Responsive surfaces changes

SHG Optical second-harmonic generation [95, 96] A high-powered pulsed laser generates frequency-doubled response due to the asymmetry of the interface Adsorption and surface coverage rapid surface changes... 

One of the most effective ways to think about optimization is to visualize how a system s response changes when we increase or decrease the levels of one or more of its factors. A plot of the system s response as a function of the factor levels is called a response surface. The simplest response surface is for a system with only one factor. In this case the response surface is a straight or curved line in two dimensions. A calibration curve, such as that shown in Figure 14.1, is an example of a one-factor response surface in which the response (absorbance) is plotted on the y-axis versus the factor level (concentration of analyte) on the x-axis. Response surfaces can also be expressed mathematically. The response surface in Figure 14.1, for example, is... 

The initial simplex is determined by choosing a starting point on the response surface and selecting step sizes for each factor. Ideally the step sizes for each factor should produce an approximately equal change in the response. For two factors a convenient set of factor levels is (a, b), a + s, h), and (a + 0.5sa, h + 0.87sb), where sa and sb are the step sizes for factors A and B. Optimization is achieved using the following set of rules ... 

The EMIRS and SNIFTIRS methods provide the IR vibrational spectra (really the difference spectra - see later) of all species whose population changes either on the electrode surface or in the electrical double layer or in the diffusion layer in response to changing the electrode potential. Spectra will also be obtained for adsorbed species whose population does not change but which undergo a change in orientation or for which the electrode potential alters the Intensity, the position or shape of IR absorption bands. Shifts in band maxima with potential at constant coverage (d nax 6 very common for adsorbed species and they provide valuable information on the nature of adsorbate/absorbent bonding and hence also additional data on adsorbate orientation. 

Figures 5 and 6 show the response surfaces plotted for Property A and Property B, respectively. Note that two variables are plotted at once, with the values of the other variables fixed at levels chosen by the experimenter. The contours in the graph represent constant levels of the response. Fortunately, the computer allows rapid replotting for various levels of the fixed variables, as well as changing the identities of the fixed and floating variables, so that the entire design space can be investigated.

Note, however, there are two critical limitations to these "predicting" procedures. First, the mathematical models must adequately fit the data. Correlation coefficients (R ), adjusted for degrees of freedom, of 0.8 or better are considered necessary for reliable prediction when using factorial designs. Second, no predictions outside the design space can be made confidently, because no data are available to warn of unexpectedly abrupt changes in direction of the response surface. The areas covered by Figures 8 and 9 officially violate this latter limitation, but because more detailed... 

Murugesan K, Dhamija A, Nam IH, Kim YM, Chang YS (2007) Decolonization of reactive black 5 by laccase optimization by response surface methodology. Dyes Pigm 75 176-184... 

The properties of the el ectrical doubl e 1 ayer (EDL) have been the subject of considerable research (1,3,5,8,10). Unlike reversible electrodes, where surface potential is controlled and charge develops in response to changes in electrode potential, mineral surfaces develop potential in response to the formation of surface charge (8). On the surface of hydrous oxides, for example, hydroxyl groups... 

To analyze response surface designs, a model is fitted to the data for each response. Usually the results are visualized in response surface plots, showing the change in response as a function of two factors. " These plots allow deciding on the optimal conditions. However, as already mentioned in Section IV, these response surfaces seem not so useful when only small variations around the nominal conditions are examined. 

In reference 20, a typical robustness test is not performed, but a study on the influence of peak measurement parameters is reported on the outcome. The study is special in the sense that no physicochemical parameter in the experimental runs is changed, but only data measurement and treatment-related parameters. These parameters can largely affect the reported results, as shown earlier, and in that sense they do influence the robusmess of the method. The different parameters (see above) were first screened in a two-level D-optimal design (9 factors in 10 experiments). The most important were then examined in a face-centered CCD, and conclusions were drawn from the response surfaces plots. 

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