Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents.

ABSTRACT: In recent years, deep eutectic solvents (DESs) have garnered considerable attention for their potential in carbon capture and utilization processes. Predicting the carbon dioxide (CO₂) solubility in DES is crucial for optimizing these solvent systems and advancing their application in sustainable technologies. In this study, we presented an evolving hybrid Quantitative Structure-Property Relationship and Gaussian Process Regression (QSPR-GPR) model that enables accurate predictions of CO₂ solubility in various DESs. The QSPR-GPR model combined the strengths of both approaches, leveraging molecular descriptors and structural features of DES components to establish a robust and adaptable predictive framework. Through a systematic evolution process, we iteratively refine

SUBMITTER: Salahshoori I 

PROVIDER: S-EPMC10573873 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature