Empirical modelling of physicochemical properties of carbon capture solvent-aqueous 2-amino-2-methyl-1-propanol (AMP)
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Soundharya Balasubramanian
und
Srinivas Vetriselvan
Abstract
This study delves into the physicochemical properties of aqueous AMP solutions, examining how varying concentrations and temperatures affect their behavior. Understanding these properties is crucial for optimizing AMP’s use in various industrial applications, from pharmaceuticals to food processing. Our findings reveal that density (ρ) decreases from 1.095 g/ml at 10 wt% and 283 K to 1.014 g/ml at 50 wt% and 333 K, showcasing significant thermal expansion effects. We also observed that the excess molar volume contracts, particularly at higher concentrations, indicating stronger solute–solvent interactions. Viscosity (μ) increases from 1.71 mPa s at 10 wt% and 283 K to 6.78 mPa s at 50 wt% and 283 K, while it decreases with temperature, highlighting how molecular motion enhances flow. Additionally, surface tension (σ) drops from 46.95 mN/m at 10 wt% and 283 K to 37.27 mN/m at 50 wt% and 333 K, reflecting the disruption of cohesive forces among water molecules. Our statistical analysis confirms the importance of concentration in shaping these properties, with high R2 values (ρ = 96.67 %, μ = 95.72 %, σ = 86.19 %) and parity charts demonstrating impressive model accuracy, exceeding 99 % for density and 95 % for viscosity.
Acknowledgments
The authors acknowledge the financial support of the Department of Science and Technology, Mission Innovation – Innovation Challenge (IC#3) CCUS Project F. No. DST/TM/EWO/MI/CCUS Project/25 (G1) Government of India, Ministry of Science &; Technology, TMD (Energy, Water &; Others), Technology Bhavan, New Delhi.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: SB – Methodology, Validation, Investigation, Data curation. SR – Methodology, Formal Analysis, Investigation, Data curation, Writing – Original Draft. AB – Conceptualization, Methodology, Resources, Writing – Original Draft, Writing – Review & Editing, Project Administration. SV – Validation, Visualization.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: The data that support the findings of this study are available from the corresponding author upon reasonable request.
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