Predicting the Surface Tension of Deep Eutectic Solvents Using Artificial Neural Networks
Ontology highlight
ABSTRACT:
SUBMITTER: Lemaoui T
PROVIDER: S-EPMC9475633 | biostudies-literature | 2022 Sep
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Deep eutectic solvents (DES) are an important class of green solvents that have been developed as an alternative to toxic solvents. However, the large-scale industrial application of DESs requires fine-tuning their physicochemical properties. Among others, surface tension is one of such properties that have to be considered while designing novel DESs. In this work, we present the results of a detailed evaluation of Quantitative Structure-Property Relationships (QSPR) modeling efforts designed to predict the surface tension of DESs, following the Organization for Economic Co-operation and Development (OECD) guidelines. The data set used comprises a large number of structurally diverse binary DESs and the models were built systematically through rigorous validation methods, including 'mixtures-out'- and 'compounds-out'-based data splitting. The most predictive individual QSPR model found is shown to be statistically robust, besides providing valuable information about the structural and physicochemical features responsible for the surface tension of DESs. Furthermore, the intelligent consensus prediction strategy applied to multiple predictive models led to consensus models with similar statistical robustness to the individual QSPR model. The benefits of the present work stand out also from its reproducibility since it relies on fully specified computational procedures and on publicly available tools. Finally, our results not only guide the future design and screening of novel DESs with a desirable surface tension but also lays out strategies for efficiently setting up silico-based models for binary mixtures.
| S-EPMC9370217 | biostudies-literature
Project description:Stabilized enzymes are crucial for the industrial application of biocatalysis due to their enhanced operational stability, which leads to prolonged enzyme activity, cost-efficiency and consequently scalability of biocatalytic processes. Over the past decade, numerous studies have demonstrated that deep eutectic solvents (DES) are excellent enzyme stabilizers. However, the search for an optimal DES has primarily relied on trial-and-error methods, lacking systematic exploration of DES structure-activity relationships. Therefore, this study aims to rationally design DES to stabilize various dehydrogenases through extensive experimental screening, followed by the development of a straightforward and reliable mathematical model to predict the efficacy of DES in enzyme stabilization. A total of 28 DES were tested for their ability to stabilize three dehydrogenases at 30°C: (S)-alcohol dehydrogenase from Rhodococcus ruber (ADH-A), (R)-alcohol dehydrogenase from Lactobacillus kefir (Lk-ADH) and glucose dehydrogenase from Bacillus megaterium (GDH). The residual activity of these enzymes in the presence of DES was quantified using first-order kinetic models. The screening revealed that DES based on polyols serve as promising stabilizing environments for the three tested dehydrogenases, particularly for the enzymes Lk-ADH and GDH, which are intrinsically unstable in aqueous environments. In glycerol-based DES, increases in enzyme half-life of up to 175-fold for Lk-ADH and 60-fold for GDH were observed compared to reference buffers. Furthermore, to establish the relationship between the enzyme inactivation rate constants and DES descriptors generated by the Conductor-like Screening Model for Real Solvents, artificial neural network models were developed. The models for ADH-A and GDH showed high efficiency and reliability (R2 > 0.75) for in silico screening of the enzyme inactivation rate constants based on DES descriptors. In conclusion, these results highlight the significant potential of the integrated experimental and in silico approach for the rational design of DES tailored to stabilize enzymes.
| S-EPMC11325221 | biostudies-literature
Project description:The encapsulation ?-carotene in whey protein concentrate (WPC) capsules through the emulsion electrospraying technique was studied, using deep eutectic solvents (DES) as solvents. These novel solvents are characterized by negligible volatility, a liquid state far below 0 °C, a broad range of polarity, high solubilization power strength for a wide range of compounds, especially poorly water-soluble compounds, high extraction ability, and high stabilization ability for some natural products. Four DES formulations were used, based on mixtures of choline chloride with water, propanediol, glucose, glycerol, or butanediol. ?-Carotene was successfully encapsulated in a solubilized form within WPC capsules; as a DES formulation with choline chloride and butanediol, the formulation produced capsules with the highest carotenoid loading capacity. SEM micrographs demonstrated that round and smooth capsules with sizes around 2 µm were obtained. ATR-FTIR results showed the presence of DES in the WPC capsules, which indirectly anticipated the presence of ?-carotene in the WPC capsules. Stability against photo-oxidation studies confirmed the expected presence of the bioactive and revealed that solubilized ?-carotene loaded WPC capsules presented excellent photo-oxidation stability compared with free ?-carotene. The capsules developed here clearly show the significant potential of the combination of DES and electrospraying for the encapsulation and stabilization of highly insoluble bioactive compounds.
