Solvent effects on CO2 capture by simple amino acids: an integrated density functional theory - machine learning approach.
単純なアミノ酸によるCO2回収に対する溶媒効果：統合的密度汎関数理論-機械学習アプローチ (AI 翻訳)
Mukul, Sandhiya Lakshmanan
🤖 gxceed AI 要約
日本語
本研究は、グリシン、アラニン、セリン陰イオンを用いたCO2回収を5つの溶媒環境でDFTと機械学習により解析。グリセロールが最適溶媒であり、反応エンタルピーは-50.8～-53.7 kcal mol⁻¹。機械学習により分子量約105 g mol⁻¹で反応性が変化し、溶媒の水素結合供与能が重要であることを特定。グリシンまたはアラニンとグリセロールの組み合わせが工業的CO2回収に有望。
English
This study investigates CO2 capture by glycine, alanine, and serine anions in five solvents using DFT and machine learning. Glycerol emerges as optimal with exothermic enthalpies (-50.8 to -53.7 kcal mol⁻¹). Machine learning identifies a molecular weight threshold (~105 g mol⁻¹) and shows solvent hydrogen-bond donating capability critically governs efficiency. Glycerol-based formulations with glycine or alanine are promising for industrial CO2 capture.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではCCUS技術の実用化が進んでおり、本研究はアミノ酸系溶媒による効率的なCO2回収の知見を提供する。特にグリセロール溶媒の優位性は、廃グリセリン等の活用も視野に入れれば、日本国内での低コスト化に寄与する可能性がある。
In the global GX context
Globally, carbon capture is a key mitigation strategy. This study provides mechanistic insights and machine learning-guided design for amino acid-based solvents, offering potential for more efficient and sustainable CO2 capture systems. The finding that solvent hydrogen-bonding ability, not dielectric constant, is critical could reshape solvent selection criteria.
👥 読者別の含意
🔬研究者:Provides mechanistic understanding and ML model for predicting CO2 capture reactivity of amino acid solutions, useful for computational chemistry and carbon capture research.
🏢実務担当者:Identifies glycerol as optimal solvent and glycine/alanine as promising candidates for industrial CO2 capture, guiding solvent selection for pilot plants.
🏛政策担当者:Supports the development of efficient carbon capture technologies that can be integrated into national climate strategies, reducing cost and energy demand.
📄 Abstract（原文）
The process of CO2 capture by amino acids offers a promising approach for carbon capture technologies, yet the influence of the molecular structure and solvent environment on the reaction mechanisms remains to be understood. The present study investigates the CO2 capture by glycine, alanine, and serine anions across five environments, namely, gas phase, water, DMSO, glycerol and lactic acid, using density functional theory with implicit solvation. The reaction proceeds via a barrierless nucleophilic attack forming a zwitterionic intermediate followed by the rate-determining intramolecular proton transfer. Glycerol emerges as the optimal medium, exhibiting highly exothermic reaction enthalpies (-50.8 to -53.7 kcal mol-1) and stabilized transition states below the reactant energy levels due to its extensive hydrogen bonding network. Structural variations reveal a kinetic-thermodynamic trade-off in which glycine shows the most favorable gas-phase thermodynamics (-21.4 kcal mol-1) and the lowest barriers (+19.4 kcal mol-1), while the methyl group of alanine introduces steric hindrance and the hydroxymethyl substituent of serine creates a complex solvent-dependent behavior, including an endothermic reaction in DMSO (+0.4 kcal mol-1), due to over-stabilization of the serine-DMSO complex. A correlation analysis of the key parameters reveals that the CO2 loading capacity negatively correlates with amino acid hydrogen bond donors (r = -0.59), explaining the serine-suppressed aqueous activity. Machine learning analysis (gradient boosting regression, R2 = 0.85) identifies a molecular weight threshold (∼105 g mol-1), where the side-chain complexity dominates the reactivity, and demonstrates that the solvent hydrogen bond-donating capability rather than the dielectric constant critically governs the capture efficiency. These findings establish glycerol-based formulations with glycine or alanine as superior candidates for industrial CO2 capture (ΔG298 = -39 to -43 kcal mol-1), highlighting strategic solvent selection for designing tunable amino acid-based carbon capture.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1039/d5cp04797hfirst seen 2026-06-11 05:16:56
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