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An advanced kinetic approach to the multistep thermal dehydration of calcium sulfate dihydrate under different heating and water vapor conditions: kinetic deconvolution and universal isoconversional analyses.

Shun IwasakiYuto ZushiNobuyoshi Koga
Published in: Physical chemistry chemical physics : PCCP (2022)
This study aims to identify the kinetic features of individual reaction steps of the multistep thermal dehydration of calcium sulfate dihydrate (CS-DH) to anhydride via a hemihydrate (CS-HH) intermediate by achieving the universal kinetic description of each reaction step under different heating and water vapor pressure ( p (H 2 O)) conditions. The mass loss processes of the thermal dehydration of CS-DH were systematically traced via humidity-controlled thermogravimetry under isothermal and linear nonisothermal conditions at various atmospheric p (H 2 O) values. After reconfirming the variation in the thermal dehydration pathway from a single-step dehydration to anhydride to a multistep process via the CS-HH intermediate with an increase in the p (H 2 O) value, the kinetic curves for each component reaction step were obtained by separating each component process from the partially overlapping mass-loss curves by kinetic deconvolution analysis as required. The induction period (IP) and the mass-loss processes of the thermal dehydrations of CS-DH to anhydride and CS-HH intermediate were compared, wherein more significant retardation effects of water vapor were observed for the IP process followed by direct dehydration to anhydride and for the mass-loss process from CS-DH to the CS-HH intermediate. The universal kinetic behavior of the thermal dehydration of the CS-HH intermediate to anhydride was compared with that of the CS-HH reagent; thus, comparable universal kinetic behaviors were observed except the reaction geometry. Based on the universal kinetic results, the key kinetic phenomenon for regulating the variation of the thermal dehydration pathway of CS-DH was discussed.
Keyphrases
  • particulate matter
  • air pollution
  • neural network