| S-EPMC7070406 | biostudies-literature
Project description:Early identification of individuals at risk of developing Alzheimer's disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al., 2018) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future. Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a "preprocessing" issue, by imputing the missing data using the previous timepoint ("forward filling") or linear interpolation ("linear filling). The third strategy utilized the minimalRNN model itself to fill in the missing data both during training and testing ("model filling"). Our analyses suggest that the minimalRNN with "model filling" compared favorably with baseline algorithms, including support vector machine/regression, linear state space (LSS) model, and long short-term memory (LSTM) model. Importantly, although the training procedure utilized longitudinal data, we found that the trained minimalRNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data. An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 63 entries as of June 3rd, 2020.
| S-EPMC7797176 | biostudies-literature
Project description:Deep eutectic solvents (DESs) are eutectic mixtures of salts and hydrogen bond donors with melting points low enough to be used as solvents. DESs have proved to be a good alternative to traditional organic solvents and ionic liquids (ILs) in many biocatalytic processes. Apart from the benign characteristics similar to those of ILs (e.g., low volatility, low inflammability and low melting point), DESs have their unique merits of easy preparation and low cost owing to their renewable and available raw materials. To better apply such solvents in green and sustainable chemistry, this review firstly describes some basic properties, mainly the toxicity and biodegradability of DESs. Secondly, it presents several valuable applications of DES as solvent/co-solvent in biocatalytic reactions, such as lipase-catalyzed transesterification and ester hydrolysis reactions. The roles, serving as extractive reagent for an enzymatic product and pretreatment solvent of enzymatic biomass hydrolysis, are also discussed. Further understanding how DESs affect biocatalytic reaction will facilitate the design of novel solvents and contribute to the discovery of new reactions in these solvents.
| S-EPMC5522511 | biostudies-other
Project description:Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.
| S-EPMC5510858 | biostudies-other
Project description:In the recent decade, deep eutectic solvents (DESs) have occupied a strategic place in green chemistry research. This paper discusses the application of DESs as functionalization agents for multi-walled carbon nanotubes (CNTs) to produce novel adsorbents for the removal of 2,4-dichlorophenol (2,4-DCP) from aqueous solution. Also, it focuses on the application of the feedforward backpropagation neural network (FBPNN) technique to predict the adsorption capacity of DES-functionalized CNTs. The optimum adsorption conditions that are required for the maximum removal of 2,4-DCP were determined by studying the impact of the operational parameters (i.e., the solution pH, adsorbent dosage, and contact time) on the adsorption capacity of the produced adsorbents. Two kinetic models were applied to describe the adsorption rate and mechanism. Based on the correlation coefficient (R2) value, the adsorption kinetic data were well defined by the pseudo second-order model. The precision and efficiency of the FBPNN model was approved by calculating four statistical indicators, with the smallest value of the mean square error being 5.01 × 10-5. Moreover, further accuracy checking was implemented through the sensitivity study of the experimental parameters. The competence of the model for prediction of 2,4-DCP removal was confirmed with an R2 of 0.99.
