The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material.However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the heat requirement is reduced by as much as a factor of two using the correct heat capacity.

Surface-functionalized silica aerogels and alcogels prepared via a two-step sol-gel process through the combination of different silicon precursors were used in adsorption of methylene blue dye molecules from aqueous media. The effect on adsorption in batch reactors of the nature of precursors, the solvent used in the adsorbents synthesis, and the pH of the dye solution was monitored. Phenyl-functionalized silica materials revealed the highest adsorption capacity. Two phenyl-modified silica aerogels were widely tested in adsorption under various experimental conditions where the effect of pH, temperature, contact time, initial dye concentration, and adsorbent dose were investigated. The synthesis solvent was found to have a clear effect on the behavior of the adsorbent.Optimal conditions were found at pH 8 and 9 where the adsorbent-adsorbate surface charge interactions and the π-π stacking are most favourable. The adsorption followed a pseudo-second order kinetics, indicative of a co-existing chemisorption and physisorption processes. The adsorption data fitted the Sips isotherm and exhibited for the best aerogel a maximum adsorption capacity of 49.2 mg of dye per gram of adsorbent. The thermodynamic study revealed the adsorption of methylene blue onto phenylfunctionalized silica aerogels to be an exothermic and ordered adsorption process.

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Reducing CO2 emissions requires urgently deploying large-scale carbon capture technologies, amongst other strategies. The quest for optimum technologies is a multi-objective problem involving various stakeholders. Today's research of these technologies follows a sequential approach, with chemists focusing first on material design and engineers subsequently seeking the optimal process. Eventually, this combination of materials and processes operates at a scale that significantly impacts the economy and the environment. Understanding these impacts requires analyzing factors such as greenhouse gas emissions over the lifetime of the capture plant, which now constitutes one of the final steps. In this work, we present the PrISMa (Process-Informed design of tailor-made Sorbent Materials) platform, which seamlessly connects materials, process design, techno-economics, and life-cycle assessment. We compare over sixty case studies in which CO2 is captured from different sources in five world regions with different technologies. These studies illustrate how the platform simultaneously informs all stakeholders: identifying the cheapest technology and optimal process configuration, revealing the molecular characteristics of top-performing materials, determining the best locations, and informing on environmental impacts, co-benefits, and trade-offs. Our platform brings together all stakeholders at an early stage of research, which is essential to accelerate innovations at a time this is most needed.

To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials, we also increasingly rely on isotherms predicted from molecular simulations. In particular, for such screening studies, it is important that the procedures to generate the data are accurate, reliable, and robust. In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms. The workflow was tested on a set of metal−organic frameworks (MOFs) and proved to be reliable given different guest molecules. We show that the coupling of our workflow with the Clausius− Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature. We also show that one can accurately predict the CO 2 and N 2 mixture isotherms using ideal adsorbed solution theory (IAST). In particular, we show that IAST is a more reliable numerical tool to predict binary adsorption uptakes for a range of pressures, temperatures, and compositions, as it does not rely on the fitting of experimental data, which typically needs to be done with analytical models such as dual-site Langmuir (DSL). This makes IAST a more suitable and general technique to bridge the gap between adsorption (raw) data and process modeling. To demonstrate this point, we show that the ranking of materials, for a standard three-step temperature swing adsorption (TSA) process, can be significantly different depending on the thermodynamic method used to predict binary adsorption data. We show that, for the design of processes that capture CO 2 from low concentration (0.4%) streams, the commonly used methodology to predict mixture isotherms incorrectly assigns up to 33% of the materials as top-performing.

The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the heat requirement is reduced by as much as a factor of two using the correct heat capacity.