Diffusionproperties of bulk fluids have been predicted using empiricalexpressions and machine learning (ML) models, suggesting that predictionsof diffusion also should be possible for fluids in confined environments.The ability to quickly and accurately predict diffusion in porousmaterials would enable new discoveries and spur development in relevanttechnologies such as separations, catalysis, batteries, and subsurfaceapplications. In this work, we apply artificial neural network (ANN)models to predict the simulated self-diffusion coefficients of realliquids in both bulk and pore environments. The training data setswere generated from molecular dynamics (MD) simulations of Lennard-Jonesparticles representing a diverse set of 14 molecules ranging fromammonia to dodecane over a range of liquid pressures and temperatures.Planar, cylindrical, and hexagonal pore models consisted of wallscomposed of carbon atoms. Our simple model for these liquids was primarilyused to generate ANN training data, but the simulated self-diffusioncoefficients of bulk liquids show excellent agreement with experimentaldiffusion coefficients. ANN models based on simple descriptors accuratelyreproduced the MD diffusion data for both bulk and confined liquids,including the trend of increased mobility in large pores relativeto the corresponding bulk liquid.

Fluorinated graphite materials are of interest for anassortmentof applications and can be synthesized under a variety of syntheticconditions from many different types of carbon. Due to such variations,structural disorders in the form of defects and polymorphism are oftenpresent. Here, we investigate the impact of local structural variationson the C-F bond dissociation energies (BDEs) in carbon-basedfluoride materials using density functional theory (DFT) computationalmethods. Employing fluorographene (FG) cluster models, we determinethe impact of different C-F bonding configurations in the coreof each platelet on the equilibrium BDEs for each C-F bond.The introduction of structural disorder decreases the first C-FBDE by approximately 1 eV compared to the canonical arrangement ofaxial C-F bonds ordered as in a network of cyclohexane "chairs".Variability of calculated BDEs among the different polymorphs decreasesupon subsequent F removal. Common structural tendencies of the adiabaticdefluorination pathways for each polymorph are identified. Our analysissuggests that at F/C ratios near 1.0, disorder in the local structurecan play a significant role in the energetics of the initial carbonfluoride defluorination and that the influence of this configurationaldisorder diminishes with decreasing F/C ratios.

Solid state nuclear magnetic resonance (NMR) spectroscopy and small-to wide-angle X-ray scattering (SWAXS) methods were used to characterize the heterogeneous dynamics and polymer domain structure in rubber modified thermoset materials containing the diglycidyl ether of bisphenol A (DGEBA) epoxy resin and a mixture of Jeffamine reactive rubber and 4,4-diaminodicyclohexylmethane (PACM) amine curing agent. The polymer chain dynamics and morphologies as a function of the PACM/Jeffamine ratio were determined. Using dipolar-filtered NMR experiments, the resulting networks are shown to be composed of mobile and rigid regions that are separated on nanometer length scales, along with a dynamically immobilized interface region. Proton NMR spin diffusion experiments measured the dimensions of the mobile phase to range between 9 and 66 nm and varied with the relative PACM concentration. Solid state 13C magic angle spinning NMR experiments show that the highly mobile phase is composed entirely of the dynamically flexible polyether chains of the Jeffamine rubber, the immobilized interface region is a mixture of DGEBA, PACM, and the Jeffamine rubber, with the PACM cross-linked to DGEBA predominantly residing in the rigid phase. The SWAXS results showed compositional nanophase separation spanning the 11–77 nm range. These measurements of the nanoscale compositional and dynamic heterogeneity provide molecular level insight into the very broad and controllable glass transition temperature distributions observed for these highly cross-linked polymer networks.

Artificial neural networks (ANNs) were developed to accurately predict the self-diffusion constants for individual components in binary fluid mixtures. The ANNs were tested on an experimental database of 4328 self-diffusion constants from 131 mixtures containing 75 unique compounds. The presence of strong hydrogen bonding molecules may lead to clustering or dimerization resulting in non-linear diffusive behavior. To address this, self- and binary association energies were calculated for each molecule and mixture to provide information on intermolecular interaction strength and were used as input features to the ANN. An accurate, generalized ANN model was developed with an overall average absolute deviation of 4.1%. Forward input feature selection reveals the importance of critical properties and self-association energies along with other fluid properties. Additional ANNs were developed with subsets of the full input feature set to further investigate the impact of various properties on model performance. The results from two specific mixtures are discussed in additional detail: one providing an example of strong hydrogen bonding and the other an example of extreme pressure changes, with the ANN models predicting self-diffusion well in both cases.

