Collagen-g-poly(Sodium Acrylate-co-Acrylamide)/sodium montmorillonite superabsorbent nanocomposites: synthesis and swelling behavior

Nanocomposite superabsorbents were synthesized by graft copolymerization of mixture of acrylamide (AAm) and acrylic acid (AA) onto collagen using potassium persulfate (KPS) as a free radical initiator and methylenebisacrylamide (MBA) as a crosslinker. Nanoclay sodium montmorillonite (MMt) was introduced as filler into superabsorbent. The chemical structure of the Collagen-g-poly(Sodium Acrylate-co-Acrylamide)/MMt nanocomposite was characterized by means of FTIR spectroscopy, XRD patterns, and TGA thermal methods. Morphology of the sample was examined by scanning electron microscopy (SEM). The effects of reaction variables were systematically optimized to achieve a superabsorbent with swelling capacity as high as possible. Under the optimized conditions concluded, the maximum swelling capacity in distilled water was 950 g/g. Dewatering of nanocomposite and clay-free superabsorbent revealed that inclusion of nanoclay into superabsorbents can improve water retention of superabsorbent under heating. The swelling ratio in various salt solution and kinetic of dewatering was also determined and additionally, the swelling of nanocomposite superabsorbent was measured in solution with pH ranged 1–13. The synthesized nanocomposite exhibited a pH-responsive characteristic.     

Software reliability prediction: A survey

Softwares play an important role in controlling complex systems. Monitoring the proper functioning of the components of such systems is the principal role of softwares. Often, a petite fault in one of the subsystems may cause irreparable damages; therefore, it is of great importance to be able to predict software faults and estimate the reliability of softwares. In this survey, we present a classification of various methods proposed in the literature to predict software reliability. This study summarizes the results of more than 200 research papers in the field. We also discuss the challenges involved in prediction methods along with proposed partial solutions (i.e., Bayesian methods) to improve the accuracy of such predictions. Moreover, we review numerous evaluation measures introduced so far to assess the performance of prediction models, the datasets they are based on, and also the results they yield.