Structural investigation of the complexation between vitamin B12 and per- and polyfluoroalkyl substances: Insights into degradation using density functional theory.

Environmental remediation of per- and polyfluoroalkyl substances (PFAS) has become a significant research topic in recent years due to the fact that these materials are omnipresent, resistant to degradation and thus environmentally persistent. Unfortunately, they have also been shown to cause health concerns. PFAS are widely used in industrial applications and consumer products. Vitamin B12 (B12) has been identified as being catalytically active towards a variety of halogenated compounds such as PFAS. It has also been shown to be effective when using sulfide as a reducing agent for B12. This is promising as sulfide is readily available in the environment. However, there are many unknowns with respect to PFAS interactions with B12. These include the reaction mechanism and B12's specificity for PFAS with certain functionalization(s). In order to understand the specificity of B12 towards branched PFAS, we examined the atomistic interactions between B12 and eight different PFAS molecules using Density Functional Theory (B3LYP/cc-pVDZ). The PFAS test set included linear PFAS and their branched analogs, carboxylic acid and sulfonic acid headgroups, and aromatic and non-aromatic cyclic structures. Conformational analyses were carried out to determine the lowest energy configurations. This analysis showed that small chain PFAS such as perfluorobutanoic acid interact with the cobalt center of B12. Bulkier PFAS prefer to interact with the amine and carbonyl groups on the sidechains of the B12 ring system. Furthermore, computed complexation energies determined that, in general, branched PFAS (e.g. perfluoro-5-methylheptane sulfonic acid) interact more strongly than linear molecules (e.g. perfluorooctanesulfonic acid). Our results indicate that it may be possible to alter the interactions between B12 and PFAS by synthetically modifying the sidechains of the ring structure.

Periodic DFT calculations to compute the attributes of a quantum material: honeycomb ruthenium trichloride.

Quantum spin liquids (QSLs) have become prominent materials of interest in the pursuit of fault-tolerant materials for quantum computing applications. This is due to the fact that these materials are theorized to host an interesting variety of quantum phenomena such as quasi-particles that may behave as anyons as a result of the high entangled nature of the spin states within the systems. Computing the electronic and magnetic properties of these materials is necessary in order to understand the underlying interactions of the materials. In this paper, the structural, electronic, and magnetic properties including lattice parameters, bandgap, Heisenberg coupling constants, and Curie temperatures for α-RuCl3, a promising candidate for the Kitaev QSL model, are computed using periodic density functional theory. Furthermore, various parameters of the calculations (i.e. functional choice, basis set, k-point density, and Hubbard correction) are varied in order to determine what effect, if any, the computational setup has on the computed properties. The results of this study indicate that PBE functional with Hubbard corrections of 1.5-2.5 eV with a k-point density of 3.0 points per Å-1 appear to be the best parameters to compute Heisenberg coupling constants for α-RuCl3. These parameters with the addition of spin orbit coupling works well for computing Curie temperatures for α-RuCl3. Distinct differences are noted in the computations of the bulk structure vs. monolayer structures, indicating that interactions between the layers play a role in the material properties and changes to the inter-layer spacing may result in interesting and unique magnetic properties that require further investigation.

Graphene as a rational interface for enhanced adsorption of microcystin-LR from water.

Cyanotoxins such as microcystin-LR (MC-LR) represent a global environmental threat to ecosystems and drinking water supplies. The study investigated the direct use of graphene as a rational interface for removal of MC-LR via interactions with the aromatic ring of the ADDA1 chain of MC-LR and the sp2 hybridized carbon network of graphene. Intra-particle diffusion model fit indicated the high mesoporosity of graphene provided significant enhancements to both adsorption capacities and kinetics when benchmarked against microporous granular activated carbon (GAC). Graphene showed superior MC-LR adsorption capacity of 75.4 mg/g (Freundlich model) compared to 0.982 mg/g (Langmuir model) for GAC. Sorption kinetic studies showed graphene adsorbs 99% of MC-LR in 30 min, compared to zero removal for GAC after 24 hr using the same MC-LR concentration. Density functional theory (DFT), calculations showed that postulated π-based interactions align well with the NMR-based experimental work used to probe primary interactions between graphene and MC-LR adduct. This study proved that π-interactions between the aromatic ring on MC-LR and graphene sp2 orbitals are a dominant interaction. With rapid kinetics and adsorption capacities much higher than GAC, it is anticipated that graphene will offer a novel molecular approach for removal of toxins and emerging contaminants with aromatic systems.

Preferential Adsorption of Prominent Amino Acids in the Urease Enzyme of Sporosarcina pasteurii on Arid Soil Components: A Periodic DFT Study.

