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

Dwadasi, Balarama Sridhar; Srinivasan, Sriram Goverapet; Rai, Beena

doi:10.1039/c9cp05719f

Cited by 11 publications

(6 citation statements)

References 80 publications

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“…As is known, the thickness of the interfacial region (defined as that both water and oil have >99% mass ratio) of oil-water biphase decreases with decreased polarity of oil phase. [26] As expected, the interfacial region with the presence of PBD molecules turns narrower from 3.8 to 2.3 nm and then to 1.9 nm with decreased polarity of oil phase (Figure 2K-M). Consequently, the distance for the evolution from PBD-PBD H-bonding to PBD-water Hbonding is gradually compressed, which is adverse to supporting the connection between nanospheres and nanofibers.…”

Section: Formation Mechanism Of the Janus Structure And Self-standing...supporting

confidence: 78%

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

Yi,

Qiu,

Sun

et al. 2023

Advanced Science

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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.

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Section: Formation Mechanism Of the Janus Structure And Self-standing...supporting

confidence: 78%

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

Yi,

Qiu,

Sun

et al. 2023

Advanced Science

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“…Among the most outstanding investigations, we can refer to studies on the reason for the differences in the stability of the Nd(III) and Dy(III) complexes with D2EHPA in a chloride medium and the physiochemical properties of the interface between aqueous and organic phases in this process using the molecular dynamics method [41][42].…”

Section: Introductionmentioning

confidence: 99%

A Deep Insight into the Selectivity Difference Between Y(III) and La(III) Ions Toward D2EHPA Ligand: Experimental and DFT Study

Alizadeh

Abdollahy

Darban

et al. 2023

Preprint

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The twofold extraction behavior of light and heavy rare earth elements transforms into a more selective extraction of heavy rare earth elements when Di-(2-ethylhexyl) phosphoric acid (D2EHPA), one of the commonest cation exchange extractants, is employed. However, why this phenomenon has not been fully investigated from the quantum perspective yet. To confirm and interpret the laboratory-observed selectivity results in the extraction of Y(III) over than La(III), this study utilized the Density Functional theory (DFT) connected with Born Haber thermodynamic besides importing the solvent effect through the Conductor-Like Screening Model (COSMO). The hydration reaction energies of La(III) and Y(III) were estimated at -383.7 kcal/mol and -171.83 kcal/mol according to the cluster solvation model. It was observed that, among other influential factors, hydration energy is a critical one in the rate of the extraction free energy of every rare earth element and its tendency to be transferred to the organic phase in reacting to the extractant ligand. It was shown that the experimental ∆∆Gext results (2.1 kcal/mol) enjoyed a proper consonance with the ∆∆Gext results of DFT calculations (1.3 kcal/mol). In the pursuit of discovering the reasons for this phenomenon, the orbital structure of every aqueous and organic complex was studied, and the significant differences in energy magnitudes were discussed. The current comprehensive design of experimental studies and calculations can give birth to a deeper understanding of the interactions of the D2EHPA extractant with La(III) and Y(III).

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“…The binding strength of a ligand to a metal ion depends on a number of factors including the nature of the molecule and the metal ion themselves, the solvent media, ionic strength of the media etc. For ligands that bind via a cation exchange mechanism (such phosphoric acid ligands), pH of the medium further becomes an important factor in determining the binding affinity, since deprotonation of the ligand is a necessary condition for the formation of an M-L complex 6 . Then, any successful design of ligand must necessarily incorporate information about the experimental conditions in addition to the nature of the metal ion itself.…”

Section: Introductionmentioning

confidence: 99%

Applied machine learning for predicting the lanthanide-ligand binding affinities

Chaube

Srinivasan

Rai

2020

Sci Rep

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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 metalligand 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 (logK 1) 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 logK 1 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 logK 1 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. Rare Earth Elements (REEs), that constitute the lanthanide block of the periodic table, together with Yttrium and Scandium, lie at the heart of many modern technologies in diverse fields ranging from health care to clean energy applications 1. With increasing adoption of clean and energy efficient technologies, the demand for REEs is expected to grow manifold in the coming years 2. Although conventional mining remains the primary source of global REE supply currently 3 , owing to the huge quantities of electronic waste (e-waste) generated, REE recovery from e-wastes becomes a promising secondary source of these critical elements 4. Much of the metal processing industry relies upon hydrometallurgical operations such as liquid-liquid extraction (LLE) to recover the target element 5. The success of an LLE operation depends critically on the choice of ligands that can selectively bind to one or more target metal ions and transport them into an oil phase in contact with an aqueous phase which originally contained the metal ions. Thus, successful recovery of REEs from e-wastes calls for the design of ligands with a high affinity for one or more target lanthanide ions. The binding strength of a ...

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

Abstract: MD simulations reveal the chemical and physical heterogeneity at the liquid–liquid interface, nature of complexes formed by phosphoric acid ligands with lanthanides, and the sequence of events in the extraction of these ions.

Cited by 11 publications

References 80 publications

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

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

A Deep Insight into the Selectivity Difference Between Y(III) and La(III) Ions Toward D2EHPA Ligand: Experimental and DFT Study

Applied machine learning for predicting the lanthanide-ligand binding affinities

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