Quantitative structure-property relationship for melting and freezing points of deep eutectic solvents

Khajeh, Aboozar; Shakourian‐Fard, Mehdi; Parvaneh, Khalil

doi:10.1016/j.molliq.2020.114744

Cited by 30 publications

(15 citation statements)

References 61 publications

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“…DESs are formed by the combinations of at least two compounds, leading to a large melting point depression upon formation of a eutectic mixture, mostly leading to mixtures that are liquid under ambient conditions. DESs show significant depressions in melting points, 2 in comparison to those of other competitive solvents with neat constituent compounds. DESs can be classified into five different categories, corresponding to the types of compounds that are mixed to form the eutectic mixture (Figure 1a).…”

Section: Introductionmentioning

confidence: 96%

Review and Perspectives for Effective Solutions to Grand Challenges of Energy and Fuels Technologies via Novel Deep Eutectic Solvents

Aparício

2021

Energy Fuels

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Sustainable technologies applied to energy-related applications should develop a pivotal role over the next decades. Therefore, the development of new materials for old processes has merged as a central research line lately. Deep eutectic solvents (DESs) have been recently considered alternative and economic task-specific solvents for many chemical and environmental processes. The low-cost production of DESs production from natural sources and their tunable properties, such as neat null toxicity and biodegradability, make these solvents suitable candidates for various processes within the green chemistry framework. Considering the millions of possible DES combinations yet to be explored, a detailed review of DES research’s current status that can elaborate on the structure–property relationship is an essential task to identify the missing links and strong points on DES research. Thus, this review work focuses on the recent research efforts on the utilization of DES on chemical processes, with the purpose of elucidation on gas capture/separation, fuel desulfurization, biodiesel production, and water treatment processes and provides a deeper understanding on outstanding scientific questions and identifies promising new research directions that involve DESs.

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Section: Introductionmentioning

confidence: 96%

Review and Perspectives for Effective Solutions to Grand Challenges of Energy and Fuels Technologies via Novel Deep Eutectic Solvents

Aparício

2021

Energy Fuels

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“…Making full use of these data by ML could figure out some patterns hidden in data and construct quantitative structure–property relationship (QSPR) models for predicting the properties of unknown materials to design new materials with desired target properties. [ 9–14 ]…”

Section: Introductionmentioning

confidence: 99%

“…Making full use of these data by ML could figure out some patterns hidden in data and construct quantitative structure-property relationship (QSPR) models for predicting the properties of unknown materials to design new materials with desired target properties. [9][10][11][12][13][14] As one of the most important branches in materials, polymers have kept playing a key part for the various macromolecular structures and architectural properties. [15][16][17] Polymers have been widely used in both consumer products and engineering applications such as aviation, automobiles, ships, infrastructure, military supplies, and many other fields.…”

Section: Introductionmentioning

confidence: 99%

New Opportunity: Machine Learning for Polymer Materials Design and Discovery

Chen

et al. 2022

Advcd Theory and Sims

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Under the guidance of the material genome initiative (MGI), the use of data‐driven methods to discover new materials has become an innovation of materials science. The polymer materials have been one of the most important parts in materials science for the excellent physical and chemical properties as well as corresponding complex structures. Machine learning, as the core of data‐driven methods, has taken an important place in polymer materials design and discovery. In this review, the authors have introduced the applications of machine learning in the design and discovery of polymer materials. The development tendency of published papers about machine learning in polymer materials, the commonly used algorithms, the polymer descriptors, the workflow of machine learning in polymer materials, and recent progresses of machine learning in materials are summarized. Then, the detail of how to use machine learning to assist design and discovery of polymer materials is fully discussed combined with two cases. Finally, the opportunities and challenges on the future development prospects of machine learning in the field of polymer materials are proposed.

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“…They are usually created by mixing a hydrogen bond acceptor (HBA), commonly a quaternary ammonium or phosphonium salt, and a hydrogen bond donor (HBD), such as metal salts or organics acids. DESs possess a number of desirable properties, such as having low vapor pressure, as well as being task-specific, easy to synthesize, cheap, non-flammable, sustainable, and biodegradable ( Smith et al, 2014 ; Altamash et al, 2017 ; Ma et al, 2017 ; Haghbakhsh et al, 2019 ; Haider et al, 2020 ; Khajeh et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Investigation of carbon dioxide solubility in various families of deep eutectic solvents by the PC-SAFT EoS

et al. 2022

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Having been introduced in 2003, Deep Eutectic Solvents (DESs) make up a most recent category of green solvents. Due to their unique characteristics, and also their tunable physical properties, DESs have shown high potentials for use in various applications. One of the investigated applications is CO2 absorption. The thermodynamic modeling of CO2 solubility in DESs has been pursued by a number of researchers to estimate the capacity and capability of DESs for such tasks. Among the advanced equations of state (EoSs), the Perturbed Chain-Statistical Associating Fluid Theory (PC-SAFT) is a well-known EoS. In this study, the performance of the PC-SAFT EoS for estimating CO2 solubility in various DESs, within wide ranges of temperatures and pressures, was investigated. A large data bank, including 2542 CO2 solubility data in 109 various-natured DESs was developed and used for this study. This is currently the most comprehensive study in the open literature on CO2 solubility in DESs using an EoS. For modeling, the DES was considered as a pseudo-component with a 2B association scheme. CO2 was considered as both an inert and a 2B-component and the results of each association scheme were compared. Considering the very challenging task of modeling a complex hydrogen bonding mixture with gases, the results of AARD% being lower than 10% for both of the investigated association schemes of CO2, showed that PC-SAFT is a suitable model for estimating CO2 solubilities in various DESs. Also, by proposing generalized correlations to predict the PC-SAFT parameters, covering different families of DESs, the developed model provides a global technique to estimate CO2 solubilities in new and upcoming DESs, avoiding the necessity of further experimental work. This can be most valuable for screening and feasibility studies to select potential DESs from the innumerable options available.

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Quantitative structure-property relationship for melting and freezing points of deep eutectic solvents

Cited by 30 publications

References 61 publications

Review and Perspectives for Effective Solutions to Grand Challenges of Energy and Fuels Technologies via Novel Deep Eutectic Solvents

Review and Perspectives for Effective Solutions to Grand Challenges of Energy and Fuels Technologies via Novel Deep Eutectic Solvents

New Opportunity: Machine Learning for Polymer Materials Design and Discovery

Investigation of carbon dioxide solubility in various families of deep eutectic solvents by the PC-SAFT EoS

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