A new solid-state 19F magic-angle spinning NMR signal at an isotropic 19F chemical shift of −53 ppm is measured from graphite fluoride synthesized by reaction of graphite with F2 at temperatures above 750 K with no catalyst. Two-dimensional NMR suggests the −53 ppm 19F NMR signal originates from covalent fluoromethanetriyl groups belonging to ordered (CyF)n bulk domains composited with the major (CF)n domains. Quantitative 19F and 13C NMR find y=4.32±0.64. DFT calculations of NMR chemical shifts for unsaturated fluorographene models show that a (C4F)n phase with fluorine bound covalently to a single side of the carbon layer best explains the observed NMR chemical shifts. We assign the new phase to this (C4F)n structure, which constitutes up to 15% of the carbon in our graphite fluoride composites. The (C4F)n content of the composite affects bulk electrochemical properties in a manner similar to graphite fluorides produced by conventional, catalyzed fluorination processes.

A chelator-mediated Fenton reaction efficiently cleaves C–C bonds in sulfonated lignin at or near room temperature and the depolymerized streams are compatible with microbial conversion.

Symbolic regression (SR) with a multi-gene genetic program has been used to elucidate new empirical equations describing diffusion in Lennard-Jones (LJ) fluids. Examples include equations to predict self-diffusion in pure LJ fluids and equations describing the finite-size correction for self-diffusion in binary LJ fluids. The performance of the SR-obtained equations was compared to that of both the existing empirical equations in the literature and to the results from artificial neural net (ANN) models recently reported. It is found that the SR equations have improved predictive performance in comparison to the existing empirical equations, even though employing a smaller number of adjustable parameters, but show an overall reduced performance in comparison to more extensive ANNs.

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.

Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid-wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R2) of -,0.99 and a mean squared error of -,2.9 x 10-5. Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.

The role of local molecular structure on calculated C-13 and F-19 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.

continued safe operation under normal and design basis events (DBE). Normal component and system tests typically confirm the cables can perform their normal operational function. The focus of the cable test program, however, is directed toward the more demanding challenge of assuring the cable function under accident or DBE. The industry has adopted 50% elongation at break (EAB) relative to the un-aged cable condition as the acceptability standard. All tests are benchmarked against the cable EAB test. EAB, however, is a destructive test so the test programs must apply an array of other nondestructive examination (NDE) tests to assure or infer the overall set of cable’s system integrity. Assessment of cable integrity is further complicated in many cases by vendor’s use of dissimilar material for jacket and insulation. Frequently the jacket will degrade more rapidly than the underlying insulation. Although this can serve as an early alert to cable damage, direct test of the cable insulation without violating the protective jacket becomes problematic. This report addresses the range of bulk electrical NDE cable tests that are or could be practically implemented in a field-test situation with a particular focus on frequency domain reflectometry (FDR). The FDR test method offers numerous advantages over many other bulk electrical tests. Two commercial FDR systems plus a laboratory vector network analyzer are used to test an array of aged and un-aged cables under identical conditions. Several conclusions are set forth, and a number of knowledge gaps are identified.

The ability to predict transport properties of liquids quickly and accurately will greatly improve our understanding of fluid properties both in bulk and complex mixtures, as well as in confined environments. Such information could then be used in the design of materials and processes for applications ranging from energy production and storage to manufacturing processes. As a first step, we consider the use of machine learning (ML) methods to predict the diffusion properties of pure liquids. Recent results have shown that Artificial Neural Networks (ANNs) can effectively predict the diffusion of pure compounds based on the use of experimental properties as the model inputs. In the current study, a similar ANN approach is applied to modeling diffusion of pure liquids using fluid properties obtained exclusively from molecular simulations. A diverse set of 102 pure liquids is considered, ranging from small polar molecules (e.g., water) to large nonpolar molecules (e.g., octane). Self-diffusion coefficients were obtained from classical molecular dynamics (MD) simulations. Since nearly all the molecules are organic compounds, a general set of force field parameters for organic molecules was used. The MD methods are validated by comparing physical and thermodynamic properties with experiment. Computational input features for the ANN include physical properties obtained from the MD simulations as well as molecular properties from quantum calculations of individual molecules. Fluid properties describing the local liquid structure were obtained from center of mass radial distribution functions (COM-RDFs). Feature sensitivity analysis revealed that isothermal compressibility, heat of vaporization, and the thermal expansion coefficient were the most impactful properties used as input for the ANN model to predict the MD simulated self-diffusion coefficients. The MD-based ANN successfully predicts the MD self-diffusion coefficients with only a subset (2 to 3) of the available computationally determined input features required. A separate ANN model was developed using literature experimental self-diffusion coefficients as model targets. Although this second ML model was not as successful due to a limited number of data points, a good correlation is still observed between experimental and ML predicted self-diffusion coefficients.

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.

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 19600s 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 alpha-Zr phosphate and alpha-Hf phosphate, and 2) amorphous analogues of these. Powder X-ray diffraction, thermogravimetry, solid-state P-31 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)(H2O)-H-1 and M(HPO4)(2)(H2O)-H-4 (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 Cs-133 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 a-Hf phosphate is better Cs-sorbent than crystalline a-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. (C) 2021 Elsevier Ltd. All rights reserved.

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.