| S-EPMC7180483 | biostudies-literature
Project description:In this experiment, the influence of the morphology and surface characteristics of cellulosic nanoparticles (i.e., cellulose nanocrystals [CNCs] and cellulose nanofibers [CNFs]) on oil-in-water (o/w) emulsion stabilization was studied using non-modified or functionalized nanoparticles obtained following deep eutectic solvent (DES) pre-treatments. The effect of the oil-to-water ratio (5, 10, and 20 wt.-% (weight percent) of oil), the type of nanoparticle, and the concentration of the particles (0.05⁻0.2 wt.-%) on the oil-droplet size (using laser diffractometry), o/w emulsion stability (via analytical centrifugation), and stabilization mechanisms (using field emission scanning electron microscopy with the model compound-i.e., polymerized styrene in water emulsions) were examined. All the cellulosic nanoparticles studied decreased the oil droplet size in emulsion (sizes varied from 22.5 µm to 8.9 µm, depending on the nanoparticle used). Efficient o/w emulsion stabilization against coalescence and an oil droplet-stabilizing web-like structure were obtained only, however, with surface-functionalized CNFs, which had a moderate hydrophilicity level. CNFs without surface functionalization did not prevent either the coalescence or the creaming of emulsions, probably due to the natural hydrophobicity of the nanoparticles and their instability in water. Moderately hydrophilic CNCs, on the other hand, distributed evenly and displayed good interaction with both dispersion phases. The rigid structure of CNCs meant, however, that voluminous web structures were not formed on the surface of oil droplets; they formed in flat, uniform layers instead. Consequently, emulsion stability was lower with CNCs, when compared with surface-functionalized CNFs. Tunable cellulose nanoparticles can be used in several applications such as in enhanced marine oil response.
| S-EPMC6278293 | biostudies-literature
Project description:The lack of accurate methods for predicting the viscosity of solvent materials, especially those with complex interactions, remains unresolved. Deep eutectic solvents (DESs), an emerging class of green solvents, have a severe lack of viscosity data, resulting in their application still staying at the stage of random trial and error, and it is difficult for them to be implemented on an industrial scale. In this work, we demonstrate the successful prediction of the viscosity of DESs based on the transition state theory-inspired neural network (TSTiNet). The TSTiNet adopts multilayer perceptron (MLP) for the transition state theory-inspired equation (TSTiEq) parameters calculation and verification using the most comprehensive DESs viscosity data set to date. For the energy parameters of the TSTiEq, the constant assumption and the fast iteration with the help of MLP can allow TSTiNet to achieve the best performance (the average absolute relative deviation on the test set of 6.84% and R 2 of 0.9805). Compared with the traditional machine learning methods, the TSTiNet has better generalization ability and dramatically reduces the maximum relative deviation of prediction under the constraints of the thermodynamic formulation. It requires only the structural information on DESs and is the most accurate and reliable model available for DESs viscosity prediction.
| S-EPMC9335917 | biostudies-literature
Project description:Deep eutectic solvents (DESs) are an emerging class of modern, often "green" solvents with unique properties. Recently, a deep eutectic system based on amphiphilic surfactant N-alkyl-N,N-dimethyl-3-ammonio-1-propanesulfonate (C12 & C14 sulfobetaine) and (1S)-(+)-10-camphor-sulfonic acid in the molar ratio 1:1.5 has been reported. Nanostructuring can be expected in this DES due to the nature of the components. In this work, we have investigated the native nanostructure in the DES comprising C12-C18 alkyl chain sulfobetaines with camphor sulfonic acid and how it interacts with polar and nonpolar species, water and dodecane, respectively, using small angle neutron scattering. By using contrast variation to highlight the relative position of the solvent components and additives, we can resolve the structure of the solvent and how it changes upon interaction with water and dodecane. Scattering from the neat DES shows structures corresponding to the self-assembly of sulfobetaines; the size of the structure increases as the alkyl chain length of the sulfobetaines increases. Water and dodecane interact, respectively, with the hydrophilic and hydrophobic moieties in the DES structure, primarily the sulfobetaine, thereby swelling and solvating the entire structure. The extent of the shift of the peak position, and the swelling, depend on concentration of the additive. The solution phase organization and the interaction of polar and nonpolar species as observed here, have the potential to affect the ordering of inorganic or polymeric materials grown in such solvents, paving new avenues for templating applications.
| S-EPMC10688184 | biostudies-literature