Degradant database

Forsberg, K., Chemical Permeation and Degradation Database and Selection Guide for Resistant Protective Materials, Instant Reference Sources, Austin, TX, 1987. 

Forsberg, K., Chemical Protective Clothing Permeation and Degradation Database, Keith, L.H., Ed., Lewis Publishers, Boca Raton, FL, 1992. 

G. Pharma D3 Degradation Database ISOLATION OF IMPURITIES/DEGRADANTS... 

Benzylic funtionallty Oxidation wiiibe emphasized in our degradation experiments FIGURE 6 Degradation database screen shot example. 

The presence of errors within the underlying database fudher degrades the accuracy and precision of the parameter e.stimate. If the database contains bias, this will translate into bias in the parameter estimates. In the flash example referenced above, including reasonable database uncertainty in the phase equilibria increases me 95 percent confidence interval to 14. As the database uncertainty increases, the uncertainty in the resultant parameter estimate increases as shown by the trend line represented in Fig. 30-24. Failure to account for the database uncertainty results in poor extrapolations to other operating conditions. 

Obviously, use of such databases often fails in case of interaction between additives. As an example we mention additive/antistat interaction in PP, as observed by Dieckmann et al. [166], In this case analysis and performance data demonstrate chemical interaction between glycerol esters and acid neutralisers. This phenomenon is pronounced when the additive is a strong base, like synthetic hydrotalcite, or a metal carboxylate. Similar problems may arise after ageing of a polymer. A common request in a technical support analytical laboratory is to analyse the additives in a sample that has prematurely failed in an exposure test, when at best an unexposed control sample is available. Under some circumstances, heat or light exposure may have transformed the additive into other products. Reaction product identification then usually requires a general library of their spectroscopic or mass spectrometric profiles. For example, Bell et al. [167] have focused attention on the degradation of light stabilisers and antioxidants... 

Waste treatment technologies for incineration and landfill of PVC are present in the ecoinvent database. The material specific burdens for the waste treatment are calculated by a supporting spreadsheet. Necessary data for the calculation of the burden are, e.g. element composition, water content, energy content, degradability in landfill, etc. Note that the Ecoinvent waste management model estimates emissions based on the element composition and some general characteristics of the materials (like degradability). Detailed characteristics, like the mobility of DEHP from PVC, are not taken into account. Therefore additional assumptions have been made for the emission of DEHP from landfill of PVC. 

Once a database is established, it is made available to other laboratories through the company s secured intranet, so that the information therein can be updated, retrieved and reviewed. The resulting structural library can be referenced throughout the lifetime of the drug for rapid identification of impurities, degradants, and metabolites. 

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