The urease enzyme is commonly used in microbially induced carbonate precipitation (MICP) and enzyme-induced carbonate precipitation (EICP) to heal and strengthen soil. Improving our understanding of the adsorption of the urease enzyme with various soil surfaces can lead to advancements in the MICP and EICP engineering methods as well as other areas of soil science. In this work, we use density functional theory (DFT) to investigate the urease enzyme's binding ability with four common arid soil components: quartz, corundum, albite, and hematite. As the urease enzyme cannot directly be simulated with DFT due to its size, the amino acids comprising at least 5% of the urease enzyme were simulated instead. An adsorption model incorporating the Gibbs free energy was used to determine the existence of amino acid-mineral binding modes. It was found that the nine simulated amino acids bind preferentially to the different soil components. Alanine favors corundum, glycine and threonine favor hematite, and aspartic acid favors albite. It was found that, under the standard environmental conditions considered here, amino acid binding to quartz is unfavorable. In the polymeric form where the side chains would dominate the binding interactions, hematite favors aspartic acid through its R-OH group and corundum favors glutamic acid through its R-Ket group. Overall, our model predicts that the urease enzyme produced by Sporosarcina pasteurii can bind to various oxides found in arid soil through its alanine, glycine, aspartic/glutamic acid, or threonine residues.

Atomistic insights into the hydrodefluorination of PFAS using silylium catalysts.

Fluorochemicals are a persistent environmental contaminant that require specialized techniques for degradation and capture. In particular, recent attention on per- and poly-fluoroalkyl substances (PFAS) has led to numerous explorations of different techniques for degrading the super-strong C-F bonds found in these fluorochemicals. In this study, we investigated the hydrodefluorination mechanism using silylium-carborane salts for the degradation of PFAS at the density functional theory (DFT) level. We find that the degradation process involves both a cationic silylium (Et3Si+) and a hydridic silylium (Et3SiH) to facilitate the defluorination and hydride-addition events. Additionally, the role of carborane ([HCB11H5F6]-) is to force unoccupied anti-bonding orbitals to be partially occupied, weakening the C-F bond. We also show that changing the substituents on carborane from fluorine to other halogens weakens the C-F bond even further, with iodic carborane ([HCB11H5I6]-) having the greatest weakening effect. Moreover, our calculations reveal why the C-F bonds are resistant to degradation, and how the silylium-carborane chemistry is able to chemically transform these bonds into C-H bonds. We believe that our results are further applicable to other halocarbons, and can be used to treat either our existing stocks of these chemicals or to treat concentrated solutions following filtration and capture.

Effect of Concrete Composition on the Thermodynamic Binding of Dopamine: A DFT Study.

Concrete has long been a standard in construction projects. However, increasing the binding of cement paste to the concrete aggregate (a collection of geological materials containing, e.g., gravel, sand, etc.) remains an open area of research, as this is a common failure point in concrete-based infrastructure. One solution is the application of an adhesive into the mix that not only is capable of binding under aqueous conditions but can aid in the binding of the aggregate to the cement paste. Bioinspired catecholic-type molecules have been shown to be an ultrastrong adhesive, even under wet conditions, and would, in principle, be an ideal candidate to use. In this study, we examine how dopamine (a molecule with a catechol functionality) binds to various oxides found in concrete mixtures. We find that dopamine binds preferentially to alkaline earth oxides; thus, for concrete mixtures rich in these minerals dopamine would be an ideal candidate for improved adhesion.

Green MIP-202(Zr) Catalyst: Degradation and Thermally Robust Biomimetic Sensing of Nerve Agents.

Rapid and robust sensing of nerve agent (NA) threats is necessary for real-time field detection to facilitate timely countermeasures. Unlike conventional phosphotriesterases employed for biocatalytic NA detection, this work describes the use of a new, green, thermally stable, and biocompatible zirconium metal-organic framework (Zr-MOF) catalyst, MIP-202(Zr). The biomimetic Zr-MOF-based catalytic NA recognition layer was coupled with a solid-contact fluoride ion-selective electrode (F-ISE) transducer, for potentiometric detection of diisopropylfluorophosphate (DFP), a F-containing G-type NA simulant. Catalytic DFP degradation by MIP-202(Zr) was evaluated and compared to the established UiO-66-NH2 catalyst. The efficient catalytic DFP degradation with MIP-202(Zr) at near-neutral pH was validated by 31P NMR and FT-IR spectroscopy and potentiometric F-ISE and pH-ISE measurements. Activation of MIP-202(Zr) using Soxhlet extraction improved the DFP conversion rate and afforded a 2.64-fold improvement in total percent conversion over UiO-66-NH2. The exceptional thermal and storage stability of the MIP-202/F-ISE sensor paves the way toward remote/wearable field detection of G-type NAs in real-world environments. Overall, the green, sustainable, highly scalable, and biocompatible nature of MIP-202(Zr) suggests the unexploited scope of such MOF catalysts for on-body sensing applications toward rapid on-site detection and detoxification of NA threats.

