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Carbon capture and storage (CCS) plays a critical role in achieving climate change mitigation targets, offering a pathway to decarbonize power generation, industrial processes, and heat production while addressing atmospheric CO2 removal. While CCS technologies are technically advanced, the widespread adoption of 100 % CO2 capture capacities such as 1 mol of CO2/mol of material and 1 g CO2/g storage (targeted by the DARPA, Defense Sciences Office, USA Govt.) has raised questions about the feasibility of achieving higher capture capacities. In the context of limiting global warming to 1.5°C, reaching 100 % CO2 capture capacity is increasingly necessary, with residual emissions requiring complementary carbon dioxide removal (CDR) technologies. This review exclusively focuses on the CO2 capture capacities of various sorbents under standard conditions, using different evaluation metrics. This study explores the performance of solid and liquid sorbents under standard conditions, analyzing factors including surface area, pore structure, solvent type, and functionalization to identify materials optimized for industrial-scale CCS applications. Emerging sorbents, including ILs, MOFs, COFs, POPs, DES, RCC, hybrid materials, and reactive sorbents, offer significant potential for enhanced selectivity and energy-efficient regeneration. Through a systematic assessment of gravimetric, volumetric, and molar capacities, the study provides insights into material efficiencies and trade-offs, offering guidance on optimizing sorbent selection for specific applications. The research advances understanding of scalable CCS technologies, contributing to global efforts to achieve net-zero emissions and address the pressing challenge of climate change. 

Ionic liquids are currently being considered as potential electrolyte candidates for next-generation batteries and energy storage devices due to their high thermal and chemical stability. However, high viscosity and low conductivity at lower temperatures have severely hampered their commercial applications. To overcome these challenges, it is necessary to develop structure–property models for ionic liquid transport properties to guide the ionic liquid design. This work expands our previous effort in developing a machine learning model on imidazolium-based ionic liquids to now include ten different cation families, representing structural and chemical diversity. The model dataset contains 2869 ionic conductivity values over a temperature range of 238–472 K collected from the NIST ILThermo database and literature values for 397 unique ionic liquids. The database covers 214 unique cations and 68 unique anions. Three machine learning models, namely multiple linear regression, random forest, and extreme gradient boosting are applied to correlate the ionic liquid conductivity data with cation and anion features. Shapely additive analysis is performed to glean insights into cation and anion features with significant impact on ionic conductivity. Finally, the extreme gradient boosting model is used to predict the ionic conductivity of ionic liquids from all the possible combinations of unique cations and anions to identify ionic liquids crossing the ionic conductivity threshold of 2.0 S m −1 . 

Ionic liquid catalyzes CO₂hydroxylation by facilitating pre-activation of CO₂viasolvent reorganization, thereby lowering the reaction's activation barrier.