Novel volume measurements on an epoxy resin subjected to carbon dioxide pressure jumps (PCO2) are used to investigate the structural recovery of glassy polymers after plasticizer jumps. Previously, we had investigated an epoxy resin after plasticizer concentration jumps using relative humidity (RH) and CO2 and evidenced structural recovery and physical aging responses. − In that work, two things were demonstrated: (a) qualitatively, the physical aging (after CO2 and RH jumps) and structural recovery (after RH jumps) responses are similar to those observed after temperature jumps, and (b) quantitatively, the responses are not the same and, moreover, they exhibit anomalous behaviors. The purpose of the present work is to further investigate the nature of the anomalous behaviors by investigating the structural recovery response of the same epoxy when subjected to PCO2-jump conditions. We provide new data using CO2-jumps and show intrinsic isopiestics, asymmetry of approach, and memory effect and then compare these with similar data for temperature-jump histories. We show that the extended Kovacs−Aklonis−Hutchinson−Ramos does not adequately describe the structural recovery of the epoxy resin after PCO2-jumps. This is consistent with what was previously observed in our laboratory on modeling the structural recovery data of the epoxy resin subsequent to relative humidity changes. Finally, we show a further anomaly in that the volume in CO2-jump experiments seems to evolve toward a different final state than it does in temperature-jump experiments, suggestive of a metastable glassy state that differs from that of the temperature-jump created glass.

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Noisy data has always been a problem to the experimental community. Effective removal of noise from data is important for better understanding and interpretation of experimental results. Over the years, several methods have evolved for filtering the noise present in the data. Fast Fourier transform (FFT) based filters are widely used because they provide precise information about the frequency content of the experimental data, which is used for filtering of noise. However, FFT assumes that the experimental data is stationary. This means that: (i) the deterministic part of the experimental data obtained from a system is at steady state without any transients and has frequency components which do not vary with respect to time and (ii) noise corrupting the experimental data is wide sense stationary, that is, mean and variance of the noise does not statistically vary with respect to time. Several approaches, for example, short time Fourier transform (STFT) and wavelet transform‐based filters, have been developed to handle transient data corrupted with nonstationary noise (mean and variance of noise varies with respect to time) data. Both these approaches provide time and frequency information about the data (time at which a particular frequency is present in the signal). However, these filtering approaches have the following drawbacks: (i) STFT requires identification of an optimal window length within which the data is stationary, which is difficult and (ii) there are theoretical limits on simultaneous time and frequency resolution. Hence, filtering of noise is compromised. Recently, empirical mode decomposition (EMD) has been used in several applications to decompose a given nonstationary data segment into several characteristic oscillatory components called intrinsic mode functions (IMFs). Fourier transform of these IMFs identifies the frequency content in the signal, which can be used for removal of noisy IMFs and reconstruction of the filtered signal. In this work, we propose an algorithm for effective filtering of noise using an EMD‐based FFT approach for applications in polymer physics. The advantages of the proposed approach are: (i) it uses the precise frequency information provided by the FFT and, therefore, efficiently filters a wide variety of noise and (ii) the EMD approach can effectively obtain IMFs from both nonstationary as well as nonlinear experimental data. The utility of the proposed approach is illustrated using an analytical model and also through two typical laboratory experiments in polymer physics wherein the material response is nonstationary; standard filtering approaches are often inappropriate in such cases. © 2010 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2011