Predicting lignin removal efficiency in deep eutectic solvent-based biomass fractionation: an explainable machine learning approach

Zijing Zhong, Babbiker Mohammed Taher Gorish, Yue Bai, Waha Ismail Yahia Abdelmula, Wenqian Dang and Daochen Zhu

Green Chem., 2025
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Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

Iman Salahshoori, Alireza Baghban and Amirhosein Yazdanbakhsh

RSC Adv., 2023, 13, 30071
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Improved environmental chemistry property prediction of molecules with graph machine learning

Shang Zhu, Bichlien H. Nguyen, Yingce Xia, Kali Frost, Shufang Xie, Venkatasubramanian Viswanathan and Jake A. Smith

Green Chem., 2023, 25, 6612
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Retraction: Novel hybrid QSPR-GPR approach for modeling of carbon dioxide capture using deep eutectic solvents

Iman Salahshoori, Alireza Baghban and Amirhosein Yazdanbakhsh

RSC Adv., 2024, 14, 20425
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Emerging applications of deep eutectic solvents in the preparation and functionalization of biomass-derived carbonaceous materials: challenges and prospects

Yiyi Shen, Haiqin Zhou, Xiaotong He, Feng Shen, Zhixiang Xu, Bo Yang, Lingzhao Kong and Lichun Dai

Green Chem., 2024, 26, 8123
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Reactive capture and electrochemical conversion of CO2 with ionic liquids and deep eutectic solvents

Saudagar Dongare, Muhammad Zeeshan, Ahmet Safa Aydogdu, Ruth Dikki, Samira F. Kurtoğlu-Öztulum, Oguz Kagan Coskun, Miguel Muñoz, Avishek Banerjee, Manu Gautam, R. Dominic Ross, Jared S. Stanley, Rowan S. Brower, Baleeswaraiah Muchharla, Robert L. Sacci, Jesús M. Velázquez, Bijandra Kumar, Jenny Y. Yang, Christopher Hahn, Seda Keskin, Carlos G. Morales-Guio, Alper Uzun, Joshua M. Spurgeon and Burcu Gurkan

Chem. Soc. Rev., 2024, 53, 8563
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Composition–property relationships of choline based eutectic solvents: impact of the hydrogen bond donor and CO2 saturation

Ruth Dikki, Vaishali Khokhar, Muhammad Zeeshan, Sanchari Bhattacharjee, Oguz Kagan Coskun, Rachel Getman and Burcu Gurkan

Green Chem., 2024, 26, 3441
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Machine learning for CO2 capture and conversion: A review

Sung Eun Jerng, Yang Jeong Park and Ju Li

Energy and AI, 2024, 16, 100361
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Prognostication of advanced CO2 capture using tunable solvents with an ensemble learning-based decision tree model

Reza Soleimani, Amir Hossein Saeedi Dehaghani, Ziba Behtouei, Hamidreza Farahani and Seyyed Mohsen Hashemi

Sci Rep, 2025, 15
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Intelligent prediction of ionic liquids and deep eutectic solvents by machine learning

Yuan Tian, Honghua Zhang, Yueyang Qiao, Han Yang, Yanrong Liu and Xiaoyan Ji

Chinese Journal of Chemical Engineering, 2025, 84, 227
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Physics-Based Machine Learning Models Predict Carbon Dioxide Solubility in Chemically Reactive Deep Eutectic Solvents

Mood Mohan, Omar N. Demerdash, Blake A. Simmons, Seema Singh, Michelle K. Kidder and Jeremy C. Smith

ACS Omega, 2024, 9, 19548
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Physics-informed machine learning to predict solvatochromic parameters of designer solvents with case studies in CO2 and lignin dissolution

Mood Mohan, Nikhitha Gugulothu, Sreelekha Guggilam, T. Rajitha Rajeshwar, Michelle K. Kidder and Jeremy C. Smith

Green Chemical Engineering, 2025, 6, 275
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Accurate VLE Predictions via COSMO-RS-Guided Deep Learning Models: Solubility and Selectivity in Physical Solvent Systems for Carbon Capture

Edoardo Parascandolo, Vincent Gerbaud, David Camilo Corrales, Noslen Hernández, Sophie Thiebaud-Roux and Ivonne Rodriguez-Donis

J. Chem. Inf. Model., 2025, 65, 8514
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Nitrogen mono-doping of graphene and co-doping with group 14 as a sensor for diisobutyl phthalate: Insight from a computational study

Michael O. Odey, Osinde I. Martin, Monsurat Alarape Raimi and Daniel Etiese

Talanta, 2025, 292, 128025
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Multi-scale studies on CO₂ capture: An in-depth investigation of amine-based deep eutectic solvents using machine learning and molecular simulations

Dingkai Hu, Dezhi Cao, Qiang Wang, Bin Wang and Shijian Lu

Chemical Engineering Journal, 2025, 519, 164885
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Unraveling new strategies of natural products drug development using deep eutectic solvents: A comprehensive review

Nur Asma, Delly Ramadon, Ratna Surya Alwi and Abdul Mun’im

Journal of Molecular Liquids, 2025, 436, 128257
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Prediction of Greenhouse Gas Solubility in Eutectic Solvents Using COSMO-RS

