The power of kinetics simulations: application of data loggers in monitoring shelf-life of materials

摘要

One of the main goals of investigating rate and progress of the changes occurring in the materials due the influence of time and temperature is the prediction of their shelf-life meant as a period of time in which the materials fulfill certain criteria concerning their chosen properties. This task can be fulfilled by the determination of the kinetic parameters of the process leading to the material properties deterioration. Once the kinetic description of the process is evaluated then it is possible to predict and simulate the materials behavior under any temperature mode. Kinetic analysis performed either by model fitting method or by isoconversional analysis supplies the kinetic parameters: the activation energy E, pre-exponential factor in Arrhenius equation A and, in model fitting approach, the form of the model function f(?) used for the phenomenological description of the reaction course versus time. The knowledge of these parameters allows predicting time-reaction extent dependence for any, arbitrarily chosen temperature fluctuations. This, in turn, provides the estimation of the long-term behavior of any material which property changes may be depicted by the kinetic analysis. In long-term periods the data influencing the progress of the material aging may be collected by the data loggers i.e. the electronic devices that record data over time. The collection of the temperature- time dependences with chosen frequency (once an hour, day or week) introduced to the kinetic simulation procedure leads to the estimation of the aging degree and supplies the valuable information about usability of the stored materials. The application of the data loggers for monitoring of the shelf-life will be illustrated by the simulation of the aging of the vaccines [1] and propellants [2]. In both cases the variety of the processes leading to the material properties deterioration was kinetically analyzed by modified kinetic computations allowing considering one, two or even more reaction stages by applying unlimited amount of combinations of different kinetic models for the best description of the reaction course. The kinetic parameters obtained in the model fitting procedure and those calculated by isoconversional analysis were verified by using the Akaike and Bayesian information criteria [3].