Polylactic acid (PLA) polymer film was degraded in abiotic and biotic environments to understand the role of microbes in the degradation process of lactic acid based polymers. The degradation studies were conducted in a well-characterized biotic system, an abiotic system, a sterile aqueous system, and a desiccated environment maintained at 40, 50, and 60 degrees C. The combination of experiments in different environments isolated the distinct effect of microbes, water, and temperature on the morphological changes in the polymer during degradation. Due to lack of availability of radiolabeled PLA, various analytical techniques were applied to observe changes in the rate and/or mechanism of degradation. CO2 evolved, weight loss, and molecular weights were measured to evaluate the extent of degradation. X-ray diffraction and differential scanning calorimetry techniques monitored the morphological changes in the polymer. FTIR was used as a semiquantitative tool to gather information about the chemistry of the degradative process. Neither of the above analytical techniques indicated any difference in the rate or mechanism of degradation attributable to the presence of microorganisms. The extent of degradation increased at higher process temperatures. FTIR data were evaluated for significant statistical difference by t-test hypothesis. The results confirmed hydrolysis of ester linkage as the primary mechanism of degradation of PLA. On the basis of these data, a probable path of PLA degradation has been suggested.

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Recently, a data-driven model-free control design method has been proposed in (7; 6) for linear systems. It is based on the minimization of a control criterion with respect to the controller parameters using an iterative gradient technique. In this paper, we extend this method to the case where both the plant and the controller can be nonlinear. It is shown that an estimate of the gradient of the control criterion can be constructed using only signal-based information obtained from closed loop experiments. The obtained estimate contains a bias which depends on the local nonlinearity of the noise description of the closed loop system which can be expected to be small in many practical situations. As a side-effect the linear model-free control design method is re-obtained in a new way.

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