Using Diglycolamide Extractants in an Imidazolium-Based Ionic Liquid for Rare Earth Element Extraction and Recovery
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Engineering topics
Windus, Theresa L.
Publications and source records attributed to Windus, Theresa L..
DFT Analysis of the Binding of Rare Earth Nitrates at Internal and External Surfaces of MCM-22
MCM-22-type zeolites constitute a well-characterized tunable class of aluminosilicates suitable for elucidating the fundamental aspects of the binding of rare earth elements (REEs) in layered materials. Starting from the pure silica version ITQ-1 with a unit cell of Si 72 O 144 , a model for periodic bulk crystalline MCM-22 with a finite Al concentration is provided by replacing a Si atom with an Al atom in the unit cell at suitable tetrahedral sites near an internal pore surface. Then, a H atom is added to an O atom bridging Si and Al atoms to create a Brønsted acid site (BAS). There are no internal silanol groups in this bulk model. To generate a model for an external surface, we adopt the fully hydroxylated surface structure of a layer within the ITQ-1 precursor with two silanols per lateral unit cell. A BAS on the external surface can be generated by replacing a near-surface Si atom with an Al atom and adding a H atom, as above. The strength of binding at a BAS of REE, X, taken to be present in the solution phase as nitrates, is determined from the energy change in the reaction X(NO 3 ) 3 + ≡Si–{OH}–Al≡ → ≡Si–{OX(NO 3 ) 2 }–Al≡+ HNO 3 . The strength of binding at the silanols is determined similarly. Binding energies are determined from two approaches. The first performs periodic plane-wave density functional theory (DFT) total energy analysis for an entire unit cell of MCM-22. The second utilizes cluster models capturing the local environment of REE binding sites and performs DFT analysis with localized basis sets. The two approaches yield consistent results for Nd, revealing similarly strong binding at either an external or internal BAS, but much weaker binding at a silanol site. This is consistent with the picture deduced from recent experiments. Here, we also comment on binding at Al-bridged siloxane sites, which have been suggested as alternative binding sites to BAS.
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CMaize: Simplifying inter-package modularity from the build up
There is a growing desire for inter-package modularity within the chemistry software community to reuse encapsulated code units across a variety of software packages. Most comprehensive efforts at achieving inter-package modularity will quickly run afoul of a very practical problem, being able to cohesively build the modules. Writing and maintaining build systems has long been an issue for many scientific software packages that rely on compiled languages such as C/C++. The push for inter-package modularity compounds this issue by additionally requiring binary artifacts from disparate developers to interoperate at a binary level. Thankfully, the de facto build tool for C/C++, CMake, is more than capable of supporting the myriad of edge cases that complicate writing robust build systems. Unfortunately, writing and maintaining a robust CMake build system can be a laborious endeavor because CMake provides few abstractions to aid the developer. Further, the need to significantly simplify the process of writing robust CMake-based build systems, especially in inter-package builds, motivated us to write CMaize. In addition to describing the architecture and design of CMaize, the article also demonstrates how CMaize is used in production-level software.
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Prediction of stability constants of metal–ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity
The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted log K values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal–ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R 2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R 2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R 2 of 0.959 ± 0.007). For relative log K predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. Here we showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.
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Special Topic on High Performance Computing in Chemical Physics
Computational modeling and simulation have become indispensable scientific tools in virtually all areas of chemical, biomolecular, and materials systems research. Computation can provide unique and detailed atomic level information that is difficult or impossible to obtain through analytical theories and experimental investigations. In addition, recent advances in micro-electronics have resulted in computer architectures with unprecedented computational capabilities, from the largest supercomputers to common desktop computers. In conclusion, combined with the development of new computational domain science methodologies and novel programming models and techniques, this has resulted in modeling and simulation resources capable of providing results at or better than experimental chemical accuracy and for systems in increasingly realistic chemical environments.
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DFT-based investigation of solvation of Nd(III)/Yb(III) cations in room-temperature ionic-liquid
We report a DFT study on the solvation of bare rare-earth (RE) cations [Nd(III) and Yb(III)] in the room-temperature ionic-liquid (RTIL) based on 1,3-dimethyl-imidazolium + , PF 6 - , [MMI][PF 6 ]. In the present article, we aim to investigate whether the chosen RTIL can preferentially solvate any of the RE cations under study. First, the RTIL binding to the RE cation is studied in the gas phase, and then explored with continuum solvation model employing RTIL parameters. First, we considered the formation of the first solvation shell, where the PF 6 - anions directly bond with the Ln(III) cation. We then further surrounded the first solvation shell with [MMI]+ cations to form the second solvation shell. The stability of PF 6 - coordinated complexes is studied by calculating the binding energy values. The number of PF 6 - anions for both Ln(III) cations starts with three ligands and goes until the maximum binding energy in the RTIL medium is reached. The coordination number and binding modes of PF 6 - depend on the size of the RE cation. Comparing the binding energy values between the RE cations for the first solvation shell complexes, it is noticed that Yb-complexes have relatively greater binding energy than Nd-complexes. We have further performed a Boltzmann population analysis to find out the most dominant conformer among each category of PF 6 - coordinated complexes. A study using Natural Population Analysis shows that after complexation with PF 6 - the charge on the Yb-centers lower to a greater extent than Nd-centers, implying stronger bonding between Yb and PF 6 - , which is in harmony with the binding energy analysis. Overall, the computational investigation provides an in-depth understanding of the complexation phenomena of RE cations in RTIL, which can be useful for the practical separation of RE cations using RTIL.
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A Perspective on Sustainable Computational Chemistry Software Development and Integration
The power of quantum chemistry to predict the ground and excited state properties of complex chemical systems has driven the development of computational quantum chemistry software, integrating advances in theory, applied mathematics, and computer science. The emergence of new computational paradigms associated with exascale technologies also poses significant challenges that require a flexible forward strategy to take full advantage of existing and forthcoming computational resources. In this context, the sustainability and interoperability of computational chemistry software development are among the most pressing issues. In this perspective, we discuss software infrastructure needs and investments with an eye to fully utilize exascale resources and provide unique computational tools for next-generation science problems and scientific discoveries.
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