Battery cells with metal casings are commonly considered incompatible with nuclear magnetic resonance (NMR) spectroscopy because the oscillating radio-frequency magnetic fields ("rf fields") responsible for excitation and detection of NMR active nuclei do not penetrate metals. Here, we show that rf fields can still efficiently penetrate nonmetallic layers of coin cells with metal casings provided "B1 damming"configurations are avoided. With this understanding, we demonstrate noninvasive high-field in situ 7Li and 19F NMR of coin cells with metal casings using a traditional external NMR coil. This includes the first NMR measurements of an unmodified commercial off-the-shelf rechargeable battery in operando, from which we detect, resolve, and separate 7Li NMR signals from elemental Li, anodic β-LiAl, and cathodic LixMnO2 compounds. Real-time changes of β-LiAl lithium diffusion rates and variable β-LiAl 7Li NMR Knight shifts are observed and tied to electrochemically driven changes of the β-LiAl defect structure.
Poly(carbon monofluoride), or (CF)n, is a layered fluorinated graphite material consisting of nanosized platelets. Here, we present experimental multidimensional solid-state NMR spectra of (CF)n, supported by density functional theory (DFT) calculations of NMR parameters, which overhauls our understanding of structure and bonding in the material by elucidating many ways in which disorder manifests. We observe strong 19F NMR signals conventionally assigned to elongated or "semi-ionic"C-F bonds and find that these signals are in fact due to domains where the framework locally adopts boat-like cyclohexane conformations. We calculate that C-F bonds are weakened but are not elongated by this conformational disorder. Exchange NMR suggests that conformational disorder avoids platelet edges. We also use a new J-resolved NMR method for disordered solids, which provides molecular-level resolution of highly fluorinated edge states. The strings of consecutive difluoromethylene groups at edges are relatively mobile. Topologically distinct edge features, including zigzag edges, crenellated edges, and coves, are resolved in our samples by solid-state NMR. Disorder should be controllable in a manner dependent on synthesis, affording new opportunities for tuning the properties of graphite fluorides.
Nagasaka, Cocoro A.; Kozma, Karoly; Brunson, Kieran G.; Russo, Chris J.; Alam, Todd M.; Nyman, May
Removal of radioactive Cs from sodium-rich solutions is a technical challenge that goes back to post World War II nuclear waste storage and treatment; and interest in this topic was reinvigorated by the Fukushima-Daiichi nuclear power plant disaster, 10 years ago. Since the 1960′s there has been considerable focus on layered Zr phosphates as robust inorganic sorbents for separation of radionuclides such as Cs. Here we present synthesis and characterization, and direct comparison of Cs sorption capacity and selectivity of four related materials: 1) crystalline α-Zr phosphate and α-Hf phosphate, and 2) amorphous analogues of these. Powder X-ray diffraction, thermogravimetry, solid-state 31P magic angle spinning nuclear magnetic resonance (MAS-NMR) spectroscopy, and compositional analysis (inductively coupled plasma optical emission spectroscopy and mass spectroscopy, ICP OES and ICP MS) provided formulae; respectively M(HPO4)2⋅1H2O and M(HPO4)2⋅4H2O (M = Hf, Zr) for crystalline and amorphous analogues. Maximum Cs loading, competitive Cs-Na selectivity and maximum Cs-Na loading followed by the above characterizations plus 133Cs MAS-NMR spectroscopy revealed that amorphous analogues are considerably better Cs-sorbents (based on maximum Cs-loading and selectivity over Na) than the well-studied crystalline Zr-analogue. Additionally, crystalline α-Hf phosphate is better Cs-sorbent than crystalline α-Zr phosphate. All these studies consistently show that Hf phosphate is less crystallize than Zr phosphate, when obtained under similar or identical synthesis conditions. We attribute this to lower solubility of Hf phosphate compared to Zr phosphate, preventing ‘defect healing’ during the synthesis process.
Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for pure components in liquid, gas and super critical phases. The ANNs were tested on an experimental database of 6625 self-diffusion constants for 118 different chemical compounds. The presence of multiple phases results in a heavy skew in the distribution of diffusion constants and multiple approaches were used to address this challenge. First, an ANN was developed with the raw diffusion values to assess what the main drawbacks of this direct method were. The first approach for improving the predictions involved taking the log 10 of diffusion to provide a more uniform distribution and reduce the range of target output values used to develop the ANN. The second approach involved developing individual ANNs for each phase using the raw diffusion values. Results show that the log transformation leads to a model with the best self-diffusion constant predictions and an overall average absolute deviation (AAD) of 6.56%. The resultant ANN is a generalized model that can be used to predict diffusion across all three phases and over a diverse group of compounds. The importance of each input feature was ranked using a feature addition method revealing that the density of the compound has the largest impact on the ANN prediction of self-diffusion constants in pure compounds.
The role of local molecular structure on calculated 13C and 19F NMR chemical shifts for graphite fluoride materials was explored by using gauge-including projector augmented wave (GIPAW) computational methods for different periodic crystal polymorphs and density functional theory (DFT) gauge-including atomic orbital (GIAO) computational methods for individual graphite fluoride platelets, i.e., fluorinated graphene (FG). The impact of stacking sequences, d-spacing, and ring conformations on fully fluorinated graphite fluoride structures was investigated. A range of different defects including Stone-Wales, F and C vacancies, void formation, and F inversion were also evaluated using FG structures. These calculations show that distinct chemical shift signatures exist for many of these polymorphs and defects, therefore providing a basis for spectral assignment and development of models describing the mean local CF structure in disordered graphite fluoride materials.
Molecular diffusion coefficients calculated using molecular dynamics (MD) simulations suffer from finite-size (i.e., finite box size and finite particle number) effects. Results from finite-sized MD simulations can be upscaled to infinite simulation size by applying a correction factor. For self-diffusion of single-component fluids, this correction has been well-studied by many researchers including Yeh and Hummer (YH); for binary fluid mixtures, a modified YH correction was recently proposed for correcting MD-predicted Maxwell-Stephan (MS) diffusion rates. Here we use both empirical and machine learning methods to identify improvements to the finite-size correction factors for both self-diffusion and MS diffusion of binary Lennard-Jones (LJ) fluid mixtures. Using artificial neural networks (ANNs), the error in the corrected LJ fluid diffusion is reduced by an order of magnitude versus existing YH corrections, and the ANN models perform well for mixtures with large dissimilarities in size and interaction energies where the YH correction proves insufficient.
In the subsurface, MgO engineered barriers are employed at the Waste Isolation Pilot Plant (WIPP), a transuranic waste repository near Carlsbad, NM. During service, the MgO will be exposed to high concentration brine environments and may form stable intermediate phases that can alter the barriers effectiveness. Here, MgO was aged in water and three different brine solutions. X-ray diffraction (XRD) and 1H nuclear magnetic resonance (NMR) analysis were performed to identify the formation of secondary phases. After aging, ~4% of the MgO was hydrated and fine-grained powders resulted in greater loss of crystallinity than hard granular grains. 1H magic angle spinning (MAS) NMR spectra resolved minor phases not visible in XRD, indicating that diverse 1H environments are present along with Mg(OH)2. Density functional theory (DFT) simulations for several proposed Mg-O-H, Mg-CI-O-H, and Na-O-H containing phases were performed to index peaks in the experimental 1H MAS NMR spectra. While proposed intermediate crystal structures exhibited overlapping 1H NMR peaks, Mg-O-H intermediates were attributed to the growth of the 1.0-0.0ppm peak while the Mg-CI-O-H structures contributed to the 2.5- 5.0ppm peak in the chloride containing brines. Overall, NMR analysis of aged MgO indicates the formation of a range of possible intermediate structures that cannot be resolved with XRD analysis alone.
Elinburg, Jessica K.; Hyre, Ariel S.; McNeely, James; Alam, Todd M.; Klenner, Steffen; Pöttgen, Rainer; Rheingold, Arnold L.; Doerrer, Linda H.
