Dynamic data analysis

Shaw, L.A. Harrington, P.D., Seeing through the smoke with dynamic data analysis detection of methamphetamine in forensic samples contaminated with nicotine. Spectroscopy 2000, 15,40-45. 

Thermal Properties. Spider dragline silk was thermally stable to about 230°C based on thermal gravimetric analysis (tga) (33). Two thermal transitions were observed by dynamic mechanical analysis (dma), one at —75° C, presumed to represent localized mobiUty in the noncrystalline regions of the silk fiber, and the other at 210°C, indicative of a partial melt or a glass transition. Data from thermal studies on B. mori silkworm cocoon silk indicate a glass-transition temperature, T, of 175°C and stability to around 250°C (37). The T for wild silkworm cocoon silks were slightly higher, from 160 to 210°C. 

The comparison with experiment can be made at several levels. The first, and most common, is in the comparison of derived quantities that are not directly measurable, for example, a set of average crystal coordinates or a diffusion constant. A comparison at this level is convenient in that the quantities involved describe directly the structure and dynamics of the system. However, the obtainment of these quantities, from experiment and/or simulation, may require approximation and model-dependent data analysis. For example, to obtain experimentally a set of average crystallographic coordinates, a physical model to interpret an electron density map must be imposed. To avoid these problems the comparison can be made at the level of the measured quantities themselves, such as diffraction intensities or dynamic structure factors. A comparison at this level still involves some approximation. For example, background corrections have to made in the experimental data reduction. However, fewer approximations are necessary for the structure and dynamics of the sample itself, and comparison with experiment is normally more direct. This approach requires a little more work on the part of the computer simulation team, because methods for calculating experimental intensities from simulation configurations must be developed. The comparisons made here are of experimentally measurable quantities. 

In this review we put less emphasis on the physics and chemistry of surface processes, for which we refer the reader to recent reviews of adsorption-desorption kinetics which are contained in two books [2,3] with chapters by the present authors where further references to earher work can be found. These articles also discuss relevant experimental techniques employed in the study of surface kinetics and appropriate methods of data analysis. Here we give details of how to set up models under basically two different kinetic conditions, namely (/) when the adsorbate remains in quasi-equihbrium during the relevant processes, in which case nonequilibrium thermodynamics provides the needed framework, and (n) when surface nonequilibrium effects become important and nonequilibrium statistical mechanics becomes the appropriate vehicle. For both approaches we will restrict ourselves to systems for which appropriate lattice gas models can be set up. Further associated theoretical reviews are by Lombardo and Bell [4] with emphasis on Monte Carlo simulations, by Brivio and Grimley [5] on dynamics, and by Persson [6] on the lattice gas model. 

A rather crude, but nevertheless efficient and successful, approach is the bond fluctuation model with potentials constructed from atomistic input (Sect. 5). Despite the lattice structure, it has been demonstrated that a rather reasonable description of many static and dynamic properties of dense polymer melts (polyethylene, polycarbonate) can be obtained. If the effective potentials are known, the implementation of the simulation method is rather straightforward, and also the simulation data analysis presents no particular problems. Indeed, a wealth of results has already been obtained, as briefly reviewed in this section. However, even this conceptually rather simple approach of coarse-graining (which historically was also the first to be tried out among the methods described in this article) suffers from severe bottlenecks - the construction of the effective potential is neither unique nor easy, and still suffers from the important defect that it lacks an intermolecular part, thus allowing only simulations at a given constant density. 

Current (3) Intensity MDA (2) Molecular dynamics Multivariate data analysis... 

Several significant challenges exist in applying data analysis and interpretation techniques to industrial situations. These challenges include (1) the scale (amount of input data) and scope (number of interpretations) of the problem, (2) the scarcity of abnormal situation exemplars, (3) uncertainty in process measurements, (4) uncertainty in process discriminants, and (5) the dynamic nature of process conditions. 

A technique is described [228] for solving a set of dynamic material/energy balances every few seconds in real time through the use of a minicomputer. This dynamic thermal analysis technique is particular useful in batch and semi-batch operations. The extent of the chemical reaction is monitored along with the measurement of heat transfer data versus time, which can be particularly useful in reactions such as polymerizations, where there is a significant change in viscosity of the reaction mixture with time. 

The first example of a dynamic flux analysis was a study performed in the 1960s [269]. In the yeast Candida utilis, the authors determined metabolic fluxes via the amino acid synthesis network by applying a pulse with 15N-labeled ammonia and chasing the label with unlabeled ammonia. Differential equations were then used to calculate the isotope abundance of intermediates in these pathways, with unknown rate values fitted to experimental data. In this way, the authors could show that only glutamic acid and glutamine-amide receive their nitrogen atoms directly from ammonia, to then pass it on to the other amino acids. 

Figure 8.1 shows dynamic mechanical analysis (DMA) data for an unfilled and 30 % glass-filled PBT. Note the sharply higher modulus ( ) in the glass-filled blend at all temperatures. 

There are many types of data in chemistry that are not specifically covered in this book. For example, we do not discuss NMR data. NMR spectra of solutions that do not include fast equilibria (fast on the NMR time scale) can be treated essentially in the same way as absorption spectra. If fast equilibria are involved, e.g. protonation equilibria, other methods need to be applied. We do not discuss the highly specialised data analysis problems arising from single crystal X-ray diffraction measurements. Further, we do not investigate any kind of molecular modelling or molecular dynamics methods. While these methods use a lot of computing time and power, they are more concerned with data generation than with data analysis. 

Fluorescence spectroscopy and its applications to the physical and life sciences have evolved rapidly during the past decade. The increased interest in fluorescence appears to be due to advances in time resolution, methods of data analysis and improved instrumentation. With these advances, it is now practical to perform time-resolved measurements with enough resolution to compare the results with the structural and dynamic features of macromolecules, to probe the structures of proteins, membranes, and nucleic acids, and to acquire two-dimensional microscopic images of chemical or protein distributions in cell cultures. Advances in laser and detector technology have also resulted in renewed interest in fluorescence for clinical and analytical chemistry. 

An interesting recent development is the application of an electron-nuclear-dynamics code [68] to penetration phenomena [69]. The scheme is capable of treating multi-electron systems and may he particularly useful for low-velocity stopping in insulating media, where alternative treatments are essentially unavailable. However, conceptional problems in the data analysis need attention, such as separation of nuclear from electronic stopping and, in particular, the very definition of stopping force as discussed in Section 5.2. 

S. Wold, J. Cheney, N. Kettaneh and C. McCready, The chemometric analysis point and dynamic data in pharma-centical and biotech prodnction (PAT) - some objectives and approaches, Chemom. Intell. Lab. Syst., 84, 159-163 (2006). 

The experimental data conformed to Eq. (93) and therefore could be interpreted by either mechanism I or II data analysis showed no linear dependence of the logarithm of parameter C in Eq. (93).on the carbon number of the alkyl sulfate hetaerons. However, in the case of dynamic ion exchange parameter C is the binding constant of the hetaeron to the stationary phase hnd its logarithm should be linearly dependent on the carbon number of the alkyl moiety. Even if the results of this study are not accepted as support for ion-pairing (mechanism I) uniquely, they cannot be used to validate dynamic ion-exchange (mechanism II) either. 

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