Fernanda Paludetto Pelaquim, Sérgio M. Vilas-Boas, Débora Costa do Nascimento, Pedro J. Carvalho, Antonio Marinho Barbosa Neto and Mariana Conceição da Costa

Int J Thermophys, 2024, 45
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An in silico study on the carbon capture performance of menthol-based natural deep eutectic solvent

Sara Rozas, Sonia Martín-Martel, Nuria Aguilar, Alberto Gutiérrez, Pedro A. Marcos, Alfredo Bol, Mert Atilhan, Andrea Mele and Santiago Aparicio

Fuel, 2025, 392, 134784
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An intelligent solvent selection approach in carbon capturing process: A comparative study of machine learning multi-class classification models

Mohammad-Mahdi Pazuki, Milad Hosseinpour, Mohsen Salimi, Mehrdad Boroushaki and Majid Amidpour

Results in Engineering, 2024, 23, 102821
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A review of ionic liquids and deep eutectic solvents design for CO2 capture with machine learning

Jiasi Sun, Yuki Sato, Yuka Sakai and Yasuki Kansha

Journal of Cleaner Production, 2023, 414, 137695
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Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Mood Mohan, Micholas Dean Smith, Omar N. Demerdash, Blake A. Simmons, Seema Singh, Michelle K. Kidder and Jeremy C. Smith

ACS Sustainable Chem. Eng., 2023, 11, 7809
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Machine learning boosted eutectic solvent design for CO2 capture with experimental validation

Xiaomin Liu, Jiahui Chen, Yuxin Qiu, Kunchi Xie, Jie Cheng, Xinze You, Guzhong Chen, Zhen Song and Zhiwen Qi

AIChE Journal, 2025, 71
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Unveiling CO2 capture in tailorable green neoteric solvents: An ensemble learning approach informed by quantum chemistry

Reza Soleimani and Amir Hossein Saeedi Dehaghani

Journal of Environmental Management, 2024, 354, 120298
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Indonesia's country-specific CO2 emission factor based on gas fuels for greenhouse gas inventory in the energy sector

Leni Herlina, Devitra Saka Rani, Tri Muji Susantoro, Agustini, Abdul Haris, Efa Yenti, Roza Adriany, Herizal, Morina, Nurul Hidayati, Rudi Suhartono Wijayako, Atyanto Daru Atmoko, Arie Rahmadi, Dewi Istiyanie, Syntha Nardey and Novie Ardhyarini

Environmental Pollution, 2025, 368, 125749
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An integrated ML model for the prediction of the melting points, phase diagrams, and eutectic points of the Type III and V deep eutectic solvents

Dian Jin, Haotian He, Li Sun, Zuoxiang Zeng and Zhen Liu

Chemical Engineering Science, 2025, 306, 121245
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Ionic liquids and deep eutectic solvents: A brief prologue and their applications in sustainable extraction and separation processes

Sachind Prabha Padinhattath, M. Shaibuna and Ramesh L. Gardas

Journal of the Indian Chemical Society, 2025, 102, 101638
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Deciphering hydrogen bonds and liquid structures in the N-methylurea/choline chloride deep eutectic solvent through ab initio molecular dynamics simulations and Raman spectroscopy

Shih-Huang Pan, Hsu-Ting Yeh, Eugene C. Lin, Jyh-Chiang Jiang and Tzyy-Schiuan Yang

Journal of Molecular Liquids, 2025, 436, 128229
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Predictive understanding of the surface tension and velocity of sound in ionic liquids using machine learning

Mood Mohan, Micholas Dean Smith, Omar Demerdash, Michelle K. Kidder and Jeremy C. Smith

The Journal of Chemical Physics, 2023, 158
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Graph neural networks for CO2 solubility predictions in Deep Eutectic Solvents

Gabriel Hernández Morales, Edgar Ivan Sanchez Medina, Arturo Jiménez-Gutiérrez and Victor M. Zavala

Computers & Chemical Engineering, 2024, 187, 108750
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Carbon price prediction based on multidimensional association rules and optimized multi-factor LSTM model

Xinqi Tu, Lianlian Fu and Qiaoling Wang

Energy, 2025, 329, 136768
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Leveraging spatial charge descriptor in deep learning models: Toward highly accurate prediction of vapor-liquid equilibrium

Hsiu-Min Hung and Ying-Chieh Hung

Journal of the Taiwan Institute of Chemical Engineers, 2025, 171, 106054
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Comprehensive models to estimate the Isobaric heat capacity of deep eutectic solvents based on Machine learning algorithms

Mohammad Amin Moradkhani and Seyyed Hossein Hosseini

Journal of Molecular Liquids, 2024, 416, 126475
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Designing deep eutectic solvents for efficient CO2 capture: A data-driven screening approach

Dmitriy M. Makarov, Yuliya A. Fadeeva, Vasiliy A. Golubev and Arkadiy M. Kolker

Separation and Purification Technology, 2023, 325, 124614
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Unlocking deep eutectic solvent knowledge through a large language model-driven framework and an interactive AI agent

