Liquification of 2,2,4-trimethyl-1,3-pentanediol into hydrophobic eutectic mixtures: A multi-criteria design for eco-efficient boron recovery

Almustafa, Ghaiath; Darwish, Ahmad S.; Lemaoui, Tarek; O'Conner, Matthew J.; Amin, Shady A.; Arafat, Hassan A.; AlNashef, Inas M.

doi:10.1016/j.cej.2021.131342

Cited by 29 publications

(17 citation statements)

References 65 publications

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“…, ethane, ethanol, ethanal, ethanoic acid) . This makes them particularly useful for describing non-covalent interactive bonds between molecules and properties that highly depend on intermolecular interactions (such as T m ) . Furthermore, σ Profiles are capable of describing molecules of any size, unlike molecular SMILES and molecular fingerprints that change according to the number of atoms within the molecule .…”

Section: Resultsmentioning

confidence: 99%

Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σ_Profiles Molecular Inputs

Boublia

Lemaoui

AlYammahi

et al. 2022

ACS Sustainable Chem. Eng.

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Polyhydroxyalkanoates (PHAs) are an emerging type of bioplastic that have the potential to replace petroleum-based plastics. They are biosynthetizable, biodegradable, and economically viable and have a range of tunable properties. Despite their great potential, the structure and properties of PHA remain unexplored due to their theoretically infinite chemical space. Therefore, computational approaches for accurate predictions of their various properties need to be developed to effectively explore this large chemical space. For this purpose, this work presents a multitask artificial neural network (ANN) capable of predicting the glass transition temperature (T g ) and melting temperature (T m ) of PHA homopolymers and copolymers. The ANN inputs included the σ Profiles as molecular parameters describing the monomer chemistry and their composition. In contrast, the polymer molecular weight (M) and polydispersity index (PDI) were used to describe the polymer state. The results showed that after optimizing the hyperparameters, the selected ANN architecture was remarkable in predicting the T g and T m of PHA with R 2 values of 0.979 and 0.986 and average absolute relative deviation (AARD) of 0.476% and 0.520%, respectively. The proposed model represents an initiative to promote the development of robust, open source, and user-friendly models capable of predicting the properties of polymers based solely on molecular parameters (σ Profiles ), thereby saving time and resources for researchers worldwide. The framework described in this work is flexible so that it can be applied to a larger chemical space and incorporate other properties of polymers.

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Section: Resultsmentioning

confidence: 99%

Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σ_Profiles Molecular Inputs

Boublia

Lemaoui

AlYammahi

et al. 2022

ACS Sustainable Chem. Eng.

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“…When the S:F mass ratio was varied from 1:4 (20 wt% HDES) up to 4:1 (80 wt% HDES), Figure 10, the extraction efficiency of acetic acid substantially increased from 13.4% to 56.5%, which is a common trend in LLX [33,45]. This indicates that the acetic acid extraction efficiency is sensitive to variation in the solvent-to-feed ratio, which was expected due to its low solubility in the HDES in the presence of water as indicated by its low distribution coefficient.…”

Section: Effect Of Solvent-to-feed Ratiomentioning

confidence: 93%

“…Hence, the different types of hydrophobic acids, menthol, and TOABr were selected as they have been previously reported in the literature for their ability to form HDESs and for their renewable and biodegradable nature [30]. Additionally, menthol-based DESs [33] and acid-based DESs [34] are known for their low viscosities in comparison to ionic-based DESs (e.g., quaternary ammonium based DESs) [23]. This is important since low viscosity solvents facilitate mass transport between the phases [32].…”

Section: Solvent Preparation and Selectionmentioning

confidence: 99%

Green Extraction of Volatile Fatty Acids from Fermented Wastewater Using Hydrophobic Deep Eutectic Solvents

et al. 2021

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Volatile fatty acids (VFAs) are carboxylic acids mainly produced via the fermentation of organic waste streams. Being industrial platform chemicals, sustainable, green and economical recovery of VFAs is necessary. Herein, hydrophobic deep eutectic solvents (HDES), “a new generation of water-immiscible designer solvents”, were assessed for the recovery of VFAs via liquid–liquid extraction. The eutectic mixture of menthol-lauric acid exhibited the highest stability and hydrophobicity. The binary solubility of the pairs {VFA in water} and {VFA in HDES}—and the saturation of the HDES with water were measured. Furthermore, the influences of key parameters on the extraction efficiency were investigated. On multi-stage extraction, an efficiency of 88% was achieved in 4 stages, and the HDES was successfully regenerated using vacuum evaporation. The HDES performance was also compared to other reported relevant solvents. It was concluded that menthol-lauric acid HDES is a promising candidate for the green extraction of VFAs from fermented wastewater.

