Combining density functional theory and 23Na NMR to characterize Na2FePO4F as a potential sodium ion battery cathode

Combined 7Li NMR, density functional theory and operando synchrotron X-ray powder diffraction to investigate a structural evolution of cathode material LiFeV2O7

In our recent study, we demonstrated using 7Li solid-state Nuclear Magnetic Resonance (ssNMR) and single-crystal X-ray diffraction that the cathode LiFeV2O7 possesses a defect associated with the positioning of vanadium atoms. We proposed that this defect could be the source of extra signals detected in the 7Li spectra. In this context, we now apply density functional theory (DFT) calculations to assign the experimental signals observed in 7Li NMR spectra of the pristine sample. The calculation results are in strong agreement with the experimental observations. DFT calculations are a useful tool to interpret the observed paramagnetic shifts and understand how the presence of disorder affects the spectra behavior through the spin-density transfer processes. Furthermore, we conducted a detailed study of the lithiated phase combining operando synchrotron powder X-ray diffraction (SPXRD) and DFT calculations. A noticeable volume expansion is observed through the first discharge cycle which likely contributes to the enhanced lithium dynamics in the bulk material, as supported by previously published ssNMR data. DFT calculations are used to model the lithiated phase and demonstrate that both iron and vanadium participate in the redox process. The unusual electronic structure of the V4+ exhibits a single electron on the 3dxy orbital perpendicular to the V-O-Li bond being a source of a negative Fermi contact shift observed in the 7Li NMR of the lithiated phase.

A combined solid-state 1H, 13C, 17O NMR and periodic DFT study of hyperfine coupling tensors in paramagnetic copper(II) compounds

We report solid-state 1H, 13C, and 17O NMR determination of hyperfine coupling tensors (A-tensors) in several paramagnetic Cu(II) (d9, S = 1/2) complexes: trans-Cu(DL-Ala)2·H217O, Cu([1-13C]acetate)2·H2O, Cu([2-13C]acetate)2·H2O, and Cu(acetate)2·H217O. Using these new experimental results and some A-tensor data available in the literature for trans-Cu(L-Ala)2 and K2CuCl4·2H2O, we were able to examine the accuracy of A-tensor computation from a periodic DFT method implemented in the BAND program. We evaluated A-tensors on 1H (I = 1/2), 13C (I = 1/2), 14N (I = 1), 17O (I = 5/2), 39K (I = 3/2), 35Cl (I = 3/2), and 63Cu (I = 3/2) nuclei over a range spanning more than 3 orders of magnitude. We found that the BAND code can reproduce reasonably well the experimental results for both A-tensors and nuclear quadrupole coupling tensors.

A machine learning protocol for revealing ion transport mechanisms from dynamic NMR shifts in paramagnetic battery materials

Solid-state nuclear magnetic resonance (ssNMR) provides local environments and dynamic fingerprints of alkali ions in paramagnetic battery materials. Linking the local ionic environments and NMR signals requires expensive first-principles computational tools that have been developed for over a decade. Nevertheless, the assignment of the dynamic NMR spectra of high-rate battery materials is still challenging because the local structures and dynamic information of alkali ions are highly correlated and difficult to acquire. Herein, we develop a novel machine learning (ML) protocol that could not only quickly sample atomic configurations but also predict chemical shifts efficiently, which enables us to calculate dynamic NMR shifts with the accuracy of density functional theory (DFT). Using structurally well-defined P2-type Na_2/3(Mg_1/3Mn_2/3)O₂ as an example, we validate the ML protocol and show the significance of dynamic effects on chemical shifts. Moreover, with the protocol, it is demonstrated that the two experimental ²³Na shifts (1406 and 1493 ppm) of P2-type Na_2/3(Ni_1/3Mn_2/3)O₂ originate from two stacking sequences of transition metal (TM) layers for the first time, which correspond to space groups P6₃/mcm and P6₃22, respectively. This ML protocol could help to correlate dynamic ssNMR spectra with the local structures and fast transport of alkali ions and is expected to be applicable to a wide range of fast dynamic systems.

We developed a widely applicable machine learning (ML) method that can help to correlate dynamic ssNMR spectra with the local structures and transport of ions and thus expands the ssNMR application to fast chemically exchanged material systems.

Selective, High-Temperature O2 Adsorption in Chemically Reduced, Redox-Active Iron-Pyrazolate Metal-Organic Frameworks

Developing O2-selective adsorbents that can produce high-purity oxygen from air remains a significant challenge. Here, we show that chemically reduced metal-organic framework materials of the type AxFe2(bdp)3 (A = Na+, K+; bdp2- = 1,4-benzenedipyrazolate; 0 < x ≤ 2), which feature coordinatively saturated iron centers, are capable of strong and selective adsorption of O2 over N2 at ambient (25 °C) or even elevated (200 °C) temperature. A combination of gas adsorption analysis, single-crystal X-ray diffraction, magnetic susceptibility measurements, and a range of spectroscopic methods, including 23Na solid-state NMR, Mössbauer, and X-ray photoelectron spectroscopies, are employed as probes of O2 uptake. Significantly, the results support a selective adsorption mechanism involving outer-sphere electron transfer from the framework to form superoxide species, which are subsequently stabilized by intercalated alkali metal cations that reside in the one-dimensional triangular pores of the structure. We further demonstrate O2 uptake behavior similar to that of AxFe2(bdp)3 in an expanded-pore framework analogue and thereby gain additional insight into the O2 adsorption mechanism. The chemical reduction of a robust metal-organic framework to render it capable of binding O2 through such an outer-sphere electron transfer mechanism represents a promising and underexplored strategy for the design of next-generation O2 adsorbents.