Interfacial structure in the liquid-liquid extraction of rare earth elements by phosphoric acid ligands: a molecular dynamics study

Water-Assisted Programmable Assembly of Flexible and Self-Standing Janus Membranes

Janus membranes with asymmetric wettability have been considered cutting-edge for energy/environmental-sustainable applications like water/fog harvester, breathable skin, and smart sensor; however, technical challenges in fabrication and accurate regulation of asymmetric wettability limit their development. Herein, by using water-assisted hydrogen-bonded (H-bonded) assembly of small molecules at water/oil interface, a facile strategy is proposed for one-step fabrication of membranes with well-regulable asymmetric wettability. Asymmetric orderly patterns, beneficial for mass transport based on abundant high-permeability sites and large surface area, are constructed on opposite membrane surfaces. Upon tuning water-assisted H-bonding via H-sites/configuration design and temperature/pH modulation, double-hydrophobic, double-hydrophilic, and hydrophobic-hydrophilic membranes are facilely fabricated. The Janus membranes show smart vapor-responsive curling and unidirectional water transport with promising flux of 1158±25 L m-2  h-1 under natural gravity and 31500±670 L·(m-2  h-1  bar-1 ) at negative pressure. This bottom-up approach offers a feasible-to-scalable avenue to precise-manipulation of Janus membranes for advanced applications, providing an effective pathway for developing tailor-made self-assembled nanomaterials.

Recent Advances in the Study of Trivalent Lanthanides and Actinides by Phosphinic and Thiophosphinic Ligands in Condensed Phases

The separation of trivalent actinides and lanthanides is a key step in the sustainable development of nuclear energy, and it is currently mainly realized via liquid-liquid extraction techniques. The underlying mechanism is complicated and remains ambiguous, which hinders the further development of extraction. Herein, to better understand the mechanism of the extraction, the contributing factors for the extraction are discussed (specifically, the sulfur-donating ligand, Cyanex301) by combing molecular dynamics simulations and experiments. This work is expected to contribute to improve our systematic understanding on a molecular scale of the extraction of lanthanides and actinides, and to assist in the extensive studies on the design and optimization of novel ligands with improved performance.

A hydrogen bond-modulated soft nanoscale water channel for ion transport through liquid-liquid interfaces

Ion transport through interfaces is of ubiquitous importance in many fields such as electrochemistry, emulsion stabilization, phase transfer catalysis, liquid-liquid extraction and enhanced oil recovery. However, the knowledge of interfacial structures that significantly affect ion transport through liquid-liquid interfaces is still lacking due to the difficulty of observing nanoscale interfaces. We studied here the evolution of interfacial structures during ion transport through the decane-water interface under different ionic concentrations and external forces using molecular dynamics simulations. The roles of hydrogen bonds in ion transport through interfaces are revealed. We identified a soft nanoscale channel during ion transport through liquid-liquid interfaces and the decane phase under specific external force. The stability of the water channel and the ion transport velocity both increase with ionic concentration due to the layered ordering structures of the water near the channel surface. We observed that the stability and connectivity of the water channel in the decane phase are remarkably improved both by the high increase of the number of hydrogen bonds in the water channel with increasing ionic concentration, and by the conformational change in water molecules near the water channel surface. Our discovery of a soft nanoscale water channel by molecular simulations implies that there is a potential stable passage for ion transport through liquid-liquid interfaces.

Molecular dynamics simulations of liquid-liquid interfaces in an electric field: The water-1,2-dichloroethane interface

The polarized interface between two immiscible liquids plays a central role in many technological processes. In particular, for electroanalytical and ion extraction applications, an external electric field is typically used to selectively induce the transfer of ionic species across the interfaces. Given that it is experimentally challenging to obtain an atomistic insight into the ion transfer process and the structure of liquid-liquid interfaces, atomistic simulations have often been used to fill this knowledge gap. However, due to the long-range nature of the electrostatic interactions and the use of 3D periodic boundary conditions, the use of external electric fields in molecular dynamics simulations requires special care. Here, we show how the simulation setup affects the dielectric response of the materials and demonstrate how by a careful design of the system it is possible to obtain the correct electric field on both sides of a liquid-liquid interface when using standard 3D Ewald summation methods. In order to prove the robustness of our approach, we ran extensive molecular dynamics simulations with a rigid-ion and polarizable force field of the water/1,2-dichloroethane interface in the presence of weak external electric fields.

Applied machine learning for predicting the lanthanide-ligand binding affinities

Binding affinities of metal-ligand complexes are central to a multitude of applications like drug design, chelation therapy, designing reagents for solvent extraction etc. While state-of-the-art molecular modelling approaches are usually employed to gather structural and chemical insights about the metal complexation with ligands, their computational cost and the limited ability to predict metal-ligand stability constants with reasonable accuracy, renders them impractical to screen large chemical spaces. In this context, leveraging vast amounts of experimental data to learn the metal-binding affinities of ligands becomes a promising alternative. Here, we develop a machine learning framework for predicting binding affinities (logK1) of lanthanide cations with several structurally diverse molecular ligands. Six supervised machine learning algorithms-Random Forest (RF), k-Nearest Neighbours (KNN), Support Vector Machines (SVM), Kernel Ridge Regression (KRR), Multi Layered Perceptrons (MLP) and Adaptive Boosting (AdaBoost)-were trained on a dataset comprising thousands of experimental values of logK1 and validated in an external 10-folds cross-validation procedure. This was followed by a thorough feature engineering and feature importance analysis to identify the molecular, metallic and solvent features most relevant to binding affinity prediction, along with an evaluation of performance metrics against the dimensionality of feature space. Having demonstrated the excellent predictive ability of our framework, we utilized the best performing AdaBoost model to predict the logK1 values of lanthanide cations with nearly 71 million compounds present in the PubChem database. Our methodology opens up an opportunity for significantly accelerating screening and design of ligands for various targeted applications, from vast chemical spaces.