Towards a holistic understanding of carbonate mineralisation controls

Calcium-carbonates (CaCO₃) are climate-controlling minerals, acting as long-term sinks in the biogeochemical carbon cycle, and playing the role of stable carbon stores in many carbon capture, utilisation and storage (CCUS) technologies. CaCO₃ minerals formed either through low-temperature chemical precipitation or as biominerals by calcifying microorganisms are also increasingly used in applications such as carbon-negative concrete, cements, and other materials, potentially enhancing the economic profitability of CCUS. The design of effective CCUS and material applications requires the ability to predict and control both the rate at which CaCO₃ (bio)minerals form, and the mineralogical properties (e.g., crystal structure, morphology or size) of the particles formed, as these factors can significantly impact the efficiency of carbonate mineralization and the long-term stability of its products. However, we still lack a comprehensive understanding of how multiple environmental variables control CaCO₃ mineralization. Indeed, carbonate crystallisation is under the influence of many physicochemical variables such as temperature, pH, supersaturation and salinity, as well as of a great number of inorganic and organic species (called additives) that can act as inhibitors or promoters of nucleation and growth (Figure 1). While the impacts of these factors on CaCO₃ mineralization have been studied individually, their combined effects remain poorly known. In fact, our current understanding is largely based on empirical studies that investigate the effects of these factors in isolation, providing findings that cannot be easily extrapolated to different conditions, and failing to capture the complexity of natural and engineered systems where multiple variables interact. 

This DPhil project will address this knowledge gap by deploying new high-throughput experimentation and in-situ mineralogical characterization methodologies (High-Throughput Screening Raman), that will allow us to rapidly perform thousands of CaCO₃ mineralization experiments, covering a wide multi-dimensional space of physicochemical variables as well as thousands of inorganic and molecular additives. The large datasets thus assembled will be used to build mathematical models and Machine Learning algorithms that will aim at gaining a mechanistic and predictive understanding of how multiple physicochemical parameters and organic molecules control the mineralogical properties and growth rates of carbonate minerals in complex chemical systems as well as in calcifying organisms.

Potential outcomes: The project results may eventually contribute in the development of more effective CCUS strategies, supporting efforts towards sustainable carbon management solutions. By improving our understanding of the role of inorganic and molecular additives in controlling the mineralogical properties of carbonate crystals, this project may furthermore enable the development of new engineering approaches for the design of improved CaCO3 particles for construction and other material applications.

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