Thermophysical characterization of Sustainable Pathways for hydrofluorocarbons separation utilizing Deep Eutectic solvents DOI Creative Commons
Luan Vittor Tavares Duarte de Alencar, Bastian González-Barramuño, S.B. Rodriguez-Reartes

et al.

Journal of Industrial and Engineering Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

Density, viscosity and CO2 solubility modeling of deep eutectic solvents from various neural network approaches DOI
S.M. Hosseini, Mariano Pierantozzi

Journal of the Taiwan Institute of Chemical Engineers, Journal Year: 2025, Volume and Issue: 169, P. 105988 - 105988

Published: Jan. 28, 2025

Language: Английский

Citations

3

Viscosity of Deep Eutectic Solvents: Predictive Modelling with Experimental Validation DOI
Dmitriy M. Makarov, A. M. Kolker

Fluid Phase Equilibria, Journal Year: 2024, Volume and Issue: 587, P. 114217 - 114217

Published: Sept. 1, 2024

Language: Английский

Citations

7

Eutectogels: The Multifaceted Soft Ionic Materials of Tomorrow DOI Creative Commons
Pablo A. Mercadal, Agustín González, Ana Beloqui

et al.

JACS Au, Journal Year: 2024, Volume and Issue: 4(10), P. 3744 - 3758

Published: Oct. 3, 2024

Eutectogels, a rising category of soft materials, have recently garnered significant attention owing to their remarkable potential in various domains. This innovative class materials consists eutectic solvent immobilized three-dimensional network structure. The use eco-friendly and cost-effective solvents further emphasizes the appeal these multiple applications. Eutectogels exhibit key characteristics most solvents, including environmental friendliness, facile preparation, low vapor pressure, good ionic conductivity. Moreover, they can be tailored display functionalities such as self-healing capability, adhesiveness, antibacterial properties, which are facilitated by incorporating specific combinations mixture constituents. perspective article delves into current landscape challenges associated with eutectogels, particularly focusing on applications CO

Language: Английский

Citations

6

Machine Learning for Predicting and Optimizing Physicochemical Properties of Deep Eutectic Solvents: Review and Perspectives DOI
Francisco Javier López-Flores, César Ramírez‐Márquez, J. Betzabe González‐Campos

et al.

Industrial & Engineering Chemistry Research, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 19, 2024

This review explores the application of machine learning in predicting and optimizing key physicochemical properties deep eutectic solvents, including CO2 solubility, density, electrical conductivity, heat capacity, melting temperature, surface tension, viscosity. By leveraging learning, researchers aim to enhance understanding customization a critical step expanding their use across various industrial research domains. The integration represents significant advancement tailoring solvents for specific applications, marking progress toward development greener more efficient processes. As continues unlock full potential it is expected play an increasingly pivotal role revolutionizing sustainable chemistry driving innovations environmental technology.

Language: Английский

Citations

4

Development of a Generalizable Gradient Boosting Regression-Based Framework for Predicting the Properties of Single and Binary IL Systems DOI

Kimia Fereydoon,

Oscar Nordness

ACS Applied Engineering Materials, Journal Year: 2025, Volume and Issue: unknown

Published: March 21, 2025

Language: Английский

Citations

0

Physics-Informed Multifidelity Gaussian Process: Modeling the Effect of Water and Temperature on the Viscosity of a Deep Eutectic Solvent DOI
Maximilian Fleck, Samir Darouich, Jürgen Pleiss

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: April 16, 2025

Knowledge of shear viscosity as function temperature and composition an aqueous deep eutectic solvent mixture is essential for process design but can be highly challenging costly to measure. The present work proposes combine a small set experimentally determined viscosities with simulated values within linear multifidelity approach predict the dependency on composition. This method provides simple that requires physics-based transformation data prior training, without need additional such densities. allows reduction in cost experiments reduces number simulations required characterize specific system. data-driven component model does not concern itself rather excess free energy term framework according Eyring's absolute rate theory. Moreover, we illustrate application kernel-based machine learning approaches daily research questions where availability limited compared size typically neural networks.

Language: Английский

Citations

0

Potential of deep eutectic solvents as green and sustainable solvents for the recovery of carboxylic acids from aqueous solution: a review DOI

S. V. Suresh Babu,

Sushil Kumar

Journal of Chemical Technology & Biotechnology, Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Abstract This paper presents an in‐depth exploration of the potential deep eutectic solvents (DESs) as green and sustainable in reactive extraction process for recovery carboxylic acids from fermentation broth/aqueous waste stream. It delves into eco‐friendly nature cost‐effectiveness DESs, highlighting their high solvation capabilities aligning with practices. The various hydrophobic DESs/NADESs, examining suitability replacements conventional along preparation, are explored. study emphasizes appropriate selection hydrogen bond donors (HBDs) acceptors (HBAs) DES formulations to enhance efficiency. role physical properties DESs such viscosity, density, surface tension ionic conductivity, which impact mass transfer efficiency discussed. In this review, effect parameters type DES/carboxylic acid, initial acid concentration, pH, aqueous phase ratio, temperature, stirring speed on possible mechanism extracted out. With recent studies using efficient excellent reusability multiple cycles is also concludes recommendations future prospects, emphasizing need optimize compositions intensified technologies scale up practical recovery. © 2025 Society Chemical Industry (SCI).

Language: Английский

Citations

0

Solvation Dynamics and Microheterogeneity in Deep Eutectic Solvents DOI
Srijan Chatterjee, Tubai Chowdhury, Sayan Bagchi

et al.

The Journal of Physical Chemistry B, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 13, 2024

Deep eutectic solvents have attracted considerable attention due to their unique properties and potential replace conventional in diverse applications, such as catalysis, energy storage, green chemistry. However, despite broad use, the microscopic mechanisms governing solvation dynamics role of hydrogen bonding deep remain insufficiently understood. In this article, we present our contributions toward unravelling micro heterogeneity within by combining vibrational Stark spectroscopy two-dimensional infrared with molecular simulations. Our findings demonstrate how composition, constituents, addition water significantly influence heterogeneous network solvent these systems. These insights provide valuable guidance for design next-generation tailored specific applications. By integrating experimental computational approaches, work sheds light on intricate relationship between nanostructure solvents, ultimately paving way innovative advances design.

Language: Английский

Citations

2

Comprehensive models to estimate the Isobaric heat capacity of deep eutectic solvents based on Machine learning algorithms DOI
M.A. Moradkhani, Seyyed Hossein Hosseini

Journal of Molecular Liquids, Journal Year: 2024, Volume and Issue: 416, P. 126475 - 126475

Published: Nov. 10, 2024

Language: Английский

Citations

1

Thermophysical characterization of Sustainable Pathways for hydrofluorocarbons separation utilizing Deep Eutectic solvents DOI Creative Commons
Luan Vittor Tavares Duarte de Alencar, Bastian González-Barramuño, S.B. Rodriguez-Reartes

et al.

Journal of Industrial and Engineering Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Language: Английский

Citations

0