Equilibrium and Kinetic Study of l- and d-Valine Adsorption in Supramolecular-Templated Chiral Mesoporous Materials

Wireless Hollow Miniaturized Objects for Electroassisted Chiral Resolution

Chiral resolution plays a crucial role in the field of drug development, especially for a better understanding of biochemical processes. In such a context, classic separation methods have been used for decades due to their versatility and easy scale-up. Among the many attempts proposed for enantioselective separation, electroassisted methods are presented as an interesting alternative. Herein, we present the use of wirelessly activated hollow tubular systems for the effective, simple, and tunable separation of racemic and enantioenriched mixtures. These double-layered tubular objects consist of an external polypyrrole chassis, a polymer with good electromechanical properties, functionalized in its inner part with an inherently chiral oligomer. The synergy between the electromechanical pumping process of the outer layer and the enantioselective affinity of the inner part induces the system to behave as a miniaturized chiral column. These hybrid objects are able to separate racemic and enantioenriched solutions of chiral model analytes into the corresponding enantiomers in high enantiomeric purity. Finally, these electromechanical systems can resolve mixtures formed by chiral probes with completely uncorrelated molecular structures injected simultaneously into the single antipodes.

Universal machine-learning algorithm for predicting adsorption performance of organic molecules based on limited data set: Importance of feature description

Adsorption of organic molecules from aqueous solution offers a simple and effective method for their removal. Recently, there have been several attempts to apply machine learning (ML) for this problem. To this end, polyparameter linear free energy relationships (pp-LFERs) were employed, and poor prediction results were observed outside model applicability domain of pp-LFERs. In this study, we improved the applicability of ML methods by adopting a chemical-structure (CS) based approach. We used the prediction of adsorption of organic molecules on carbon-based adsorbents as an example. Our results show that this approach can fully differentiate the structural differences between any organic molecules, while providing significant information that is relevant to their interaction with the adsorbents. We compared two CS feature descriptors: 3D-coordination and simplified molecular-input line-entry system (SMILES). We then built CS-ML models based on neural networks (NN) and extreme gradient boosting (XGB). They all outperformed pp-LFERs based models and are capable to accurately predict adsorption isotherm of isomers with similar physiochemical properties such as chiral molecules, even though they are trained with achiral molecules and racemates. We found for predicting adsorption isotherm, XGB shows better performance than NN, and 3D-coordinations allow effective differentiation between organic molecules.