Degradation data interpretation

By definition, the exemplar patterns used by these algorithms must be representative of the various pattern classes. Performance is tied directly to the choice and distribution of these exemplar patterns. In light of the high dimensionality of the process data interpretation problem, these approaches leave in question how reasonable it is to accurately partition a space such as R6+ (six-dimensional representation space) using a finite set of pattern exemplars. This degradation of interpretation performance as the number of possible labels (classes) increases is an issue of output dimensionality. 

Additional observations and conclusions based on interpretation of the degradation data are as follows ... 

A9.1.12 A wide range of degradation data are available that must be interpreted according to the... 

Fig. 8. HPLC trace of the J6-DMSO solution of a 2.5 mg sample of cryptospirolepine (7) stored for 10 years. None of the starting alkaloid remains in the sample (LC/MS data). The two largest degradant species, DP-1 and DP-2 (35 and 16%, respectively) have been identified. The former, DP-1, was quickly identified by both spectroscopist data interpretation and by the Structure Elucidator v7.0 CASE program as cryptolepinone. The structure of the latter, DP-2, ultimately identified as cryptoquindoline (8), was determined in parallel, both by a competent spectroscopist with extensive experience with this class of alkaloid structures and using Structure Elucidator v7.0.

DSC is often used in conjunction with TA to determine if a reaction is endothermic, such as melting, vaporization and sublimation, or exothermic, such as oxidative degradation. It is also used to determine the glass transition temperature of polymers. Liquids and solids can be analyzed by both methods of thermal analysis. The sample size is usually limited to 10-20 mg. Thermal analysis can be used to characterize the physical and chemical properties of a system under conditions that simulate real world applications. It is not simply a sample composition technique. Much of the data interpretation is empirical in nature and more than one thermal method may be required to fully understand the chemical and physical reactions occurring in a sample. Condensation of volatile reaction products on the sample support system of a TA can give rise to anomalous weight changes. 

The MWD of polycarbonate is also noted to change on repeated injection molding [41a]. It was concluded that the bond rupture process was random but this conclusion seems in doubt [14a]. Changes in and M seem more consistent with a nonrandom mechanism. As already stated, the literature contains many statements concerning the mechanism of degradation without sufficient experimental documentation and data interpretation to warrant incisive conclusions concerning mechanism. 

Equations [2.26] and [2.30] have been widely used in the literature to interpret degradation data for their simplicity. The validity of these equations is often limited to the very early stage of the degradation. For amorphous polymers, the master Equation [2.15] is more generally valid. However, short chain diffusion is ignored, which is the topic of the following chapters. 

This chapter presents a summary of the systematic EIS modeling of industrial and automotive lubricants. Initially EIS data interpretation for fresh lubricant, influenced by the effects of the chemical composition, temperature, electrochemical potential, AC frequency, and electrode geometry, is presented. Another important practical aspect is determination of changes in the lubricant s bulk and interfacial impedance model parameters as a result of its degradation by time-dependent oxidation and contamination with soot, fuel, and water at different stages in the exploitation cycle. 

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