The imperative need to mitigate CO 2 emissions has become increasingly critical due to their severe impact on environmental sustainability and public health. Among the emerging carbon capture technologies, deep eutectic solvent (DES)-based technologies have attracted significant attention owing to their facile synthesis and superior CO 2 capture capacity. However, their widespread industrial deployment has been hindered by challenges associated with high viscosity and cost. To address these limitations, this study adopts novel a approach that synergistically integrates cosolvent addition and immobilization to develop a slurry with enhanced CO 2 capture efficiency. Specifically, an aqueous DES solution was formulated using imidazolium chloride-ethylenediamine ([ImCl][EDA]) in a 1:6 molar ratio with water as the cosolvent, followed by the immobilization of DES onto mesoporous silica to form a composite slurry. CO 2 capture experiments revealed a high sorption capacity of 28.34 wt% at 22 °C and 1 bar, along with rapid sorption and desorption rates of 1.39 and 0.30 mol CO 2 /(kg sorbent·min) within the first 2 min. Furthermore, the slurry exhibited excellent cyclic stability, maintaining a 98 % recovery rate. The significant improvements in CO 2 capture capacity, desorption kinetics, and thermal stability underscore the potential of this hybrid system for scalable industrial applications in carbon capture and utilization. • Incorporation of immobilized silica improved the CO 2 capture capacity of DES. • CO 2 sorption/desorption rates were improved by adding immobilized silica. • The slurry showed better performance compared to MEA. • Thermal stability was enhanced by adding immobilized silica. • The slurry maintained the performance against long-term storage.

Cryogenic carbon capture (CCC), a promising technology for the sequestration of CO 2 from natural gas streams, relies on the CO 2 frost formation at cryogenic temperatures. Precise estimation of the CO 2 frost formation temperature (FFT) is vital for optimizing CCC processes and maximizing efficiency. This study deals with the development of machine learning models for predicting the FFT of CO 2 in natural gas blends. A comprehensive dataset, including 430 experimental samples, was assembled from the literature. The foregoing dataset included the FFT data for binary and ternary natural gas blends across a wide range of pressures and component fractions. Three distinct black-box algorithms, including Regression Tree (RT), Radial Basis Function Neural Network (RBF-NN) and Support Vector Machine (SVM) were employed to model FFT. The performance of each model was rigorously explored through diverse statistical and graphical methods. While all developed models demonstrated high accuracy, the RBF-NN model was the superior predictive tool, achieving a mean absolute percentage error (MAPE) of 0.82 % and a standard deviation (SD) of 1.19 % during the validation phase. Also, an explicit correlation for FFT was proposed using the white-box machine learning technique of Gene Expression Programming (GEP), which achieved the MAPE of 0.59 % for all data. The models were also capable of predicting the FFT of CO 2 in both binary and ternary blends, and favorably captured the complicated physical variations of FFT under diverse operating conditions. To gain deeper insights into the most fundamental factors in controlling the FFT of CO 2 , a sensitivity analysis was conducted. The findings of the current study, in turn, contribute to the understanding of FFT of CO 2 and the optimal design of the CCC processes in natural gas industries. • 430 experimental data for FFT of CO 2 in natural gas blends were analyzed. • Three Black-box machine learning tools were employed for modeling. • A simple FFT correlation was also proposed based on the intelligent method of GEP. • The models properly described the physical variations of FFT. • The most affecting factors on FFT were determined through a sensitivity analysis.