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Portlandite (Ca(OH)₂; also known as calcium hydroxide or hydrated lime), an archetypal alkaline solid, interacts with carbon dioxide (CO₂) via a classic acid–base “carbonation” reaction to produce a salt (calcium carbonate: CaCO₃) that functions as a low‐carbon cementation agent, and water. Herein, we revisit the effects of reaction temperature, relative humidity (RH), and CO₂concentration on the carbonation of portlandite in the form of finely divided particulates and compacted monoliths. Special focus is paid to uncover the influences of the moisture state (i.e., the presence of adsorbed and/or liquid water), moisture content and the surface area‐to‐volume ratio (s_a/v, mm⁻¹) of reactants on the extent of carbonation. In general, increasing RH more significantly impacts the rate and thermodynamics of carbonation reactions, leading to high(er) conversion regardless of prior exposure history. This mitigated the effects (if any) of allegedly denser, less porous carbonate surface layers formed at lower RH. In monolithic compacts, microstructural (i.e., mass‐transfer) constraints particularly hindered the progress of carbonation due to pore blocking by liquid water in compacts with limited surface area to volume ratios. These mechanistic insights into portlandite's carbonation inform processing routes for the production of cementation agents that seek to utilize CO₂borne in dilute (≤30 mol%) post‐combustion flue gas streams.

 

Numerical simulations have revolutionized material design. However, although simulations excel at mapping an input material to its output property, their direct application to inverse design has traditionally been limited by their high computing cost and lack of differentiability. Here, taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm, we introduce a computational inverse design framework that addresses these challenges, by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end differentiation. Thanks to its differentiability, the simulation is used to directly train a deep generative model, which outputs an optimal porous matrix based on an arbitrary input sorption isotherm curve. Importantly, this inverse design pipeline leverages the power of tensor processing units (TPU)—an emerging family of dedicated chips, which, although they are specialized in deep learning, are flexible enough for intensive scientific simulations. This approach holds promise to accelerate inverse materials design. 

The precipitation of calcium carbonate (CaCO3) is a key mechanism in carbon capture applications relying on mineralization. In that regard, Ca-rich cementitious binders offer a unique opportunity to act as a large-scale carbon sink by immobilizing CO2 as calcium carbonate by mineralization. However, the atomistic mechanism of calcium carbonate formation is still not fully understood. Here, we study the atomic scale nucleation mechanism of an early stage amorphous CaCO3 gel based on reactive molecular dynamics (MD) simulations. We observe that reactive MD offers a notably improved description of this reaction as compared to classical MD, which allows us to reveal new insights into the structure of amorphous calcium carbonate gels and formation kinetics thereof.

 

Developing new sustainable concrete technology has become an urgent need, requiring faster and deeper insights into the fundamental mechanisms driving the cement hydration reactions. Molecular simulations have the potential to provide such understanding since the hydration reaction and the cement chemistry are particularly dominated by mechanisms at the atomic scale. In this letter, we review the application of two major approaches namely classical (including reactive) molecular dynamics simulations and density function theory calculations of cementitious materials. We give an overview of molecular simulations involving the major mineral and hydrate phases.