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“…DESs have been actively applied as a promising alternative to volatile organic compounds and ionic liquids. − DESs are attractive for several reasons, including their inexpsensive to moderate cost of synthesis, the absence of purifying steps required before use, their low flammability, and their biodegradability. ,− Since their discovery, the application of DESs in various fields has shown the bright future of DESs as a promising solvent. − Such an example could be found in fuel purification processes, where DESs led their effectiveness in improving the quality of fuels. − One of the most advanced applications of DESs in fuel purification is the simultaneous removal of different aromatics. Choline chloride was used as an HBA in the DESs developed by Larriba et al to eliminate BTEX ( i.e.…”

Section: Introductionmentioning

confidence: 99%

Solvent Regeneration Methods for Combined Dearomatization, Desulfurization, and Denitrogenation of Fuels Using Deep Eutectic Solvents

et al. 2022

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Deep eutectic solvents (DESs) can be used as potential solvents for various applications. However, their recovery depends on both economic and environmental considerations. In this study, the possibilities for the recovery of methyl triphenyl phosphonium bromide/triethylene glycol (MTPPB/TEG 1:4) after the application of combined dearomatization, desulfurization, and denitrogenation of fuels are investigated. The DES was first prepared and characterized for its density, viscosity, and water content. Then, the single-stage liquid–liquid extraction was conducted in addition to testing the repetitive use of the DES. After that, two regeneration methods were studied: the stripping method (with n-heptane) and the washing method (with distilled water or diethyl ether). In addition, a parametric study was conducted to optimize the regeneration methods. The results showed that washing the used DES with distilled water was significantly more effective than stripping the DES with n-heptane. In terms of quinoline reduction, distilled water reduced the quinoline content in the DES from 3.2 to 2.1 wt %, while n-heptane showed a minor reduction in the quinoline content (3.2 to 3 wt %). It was also found that a much more effective recovery could be achieved by (i) increasing the DES-to-regeneration solvent mass ratio and (ii) increasing the number of wash cycles. Furthermore, the regeneration temperature did not have a significant effect on the recyclability of the DES. The results demonstrated that the regenerated DES was as effective in extraction as a fresh batch of DES.

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Liquification of 2,2,4-trimethyl-1,3-pentanediol into hydrophobic eutectic mixtures: A multi-criteria design for eco-efficient boron recovery

Cited by 29 publications

References 65 publications

Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σ_Profiles Molecular Inputs

Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σ_Profiles Molecular Inputs

Green Extraction of Volatile Fatty Acids from Fermented Wastewater Using Hydrophobic Deep Eutectic Solvents

Solvent Regeneration Methods for Combined Dearomatization, Desulfurization, and Denitrogenation of Fuels Using Deep Eutectic Solvents

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Liquification of 2,2,4-trimethyl-1,3-pentanediol into hydrophobic eutectic mixtures: A multi-criteria design for eco-efficient boron recovery

Cited by 29 publications

References 65 publications

Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σProfiles Molecular Inputs

Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σProfiles Molecular Inputs

Green Extraction of Volatile Fatty Acids from Fermented Wastewater Using Hydrophobic Deep Eutectic Solvents

Solvent Regeneration Methods for Combined Dearomatization, Desulfurization, and Denitrogenation of Fuels Using Deep Eutectic Solvents

Contact Info

Product

Resources

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Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σ_Profiles Molecular Inputs

Multitask Neural Network for Mapping the Glass Transition and Melting Temperature Space of Homo- and Co-Polyhydroxyalkanoates Using σ_Profiles Molecular Inputs