Investigating the metastability of amorphous calcium carbonate by droplet microfluidics experiments using machine learning.

ABSTRACT: Amorphous calcium carbonate (ACC) plays an important role in the crystallization pathways of calcite and its polymorphs influencing many natural and anthropogenic processes, such as carbon sequestration. Characterizing the dissolution rate of ACC in presence of additives of contaminants in favor of crystalline phases is challenging as such reactions occur readily in bulk solution. Droplet microfluidics offers a solution by confining ACC within a droplet, enabling a quantification of the transformation rate of ACC into crystalline phases. However, accurate quantification of this transformation requires analyzing more than thousands of droplets identifying the different polymorphs of calcium carbonate during an experiment, which is labor-intensive. Here we develop a visual-based machine lear

SUBMITTER: Santoso R 

PROVIDER: S-EPMC12181231 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature