First-principles prediction of critical micellar concentrations for ionic and nonionic surfactants

An Insightful Overview of Microbial Biosurfactant: A Promising Next-Generation Biomolecule for Sustainable Future

Microbial biosurfactant is an emerging vital biomolecule of the 21st century. They are amphiphilic compounds produced by microorganisms and possess unique properties to reduce surface tension activity. The use of microbial surfactants spans most of the industrial fields due to their biodegradability, less toxicity, being environmentally safe, and being synthesized from renewable sources. These would be highly efficient eco-friendly alternatives to petroleum-derived surfactants that would open up new approaches to research on the production of biosurfactants. In the upcoming era, biobased surfactants will become a dominating multifunctional compound in the world market. Research on biosurfactants ranges from the search for novel microorganisms that can produce new molecules, structural and physiochemical characterization of biosurfactants, and fermentation process for enhanced large-scale productivity and green applications. The main goal of this review is to provide an overview of the recent state of knowledge and trends about microbially derived surfactants, various aspects of biosurfactant production, definition, properties, characteristics, diverse advances, and applications. This would lead a long way in the production of biosurfactants as globally successful biomolecules of the current century.

Aqueous Micellar Environment Impacts the Co-Catalyzed Phototransformation: A Case Study

In recent years, methodologies that rely on water as the reaction medium have gained considerable attention. The unique properties of micellar solutions were shown to improve the regio-, stereo-, and chemoselectivity of different transformations. Herein, we demonstrate that the aqueous environment is a suitable medium for a visible light driven cobalt-catalyzed reaction involving radical species. In this system, reduced vitamin B12 reacts with alkyl halides, generating radicals that are trapped by the lipophilic olefin present in the Stern layer. A series of NMR measurements and theoretical studies revealed the location of reaction components in the micellar system.

Photochemical cyclopropanation in aqueous micellar media - experimental and theoretical studies

While in nature, reactions occur in water-based confined compartments, for a long time, water has been often regarded as an unsuitable medium for organic reactions. We have, however, found that photochemical cyclopropanation of styrenes with diazo compounds or their precursors can be performed in micellar systems. COSMO-RS studies revealed that the reactivity correlates with the predicted critical micelle concentration (CMC), with higher CMC values delivering higher yields.

Analyzing the Accuracy of Critical Micelle Concentration Predictions Using Deep Learning

This paper presents a novel approach to predicting critical micelle concentrations (CMCs) by using graph neural networks (GNNs) augmented with Gaussian processes (GPs). The proposed model uses learned latent space representations of molecules to predict CMCs and estimate uncertainties. The performance of the model on a data set containing nonionic, cationic, anionic, and zwitterionic molecules is compared against a linear model that works with extended connectivity fingerprints (ECFPs). The GNN-based model performs slightly better than the linear ECFP model when there is enough well-balanced training data and achieves predictive accuracy that is comparable to published models that were evaluated on a smaller range of surfactant chemistries. We illustrate the applicability domain of our model using a molecular cartogram to visualize the latent space, which helps to identify molecules for which predictions are likely to be erroneous. In addition to accurately predicting CMCs for some surfactant classes, the proposed approach can provide valuable insights into the molecular properties that influence CMCs.

Tools to enable the study and translation of supramolecular amphiphiles

This tutorial review focuses on providing a summary of the key techniques used for the characterisation of supramolecular amphiphiles and their self-assembled aggregates; from the understanding of low-level molecular interactions, to materials analysis, use of data to support computer-aided molecular design and finally, the translation of this class of compounds for real world application, specifically within the clinical setting. We highlight the common methodologies used for the study of traditional amphiphiles and build to provide specific examples that enable the study of specialist supramolecular systems. This includes the use of nuclear magnetic resonance spectroscopy, mass spectrometry, X-ray scattering techniques (small- and wide-angle X-ray scattering and single crystal X-ray diffraction), critical aggregation (or micelle) concentration determination methodologies, machine learning, and various microscopy techniques. Furthermore, this review provides guidance for working with supramolecular amphiphiles in in vitro and in vivo settings, as well as the use of accessible software programs, to facilitate screening and selection of druggable molecules. Each section provides: a methodology overview - information that may be derived from the use of the methodology described; a case study - examples for the application of these methodologies; and a summary section - providing methodology specific benefits, limitations and future applications.

Machine learning hybrid approach for the prediction of surface tension profiles of hydrocarbon surfactants in aqueous solution

HYPOTHESIS

Predicting the surface tension (SFT)-log(c) profiles of hydrocarbon surfactants in aqueous solution is computationally non-trivial, and empirically challenging due to the diverse and complex architecture and interactions of surfactant molecules. Machine learning (ML), combining a data-based and knowledge-based approach, can provide a powerful means to relate molecular descriptors to SFT profiles.

EXPERIMENTS

A dataset of SFT for 154 model hydrocarbon surfactants at 20-30 °C is fitted to the Szyszkowski equation to extract three characteristic parameters (Γ_max,K_L and critical micelle concentration (CMC)) which are correlated to a series of 2D and 3D molecular descriptors. Key (∼10) descriptors were selected by removing co-correlation, and employing a gradient-boosted regressor model to rank feature importance and carry out recursive feature elimination (RFE). The hyperparameters of each target-variable model were fine-tuned using a randomised cross-validated grid search, to improve predictive ability and reduce overfitting.

FINDINGS

The ML models correlate favourably with test experimental data, with R²= 0.69-0.87, and the merits and limitations of the approach are discussed based on 'unseen' hydrocarbon surfactants. The incorporation of a knowledge-based framework provides an appropriate smoothing of the experimental data which simplifies the data-driven approach and enhances its generality. Open-source codes and a brief tutorial are provided.

Surface Properties of Alkyldi(oxyethylene) β-D-Maltoside

A series of nonionic disaccharide-based surfactants alkyldi(oxyethylene) β-d-maltosides (4a-4h, n = 6-16) were synthesized, and their physicochemical properties were further investigated. Six β-D-maltosides (4c-4h, n = 8-16) exhibited a fan-shaped texture feature, whereas hexyldi(oxyethylene) β-D-maltoside (4a) had the strongest hygroscopicity. Owing to the incorporation of the hydrophilic dioxyethyl spacer (-(OCH₂CH₂)₂-), the related water solubility improved significantly. Tetradecyldi(oxyethylene) β-D-maltoside (4g) had good water solubility, whereas hexadecyldi(oxyethylene) β-D-maltoside (4h) had weak water solubility. Meanwhile, the surface tension of β-D-maltosides (4a-4g, n = 6-14) had a decreasing tendency with increasing the alkyl chain length, whereas 4g had the best surface activity. Furthermore, decyldi(oxyethylene) β-D-maltoside (4e) had the best foaming ability and foam stability. Dodecyldi(oxyethylene) β-D-maltoside (4f) had the best emulsifying property in the rapeseed oil/water system. In contrast, both ammonium dodecyl sulfate (NH₄DS)/4f and cetyltrimethylammonium chloride (CTAC)/4f binary surfactant systems showed a synergistic effect in surface activity because the C_CMC/CMC^id_mix was <1. NaCl impacted the surface activity of the aqueous 4f solution through salt-surfactant synergistic effects. The results showed that such surfactants should have potential applications in the related field in the future.