Xiting Peng, Yi Shen Tew, Kai Zhao, Chi Wang, Ren'ai Li, Shanying Hu and Xiaonan Wang

Green Chemical Engineering, 2025, 6, 572
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Predicting deep eutectic solvents for absorption of SO2 based on multilayer perceptron

Dian Jin, Youcai Zhu, Siyang Tang and Zhen Liu

Separation and Purification Technology, 2025, 353, 128594
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Machine learning for deep eutectic solvents: advances in property prediction and molecular design

Anshu Sharma, Aman Garg, Li Li, Indranath Chatterjee, Bong-seop Lee and Akhil Garg

Journal of Molecular Liquids, 2025, 437, 128317
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Deep Learning Approaches for Predicting the Surface Tension of Ionic Liquids

Nikhitha Gugulothu, Mood Mohan, Madaline R. Marland, Jeremy C. Smith and Michelle K. Kidder

J. Chem. Inf. Model., 2025, 65, 5856
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Machine learning-driven optimization of Deep Eutectic Solvents: Accelerating physicochemical properties modeling

Sanjeev Saini, Arun Sharma, Navjot Saini, Navneet Kaur and Narinder Singh

Sustainable Materials and Technologies, 2025, 45, e01536
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Methodological Roadmap for Machine Learning in Deep Eutectic Solvent Research: A Framework-Driven Review and Perspective

Ting Wu, Jiuhang Song, Yinan Hu, Quanyuan Qiu, Jiaxin Tang, Wanqi Peng, Xinyuan Fu, Chenxi Shi and Xiaoqing Lin

Ind. Eng. Chem. Res., 2025, 64, 16443
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Direct air capture of CO2: from insights into the current and emerging approaches to future opportunities

Muhammad Zeeshan, Michelle K. Kidder, Emily Pentzer, Rachel B. Getman and Burcu Gurkan

Front. Sustain., 2023, 4
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Machine Learning for Predicting and Optimizing Physicochemical Properties of Deep Eutectic Solvents: Review and Perspectives

Francisco Javier López-Flores, César Ramírez-Márquez, J. Betzabe González-Campos and José María Ponce-Ortega

Ind. Eng. Chem. Res., 2025, 64, 3103
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Explicable Machine Learning for Predicting High-Efficiency Lignocellulose Pretreatment Solvents Based on Kamlet–Taft and Polarity Parameters

Hanwen Ge, Yuekun Bai, Rui Zhou, Yaoze Liu, Jiahui Wei, Shenglin Wang, Bin Li and Huanfei Xu

ACS Sustainable Chem. Eng., 2024, 12, 7578
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Solvent Screening for Separation Processes Using Machine Learning and High-Throughput Technologies

Justin P. Edaugal, Difan Zhang, Dupeng Liu, Vassiliki-Alexandra Glezakou and Ning Sun

Chem Bio Eng., 2025, 2, 210
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Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

Zhen Song, Jiahui Chen, Jie Cheng, Guzhong Chen and Zhiwen Qi

Chem. Rev., 2024, 124, 248
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Prediction of acetylene solubility by a mechanism-data hybrid-driven machine learning model constructed based on COSMO-RS theory

Yao Mu, Tianying Dai, Jiahe Fan and Yi Cheng

Journal of Molecular Liquids, 2024, 414, 126194
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Synergistic effect of hydrogen bond donors in deep eutectic solvents on separation of dimethyl carbonate/ethanol

Ruoyu Hu, Zhenhua Wen, Tianci Lu, Wenli Liu, Jiuxu Ruan, Zhaoyou Zhu, Yinglong Wang, Jingwei Yang, Jianguang Qi and Peizhe Cui

AIChE Journal, 2025
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Accurate Machine Learning for Predicting the Viscosities of Deep Eutectic Solvents

Mood Mohan, Karuna Devi Jetti, Micholas Dean Smith, Omar N. Demerdash, Michelle K. Kidder and Jeremy C. Smith

J. Chem. Theory Comput., 2024, 20, 3911
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Molecular Dynamics Insights of CO2 Capture through Phosphonium-Based Deep Eutectic Solvents for Direct Air Capture

Rajib Nandi, Nabendu Paul and Tamal Banerjee

ACS Sustainable Chem. Eng., 2025, 13, 8234
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Rigorous Explainable Artificial Intelligence Models for Predicting CO2–Brine Interfacial Tension: Implications for CO2 Sequestration in Saline Aquifers

Menad Nait Amar, Mohamed Riad Youcefi, Fahd Mohamad Alqahtani, Hakim Djema and Mohammad Ghasemi

Energy Fuels, 2025, 39, 14237
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Predicting CO2 equilibrium solubility in various amine-CO2 systems using an artificial neural network model

Apri Wahyudi and Uthaiporn Suriyapraphadilok

Energy and AI, 2024, 18, 100426
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High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Mood Mohan, Karuna Devi Jetti, Sreelekha Guggilam, Micholas Dean Smith, Michelle K. Kidder and Jeremy C. Smith

ACS Sustainable Chem. Eng., 2024, 12, 7040
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