The Bragg peaks are seen to broaden significantly resulting in an overlap between neighboring peaks like the group of 221, 310 and 311. In this example the size distributions were determined by transmission electron microscopy(lead sheet) (PEI; see Fig. 3.7(a) and (b)), and were found to be rather monodisperse, i.e. the standard devia- tion of the size distribution function amounted to only 7% of the average size. Rather often, polycrystalline samples exhibit pronounced variations in crystallite size with grains including dislocation networks, stacking faults, twin planes and other lattice faults. These distortions can severely complicate the interpretation of the peak broadening. In such complex problems like the microstructure of solids the interpretation of the results strongly depends on underlying model assumptions. A set of formulas is presented in the following, which relate microstructure parameters and line profile parameters. When applying these formulas, it should always be kept in mind that they are only valid for some restricting assumptions on the microstructure, i.e. within the validity range of the underlying model. The models should be subjected to mistrust and results derived from them should only be given with simultaneously indicating the basic assumptions. This section is con- cemed with models that neglect any broadening due to microstrain and other lat- tice distortions, but solely account for small crystallite size. The presentation starts by assuming a polycrystalline sample to be comprised of crystallites having all the same shape and dimension. Although this is a purely hypothetical case, it allows or the introduction of the relevant quantities and the derivation of the Scherrer equation. The discussion is restricted to the case of cubic materials.
One source of line broadening is due to the finite dimensions of coherently scattering domains. The effect is illustrated by the example of the profile of an a-phase Co film shown in Fig. 3.7(c) [24[. Ferromagnetic materials(lead glass) like Co, Fe, FePt, etc., with small crystallite sizes are currently investigated with respect to their application in ultrahigh-density recording. However, for crystallites that are too small the magnetic state could switch from ferromagnetic to superparamagnetic and the magnetic polarization within each magnetic domain would be governed by thermal fluctuations. Crystallite sizes thus have to be carefully controlled in the development of new thin-film recording media. The full &2B pattern shown in Fig. 3.7(c) has been measured for a sample with an average crystallite size of 11 nm, while the peaks in the inset were recorded for decreasing crystallite sizes of 9, 7, 5, and 3 nm. The Bragg peaks are seen to broaden significantly resulting in an overlap between neighboring peaks like the group of 221, 310 and 311. In this example the size distributions were determined by transmission electron microscopy(lead sheet) (PEI; see Fig. 3.7(a) and (b)), and were found to be rather monodisperse, i.e. the standard devia- tion of the size distribution function amounted to only 7% of the average size. Rather often, polycrystalline samples exhibit pronounced variations in crystallite size with grains including dislocation networks, stacking faults, twin planes and other lattice faults. These distortions can severely complicate the interpretation of the peak broadening. In such complex problems like the microstructure of solids the interpretation of the results strongly depends on underlying model assumptions. A set of formulas is presented in the following, which relate microstructure parameters and line profile parameters. When applying these formulas, it should always be kept in mind that they are only valid for some restricting assumptions on the microstructure, i.e. within the validity range of the underlying model. The models should be subjected to mistrust and results derived from them should only be given with simultaneously indicating the basic assumptions. This section is con- cemed with models that neglect any broadening due to microstrain and other lat- tice distortions, but solely account for small crystallite size. The presentation starts by assuming a polycrystalline sample to be comprised of crystallites having all the same shape and dimension. Although this is a purely hypothetical case, it allows or the introduction of the relevant quantities and the derivation of the Scherrer equation. The discussion is restricted to the case of cubic materials.
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GENERAL APPROACH TO HUMAN FACTORS IN THE MEDICAL DEVICE DEVELOPMENT PROCESS TO REDUCE HUMAN ERRORS6/5/2013 The occurrence of human errors can be significantly reduced by making human factors an integral part of the medical(cheap medical equipment) device development process from the concept phase to the production phase as shown in Figure 4.2. During the concept phase, the human factors specialist works with market researchers, helps develop and implement questionnaires, conducts interviews with potential users of the device, evaluates competitive devices, and performs analysis of industry and regulatory standards. The specialist also examines the proposed operation of the potential device with respect to educational background, skill range, and experiences of the intended users and identifies the possible use environments of the device under consideration. During the allocation of functions and preliminary design phase, both the human factors specialist and the design professionals determine which device functions will be automatic and which will require manual points of interface between humans and the device. More specifically, the points of interface are those operations where humans have to monitor and control so that the desired output or feedback from the device is obtained. The analysis of the preliminary design is performed with respect to the device operating environment and the skill level of the most untrained user. Usually, this task is performed by considering the drawings or sketches of the operational environment and gauging reactions of potential users. After evaluating the device, during the preproduction prototype phase, the prototype is built or updated for additional evaluation and market testing. The market test and evaluation phase involves not only the actual testing of the device, but also a thorough examination of the feedback received from the market test by human factors, marketing, and engineering professionals. During the final design phase, the device design is finalized by incorporating any human-factors-related changes generated by the marketing, test, and evaluation. In the production phase, the device is produceddisabled products) and put on the market. Nonetheless, during this phase the human factors specialist usually monitors the device performance, conducts analysis of the proposed design changes, and assists in the development of user-related training programs. |
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