Decarboxylation-induced thermal cross-linking occurs at elevated temperatures (∼15 °C above glass transition temperature) for 6FDA–DAM:DABA polyimides, which can stabilize membranes against swelling and plasticization in aggressive feed streams. Despite this advantage, such a high temperature might result in collapse of substructure and transition layers in the asymmetric structure of a hollow fibers based on such a material. In this work, the thermal cross-linking of the 6FDA–DAM:DABA at temperatures much below the glass transition temperature (∼387 °C by DSC) was demonstrated. This sub-T g cross-linking capability enables extension to asymmetric structures useful for large scale membranes. The resulting polymer membranes were characterized by swelling in known solvents for the un-cross-linked materials, TGA analysis, and permeation tests of aggressive gas feed stream at higher pressure. The annealing temperature and time clearly influence the degree of cross-linking of the membranes, and results in a slight difference in selectivity for membranes under various cross-linking conditions. Results indicate that the sub-T g thermal cross-linking of 6FDA–DAM:DABA dense film membrane can be carried out completely even at a temperature as low as 330 °C. Permeabilities were tested for the polyimide membranes using both pure gases (He, O2, N2, CH4, CO2) and mixed gases (CO2/CH4). The selectivity of the cross-linked membrane can be maintained even under very aggressive CO2 operating conditions that are not possible without cross-linking. Moreover, the plasticization resistance was demonstrated up to 700 psia for pure CO2 gas or 1000 psia for 50% CO2 mixed gas feeds.

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The performance of thermally cross-linkable hollow fiber membranes for CO 2 /CH 4 separation and the membrane stability against CO 2 plasticization was investigated. The fiber membranes were thermally cross-linked at various conditions. Cross-linking temperature was found to have a significant effect, while shorter soak time and the presence of trace oxidizer (O 2 or N 2 O) had a negligible effect. The cross-linked fibers were tested using high CO 2 content feeds (50−70% CO 2 ) at a variety of feed pressures (up to 1000 psia), temperatures, and permeate pressures (up to 100 psia) to evaluate membrane performance under various realistic operating conditions. The results demonstrated that cross-linking improves membrane selectivity and effectively eliminates swelling-induced hydrocarbon loss at high pressures. Excellent stability under aggressive feeds (with CO 2 partial pressure up to 700 psia) suggests that cross-linked hollow fiber membranes have great potential for use in diverse aggressive applications, even beyond the CO 2 /CH 4 example explored in this work.

BackgroundThe regulation of many cell functions is inherently linked to cell-cell contact interactions. However, effects of contact interactions among adherent cells can be difficult to detect with global summary statistics due to the localized nature and noise inherent to cell-cell interactions. The lack of informatics approaches specific for detecting cell-cell interactions is a limitation in the analysis of large sets of cell image data, including traditional and combinatorial or high-throughput studies. Here we introduce a novel histogram-based data analysis strategy, termed local cell metrics (LCMs), which addresses this shortcoming.ResultsThe new LCM method is demonstrated via a study of contact inhibition of proliferation of MC3T3-E1 osteoblasts. We describe how LCMs can be used to quantify the local environment of cells and how LCMs are decomposed mathematically into metrics specific to each cell type in a culture, e.g., differently-labelled cells in fluorescence imaging. Using this approach, a quantitative, probabilistic description of the contact inhibition effects in MC3T3-E1 cultures has been achieved. We also show how LCMs are related to the naïve Bayes model. Namely, LCMs are Bayes class-conditional probability functions, suggesting their use for data mining and classification.ConclusionLCMs are successful in robust detection of cell contact inhibition in situations where conventional global statistics fail to do so. The noise due to the random features of cell behavior was suppressed significantly as a result of the focus on local distances, providing sensitive detection of cell-cell contact effects. The methodology can be extended to any quantifiable feature that can be obtained from imaging of cell cultures or tissue samples, including optical, fluorescent, and confocal microscopy. This approach may prove useful in interpreting culture and histological data in fields where cell-cell interactions play a critical role in determining cell fate, e.g., cancer, developmental biology, and tissue regeneration.