Adventures in DFTB: Toward an Automatic Parameterization Scheme.

As we push forward on understanding the fate of chemicals in the environment, we need a method that will allow for the simulation of the inherent heterogeneity. Density functional tight binding (DFTB) is a methodology that allows for a detailed electronic description and would be ideal for this problem. While many parameters can be derived directly from DFT, empirical parameters still exist in the confinement and repulsion potentials. In this manuscript, we examine these potentials and present solutions that will minimize the degree of empiricism. Our results show that it is possible to construct confinement potentials from examining the atomic radial wavefunctions. Moreover, we found that the heterogeneous repulsion potentials can be derived from using only homogeneous repulsion curves.

DFT investigation on the adsorption of munition compounds on α-Fe2O3: similarity and differences with α-Al2O3.

Arid environments have long been a testing and training ground for novel munitions. However, these activities leave behind unknown quantities of munition residues with unknown impact on local flora and fauna. In particular, arid soil contains Lewis acidic metal oxides which bind and catalyze the electron rich substituent groups commonly found in munition compounds, although the exact mechanisms are poorly understood. The current study remedies this lack of knowledge by utilizing density functional theory (DFT) to explore various orientations of four important munition compounds on the α-Fe2O3(0001) and α-Al2O3(0001) surfaces. Our findings reveal that while α-Fe2O3 binds the munition compounds more strongly than α-Al2O3, all four compounds experienced elongation of their nitro (-NO2) groups, indicating their susceptibility towards degradation on these surfaces.

Nickel supported on nitrogen-doped carbon nanotubes as hydrogen oxidation reaction catalyst in alkaline electrolyte.

The development of a low-cost, high-performance platinum-group-metal-free hydroxide exchange membrane fuel cell is hindered by the lack of a hydrogen oxidation reaction catalyst at the anode. Here we report that a composite catalyst, nickel nanoparticles supported on nitrogen-doped carbon nanotubes, has hydrogen oxidation activity similar to platinum-group metals in alkaline electrolyte. Although nitrogen-doped carbon nanotubes are a very poor hydrogen oxidation catalyst, as a support, it increases the catalytic performance of nickel nanoparticles by a factor of 33 (mass activity) or 21 (exchange current density) relative to unsupported nickel nanoparticles. Density functional theory calculations indicate that the nitrogen-doped support stabilizes the nanoparticle against reconstruction, while nitrogen located at the edge of the nanoparticle tunes local adsorption sites by affecting the d-orbitals of nickel. Owing to its high activity and low cost, our catalyst shows significant potential for use in low-cost, high-performance fuel cells.

Evaluation of theoretical approaches for describing the interaction of water with linear acenes.

The interaction of a water monomer with a series of linear acenes (benzene, anthracene, pentacene, heptacene, and nonacene) is investigated using a wide range of electronic structure methods, including several "dispersion"-corrected density functional theory (DFT) methods, several variants of the random phase approximation (RPA), DFT-based symmetry-adapted perturbation theory with density fitting (DF-DFT-SAPT), MP2, and coupled-cluster methods. The DF-DFT-SAPT calculations are used to monitor the evolution of the electrostatics, exchange-repulsion, induction, and dispersion contributions to the interaction energies with increasing acene size and also provide the benchmark data against which the other methods are assessed.

Benchmark calculations of water-acene interaction energies: Extrapolation to the water-graphene limit and assessment of dispersion-corrected DFT methods.

In a previous study (J. Phys. Chem. C, 2009, 113, 10242-10248) we used density functional theory based symmetry-adapted perturbation theory (DFT-SAPT) calculations of water interacting with benzene (C(6)H(6)), coronene (C(24)H(12)), and circumcoronene (C(54)H(18)) to estimate the interaction energy between a water molecule and a graphene sheet. The present study extends this earlier work by use of a more realistic geometry with the water molecule oriented perpendicular to the acene with both hydrogen atoms pointing down. We also include results for an intermediate C(48)H(18) acene. Extrapolation of the water-acene results gives a value of -3.0 +/- 0.15 kcal mol(-1) for the binding of a water molecule to graphene. Several popular dispersion-corrected DFT methods are applied to the water-acene systems and the resulting interacting energies are compared to results of the DFT-SAPT calculations in order to assess their performance.

A second generation distributed point polarizable water model.

A distributed point polarizable model (DPP2) for water, with explicit terms for charge penetration, induction, and charge transfer, is introduced. The DPP2 model accurately describes the interaction energies in small and large water clusters and also gives an average internal energy per molecule and radial distribution functions of liquid water in good agreement with experiment. A key to the success of the model is its accurate description of the individual terms in the n-body expansion of the interaction energies.