Ultrahigh molecular weight polyethylene was investigated using in situ confocal micro-Raman spectroscopy during annealing at 110.0°C. Based on the Raman spectra, crystalline, amorphous, and all-trans noncrystalline fractions were recognized to evaluate rearrangement kinetics during isothermal annealing at 110.0°C and phase evolution during cooling from 110.0 to 30.0°C. For the crystalline fraction, a substantial increase from 0.600 ± 0.001 to 0.639 ± 0.008 was observed during the first 24.2 min of annealing; a very gradual increase from 0.639 ± 0.001 to 0.679 ± 0.001 occurred during the following 114.6 min. For the amorphous phase fraction, conversely, a sharp decrease from 0.240 ± 0.000 to 0.213 ± 0.004 was exhibited during the first 24.2 min of annealing, and then, a flat decrease happened from 0.213 ± 0.004 to 0.192 ± 0.001 as time expanded to 138.8 min. For the all-tans noncrystalline fraction, a gradual decrease was shown from 0.160 ± 0.000 at 0.0 min to 0.128 ± 0.001 at 138.8 min. The rearrangement rate constant K was obtained to be 0.632 by an Avrami equation. During cooling from 110.0 to 30.0°C, there were two phase evolution regions: region 1 from 110.0 to 90.0°C and region 2 from 90.0 to 30.0°C. The crystal lamella thickened faster in region 1 than in region 2. The amorphous layer continually decreased in content in the combined region of 1 and 2. The all-trans noncrystalline phase obviously decreased in region 1 and then almost maintained a constant level in region 2.

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A process simulation model and a statistical method comprising full factorial and response surface designs were employed to study and optimize acrylamide polymerization in a batch reactor. It was evaluated that the dependence of factors such as initial mass fractions of initiator and acrylamide, reacting temperature and operation time in affecting the polymer weight-average (Mw) of polyacrylamide and acrylamide conversion. The simulation model was established on the basis of a kinetic mechanism that refers to backbiting, termination by disproportionation and combination, as well as gel effect, to achieve the Mw of polyacrylamide and acrylamide conversion. Full factorial and response surface designs were developed to screen significant factors and to build reliable predictive function models for polyacrylamide Mw and acrylamide conversion. The results showed significant independent and interactive factor effects on polyacrylamide Mw and acrylamide conversion and were used to optimize the polymerization of acrylamide.