Lab-on-a-bead: Polymeric natural deep eutectic solvents as a versatile platform for (bio)sensor design

Gomez, Federico José Vicente; Vidal, Ezequiel; Domini, Claudia E.; Zanini, Graciela Pilar; Silva, María Fernanda; Garcı́a, Carlos D.

doi:10.1016/j.molliq.2023.122040

Cited by 5 publications

(2 citation statements)

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“…Depending on the selected DES matrix and its targeted application, initiators or photoinitiators, alongside crosslinkers, may be introduced in conjunction with enzymes to instigate the polymerization process. [66,81,82] Several reports already demonstrate the use of DEM made of acrylamide (AAm), meth(acrylic) acid, and N-isopropylacrylamide. [83][84][85][86][87][88][89][90] Therefore, this approach could also be explored for enzyme encapsulation, forming nanocapsules/nanogels by adding cross-linkers and tuning the chemical structure of the DEM.…”

Section: Enzyme Immobilization In Eutectogelsmentioning

confidence: 99%

Eutectogels as Promising Materials in Biocatalysis

Kumar,

Calderón,

Beloqui

et al. 2024

ChemCatChem

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The entrapment of deep eutectic solvents (DES) and eutectic systems into porous scaffolds renders a new class of soft and nonvolatile materials called eutectogels that have recently stepped into the spotlight in different areas ranging from electronics to drug delivery. Recent progress in the use of DES in biocatalysis, where they have been demonstrated to improve substrate supply, conversion, and enzyme stability, has opened an unparalleled opportunity to exploit the merits of eutectogels for immobilizing biological catalysts. The resulting functional materials could outperform traditional hydrogels and ionic liquid gels, offering fresh perspectives to broaden the application scope of many enzymes. In this perspective, we go into the potential of eutectogels as innovative scaffolds that support biocatalytic reactions and discuss different applications where these systems could show plain benefits compared to traditional materials. Future directions for this newly developed technology are highlighted.

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Section: Enzyme Immobilization In Eutectogelsmentioning

confidence: 99%

Eutectogels as Promising Materials in Biocatalysis

Kumar,

Calderón,

Beloqui

et al. 2024

ChemCatChem

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“…Among the thousands of examples published, − DES featuring a melting point (MP) below room temperature are the most interesting as they have the potential to replace both traditional solvents and ionic liquids in room-temperature applications. Moreover, and as recently described by our group, the broad chemical diversity of the components has allowed researchers to make DESs with a broad range of chemical and physical properties, enabling unique applications in extractions, , reaction media, , sensors, , drug delivery, , mass conversion, , biology, , and environmental remediation. , Chemically speaking, DES are supported by a delicate interplay of intermolecular forces that include electrostatic interactions, hydrophobic interactions, van der Waals, , and (probably most importantly) hydrogen bonding. − While some reports suggest that some of the properties of DES can be modulated by the addition of water, − it is absolutely clear that many of those properties are determined by the balance of intermolecular interactions, which are (in turn) determined by the structure and functional groups of the components.…”

Section: Introductionmentioning

confidence: 99%

eutXG: A Machine-Learning Model to Understand and Predict the Melting Point of Novel X-Bonded Deep Eutectic Solvents

Ayres,

Bandara,

McMillen

et al. 2024

ACS Sustainable Chem. Eng.

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We present the application of an extreme gradient boosting model (eutXG) to predict the melting point (MP) of deep eutectic solvents (DES). The model is based on XGBoost, a decision tree ensemble based on gradient boosting designed to be highly scalable that enables superior training speed and prediction accuracy. The selected model�trained with molecular fingerprints, molar ratios, and selected chemical descriptors�enabled the prediction of the MPs of DES with an average accuracy of 97.6%, which represents a difference of just ±2.4% with respect to the values reported in the literature. Using SHapley Additive exPlanations (SHAP), further insights into the relative importance of different inputs used to train the machine learning model were identified. Moreover, the generalization ability of the eutXG model was critically assessed by comparing the predicted vs the experimentally determined MP of a series of novel DES based on halogen bonding, developed by mixing tetraalkylammonium triiodide salts (NPe 4 I 3 or NHex 4 I 3 ) with organoiodines, such as 1,2diiodotetrafluorobenzene (o-F 4 DIB), 1,3-diiodotetrafluorobenzene (m-F 4 DIB), or 2,5-diiodothiophene (2,5-DIT), demonstrating its ability to predict the actual melting with a difference of only 2 K. Our results not only reinforce the importance of having (at least some) representative data for the training step to increase the accuracy of the model's predictions but also demonstrate the ability of eutXG to accelerate the development of novel applications for this entirely new class of hydrophobic DES, potentially impacting a wide range of fields from pharmaceuticals to agrochemicals.

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