Trends in Ionic Liquid Diffusion in Graphite Slit Pores from Molecular Dynamics Simulation and Machine Learning
Explore the source record for details and available documents.
Greathouse, Jeffery A.↗
Engineering topics
Alam, Todd M.
Publications and source records attributed to Alam, Todd M..
Impact of Disorder and Defects on Bond Energies in Highly Fluorinated Graphene
Fluorinated graphite materials are of interest for an assortment of applications and can be synthesized under a variety of synthetic conditions from many different types of carbon. Due to such variations, structural disorders in the form of defects and polymorphism are often present. Here, we investigate the impact of local structural variations on the C–F bond dissociation energies (BDEs) in carbon-based fluoride materials using density functional theory (DFT) computational methods. Employing fluorographene (FG) cluster models, we determine the impact of different C–F bonding configurations in the core of each platelet on the equilibrium BDEs for each C–F bond. The introduction of structural disorder decreases the first C–F BDE by approximately 1 eV compared to the canonical arrangement of axial C–F bonds ordered as in a network of cyclohexane “chairs”. Variability of calculated BDEs among the different polymorphs decreases upon subsequent F removal. Common structural tendencies of the adiabatic defluorination pathways for each polymorph are identified. In conclusion, our analysis suggests that at F/C ratios near 1.0, disorder in the local structure can play a significant role in the energetics of the initial carbon fluoride defluorination and that the influence of this configurational disorder diminishes with decreasing F/C ratios.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Machine Learning Predictions of Simulated Self-Diffusion Coefficients for Bulk and Confined Pure Liquids
Diffusion properties of bulk fluids have been predicted using empirical expressions and machine learning (ML) models, suggesting that predictions of diffusion also should be possible for fluids in confined environments. The ability to quickly and accurately predict diffusion in porous materials would enable new discoveries and spur development in relevant technologies such as separations, catalysis, batteries, and subsurface applications. Here in this work, we apply artificial neural network (ANN) models to predict the simulated self-diffusion coefficients of real liquids in both bulk and pore environments. The training data sets were generated from molecular dynamics (MD) simulations of Lennard-Jones particles representing a diverse set of 14 molecules ranging from ammonia to dodecane over a range of liquid pressures and temperatures. Planar, cylindrical, and hexagonal pore models consisted of walls composed of carbon atoms. Our simple model for these liquids was primarily used to generate ANN training data, but the simulated self-diffusion coefficients of bulk liquids show excellent agreement with experimental diffusion coefficients. ANN models based on simple descriptors accurately reproduced the MD diffusion data for both bulk and confined liquids, including the trend of increased mobility in large pores relative to the corresponding bulk liquid.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Tuning polymerization-induced self-assembly in polymer thermosets to optimize their physical properties
Explore the source record for details and available documents.
Jones, Brad Howard↗
Symbolic regression development of empirical equations for diffusion in Lennard-Jones fluids
Symbolic regression (SR) with a multi-gene genetic program has been used to elucidate new empirical equations describing diffusion in Lennard-Jones (LJ) fluids. Some 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.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Prediction of Self-Diffusion in Binary Fluid Mixtures Using Artificial Neural 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.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Dynamic heterogeneity and nanophase separation in rubber-toughened amine-cured highly cross-linked polymer networks
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 13 C 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.
36 MATERIALS SCIENCE↗
Depolymerization of lignin for biological conversion through sulfonation and a chelator-mediated Fenton reaction
The generating value from lignin through depolymerization and biological conversion to valuable fuels, chemicals, or intermediates has great promise but is limited by several factors including lack of cost-effective depolymerization methods, toxicity within the breakdown products, and low bioconversion of the breakdown products. High yield depolymerization of natural lignins requires cleaving carbon-carbon bonds in addition to ether bonds. To address that need, we report that a chelator-mediated Fenton reaction can efficiently cleave C-C bonds in sulfonated polymers at or near room temperature, and that unwanted repolymerization can be minimized through optimizing reaction conditions. This method was used to depolymerize lignosulfonate from M w = 28,000 g/mol to M w = 800 g/mol. The breakdown products were characterized by SEC, FTIR and NMR and evaluated for bioavailability. The breakdown products are rich in acid, aldehyde, and alcohol functionalities but are largely devoid of aromatics and aliphatic dienes. A panel of nine organisms were tested for the ability to grow on the breakdown products. Growth at a low level was observed for several monocultures on the depolymerized LS in absence of glucose. Much stronger growth was observed in the presence of 0.2% glucose and for one organism we demonstrate doubling of melanin production in the presence of depolymerized LS. The results suggest that this chelator-mediated Fenton method is a promising new approach for biological conversion of lignin into higher value chemicals or intermediates.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Probe for operando in situ electrochemical nuclear magnetic resonance spectroscopy
Electrochemical devices with metal casings have been considered incompatible with nuclear magnetic resonance (NMR) spectroscopy because the oscillating magnetic fields (“rf fields”) responsible for excitation and detection of NMR active nuclei do not penetrate metals. According to the present invention, rf fields can still efficiently penetrate into nonmetallic layers of electrochemical cells (such as a coin cell battery configuration) provided the magnetic field is oriented tangentially to the electrochemical cell electrodes in a “skimming” orientation. As an example, noninvasive high field in situ 7Li and 19F NMR of an unmodified commercial off-the-shelf rechargeable coin cell was demonstrated using a traditional external NMR coil setup. The in operando NMR measurements revealed that irreversible physical changes accumulate at the anode during electrochemical cycling.
Sorte, Eric Glenn↗
Using Computationally-Determined Properties for Machine Learning Prediction of Self-Diffusion Coefficients in Pure Liquids
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. Furthermore, 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.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores
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., R 2 ) of ~0.99 and a mean squared error of ~2.9 × 10 –5 . Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
CB10412: Bulk CWA Destruction
The objective of this project was to eliminate and/or render bulk agent unusable by a threat entity via neutralization and/or polymerization of the bulk agent using minimal quantities of additives. We proposed the in situ neutralization and polymerization of bulk chemical agents (CAs) by performing reactions in the existing CA storage container via wet chemical approaches using minimal quantities of chemical based materials. This approach does not require sophisticated equipment, fuel to power generators, electricity to power equipment, or large quantities of decontaminating materials. By utilizing the CA storage container as the batch reactor, the amount of logistical resources can be significantly reduced. Fewer personnel are required since no sophisticated equipment needs to be set up, configured, or operated. Employing the CA storage container as the batch reactor enables the capability to add materials to multiple containers in a short period of time as opposed to processing one container at a time for typical batch reactor approaches. In scenarios where a quick response is required, the material can be added to all the CA containers and left to react on its own without intervention. Any attempt to filter the CA plus material solution will increase the rate of reaction due to increased agitation of the solution.
36 MATERIALS SCIENCE↗
NMR spectroscopy of coin cell batteries with metal casings
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 “B 1 damming” configurations are avoided. With this understanding, we demonstrate noninvasive high-field in situ 7 Li and 19 F 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 7 Li NMR signals from elemental Li, anodic β-LiAl, and cathodic Li x MnO 2 compounds. Real-time changes of β-LiAl lithium diffusion rates and variable β-LiAl 7 Li NMR Knight shifts are observed and tied to electrochemically driven changes of the β-LiAl defect structure.
25 ENERGY STORAGE↗
Modes of Disorder in Poly(carbon monofluoride)
Poly(carbon monofluoride), or (CF) n , is a layered fluorinated graphite material consisting of nanosized platelets. In this work, 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 19 F 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. 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.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Cs absorption capacity and selectivity of crystalline and amorphous Hf and Zr phosphates
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.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