-component systems (in which several boundaries are formed during sedimentation) is more complex but is facilitated by the availability of computer programs for data analysis which incorporate mathematical models for such systems. Fitting of experimental data to these models allows rapid estimation of quantities such as stoichiometry and binding constants for ligands (Example 7.3). More information on the analytical approaches applicable to such complex systems may be obtained from the excellent reviews cited in the bibliography at the end of this chapter. 7.2.8 Sedimentation Equilibrium Analysis

So far, we have computed the maximum output power delivered to a resistive load by the current source, and we also have determined the corresponding optimum resistive load; we have also computed the power corresponding to other resistances, assuming that the ampli er operates at the beginning of compression, that is, at the limit operating input power level where the current and voltage waveforms begin to be distorted. We now compute the output power delivered to the load resistance if the reactances are not resonated, that is, if the load seen by the current source is not purely resistive. Let us assume a load curve as in Figure 4.19. This load curve corresponds to a load resistance as in the case of voltage-limited output power (case (a), dotted line in Figure 4.19) with an added shunt susceptance. The

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