The synthesis and characterization of a series of Sn(ii) and Sn(iv) complexes supported by the highly electron-withdrawing dianionic perfluoropinacolate (pinF) ligand are reported herein. Three analogs of [SnIV(pinF)3]2- with NEt3H+ (1), K+ (2), and {K(18C6)}+ (3) counter cations and two analogs of [SnII(pinF)2]2- with K+ (4) and {K(15C5)2}+ (5) counter cations were prepared and characterized by standard analytical methods, single-crystal X-ray diffraction, and 119Sn Mössbauer and NMR spectroscopies. The six-coordinate SnIV(pinF) complexes display 119Sn NMR resonances and 119Sn Mössbauer spectra similar to SnO2 (cassiterite). In contrast, the four-coordinate SnII(pinF) complexes, featuring a stereochemically-active lone pair, possess low 119Sn NMR chemical shifts and relatively high quadrupolar splitting. Furthermore, the Sn(ii) complexes are unreactive towards both Lewis bases (pyridine, NEt3) and acids (BX3, Et3NH+). Calculations confirm that the Sn(ii) lone pair is localized within the 5s orbital and reveal that the Sn 5px LUMO is energetically inaccessible, which effectively abates reactivity. This journal is
A series of networks is introduced with systematically varied network heterogeneity and high overall values of average glass transition temperature (Tg), based on polymerization of rigid acrylate and aromatic thiol monomers. The curing behavior, chain dynamics, and microstructure of these networks were investigated through a combination of dynamic mechanical analysis and infrared spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and x-ray scattering, respectively. The maximum Tg achieved during cure can be related to the breadth of the mechanical loss tangent, as others have previously suggested, as well as the temperature dependence of the chain dynamics in the network as monitored by 1H NMR. In addition, the microstructures of the networks are characterized by periodic, fractal microgels with characteristic length scales of ca. 20–40 nm. Intriguingly, this structural motif persists in the more homogeneous networks exhibiting comparatively narrow glass transitions and chain dynamics, indicating that dynamically homogeneous networks can still exhibit significant compositional heterogeneity at the mesoscale.
NMR spectroscopy continues to provide important molecular level details of dynamics in different polymer materials, ranging from rubbers to highly crosslinked composites. It has been argued that thermoset polymers containing dynamic and chemical heterogeneities can be fully cured at temperatures well below the final glass transition temperature (Tg). In this paper, we described the use of static solid-state 1H NMR spectroscopy to measure the activation of different chain dynamics as a function of temperature. Near Tg, increasing polymer segmental chain fluctuations lead to dynamic averaging of the local homonuclear proton-proton (1H-1H) dipolar couplings, as reflected in the reduction of the NMR line shape second moment (M2) when motions are faster than the magnitude of the dipolar coupling. In general, for polymer systems, distributions in the dynamic correlation times are commonly expected. To help identify the limitations and pitfalls of M2 analyses, the impact of activation energy or, equivalently, correlation time distributions, on the analysis of 1H NMR M2 temperature variations is explored. It is shown by using normalized reference curves that the distributions in dynamic activation energies can be measured from the M2 temperature behavior. An example of the M2 analysis for a series of thermosetting polymers with systematically varied dynamic heterogeneity is presented and discussed.
Different machine learning (ML) methods were explored for the prediction of self-diffusion in Lennard-Jones (LJ) fluids. Using a database of diffusion constants obtained from the molecular dynamics simulation literature, multiple Random Forest (RF) and Artificial Neural Net (ANN) regression models were developed and characterized. The role and improved performance of feature engineering coupled to the RF model development was also addressed. The performance of these different ML models was evaluated by comparing the prediction error to an existing empirical relationship used to describe LJ fluid diffusion. It was found that the ANN regression models provided superior prediction of diffusion in comparison to the existing empirical relationships.
Materials often contain minor heterogeneous phases that are difficult to characterize yet nonetheless significantly influence important properties. Here we describe a solid-state NMR strategy for quantifying minor heterogenous sample regions containing dilute, essentially uncoupled nuclei in materials where the remaining nuclei experience heteronuclear dipolar couplings. NMR signals from the coupled nuclei are dephased while NMR signals from the uncoupled nuclei can be amplified by one or two orders of magnitude using Carr-Meiboom-Purcell-Gill (CPMG) acquisition. The signal amplification by CPMG can be estimated allowing the concentration of the uncoupled spin regions to be determined even when direct observation of the uncoupled spin NMR signal in a single pulse experiment would require an impractically long duration of signal averaging. We use this method to quantify residual graphitic carbon using13 C CPMG NMR in poly(carbon monofluoride) samples synthesized by direct fluorination of carbon from various sources. Our detection limit for graphitic carbon in these materials is better than 0.05 mol%. The accuracy of the method is discussed and comparisons to other methods are drawn.