Investigating calcite growth rates using a quartz crystal microbalance with dissipation (QCM-D). (1st February 2018) - Cao, Bo; Stack, Andrew G.; Steefel, Carl I.; DePaolo, Donald J.; Lammers, Laura N.; Hu, Yandi
Abstract: Calcite precipitation plays a significant role in processes such as geological carbon sequestration and toxic metal sequestration and, yet, the rates and mechanisms of calcite growth under close to equilibrium conditions are far from well understood. In this study, a quartz crystal microbalance with dissipation (QCM-D) was used for the first time to measure macroscopic calcite growth rates. Calcite seed crystals were first nucleated and grown on sensors, then growth rates of calcite seed crystals were measured in real-time under close to equilibrium conditions (saturation index, SI = log ({Ca 2+ }/CO3 2− /Ksp ) = 0.01–0.7, where {i} represent ion activities and Ksp = 10 −8.48 is the calcite thermodynamic solubility constant). At the end of the experiments, total masses of calcite crystals on sensors measured by QCM-D and inductively coupled plasma mass spectrometry (ICP-MS) were consistent, validating the QCM-D measurements. Calcite growth rates measured by QCM-D were compared with reported macroscopic growth rates measured with auto-titration, ICP-MS, and microbalance. Calcite growth rates measured by QCM-D were also compared with microscopic growth rates measured by atomic force microscopy (AFM) and with rates predicted by two process-based crystal growth models. The discrepancies in growth rates among AFM measurements and model predictions appear to mainly arise from differences in step densities, and the step velocities were consistent among the AFM measurements as well as with both model predictions. Using the predicted steady-state step velocity and the measured step densities, both models predict well the growth rates measured using QCM-D and AFM. This study provides valuable insights into the effects of reactive site densities on calcite growth rate, which may help design future growth models to predict transient-state step densities.