A Co-Doping Materials Design Strategy for Selective Ozone Electrocatalysts.

Catalysts for electrochemical ozone production (EOP) face inherent selectivity challenges stemming from thermodynamic constraints. This work establishes a design strategy for minimizing these limitations and inducing EOP activity in tin oxide, which is an intrinsically EOP-inactive material. We propose that selective ozone production using tin oxide catalysts can be broadly achieved by co-doping with two elements: first, n-type dopants to enhance electrical conductivity, and second, transition metal dopants that leach and homogeneously generate essential hydroperoxyl radical intermediates. Synthesizing tantalum, antimony, and tungsten n-type dopants with nickel, cobalt, and iron as transition metal dopants confirms that properly co-doping tin oxide yields EOP-active catalysts. This study offers a robust framework for advancing EOP catalyst design and serves as a case study for the application of fundamental co-catalysis and solid-state physics principles to induce catalytic activity in inert materials.

Interplay between Catalyst Corrosion and Homogeneous Reactive Oxygen Species in Electrochemical Ozone Production.

Electrochemical ozone production (EOP), a six-electron water oxidation reaction, offers promising avenues for creating value-added oxidants and disinfectants. However, progress in this field is slowed by a dearth of understanding of fundamental reaction mechanisms. In this work, we combine experimental electrochemistry, spectroscopic detection of reactive oxygen species (ROS), oxygen-anion chemical ionization mass spectrometry, and computational quantum chemistry calculations to determine a plausible reaction mechanism on nickel- and antimony-doped tin oxide (Ni/Sb-SnO2, NATO), one of the most selective EOP catalysts. Antimony doping is shown to increase the conductivity of the catalyst, leading to improved electrochemical performance. Spectroscopic analysis and electrochemical experiments combined with quantum chemistry predictions reveal that hydrogen peroxide (H2O2) is a critical reaction intermediate. We propose that leached Ni4+ cations catalyze hydrogen peroxide into solution phase hydroperoxyl radicals (•OOH); these radicals are subsequently oxidized to ozone. Isotopic product analysis shows that ozone is generated catalytically from water and corrosively from the catalyst oxide lattice without regeneration of lattice oxygens. Further quantum chemistry calculations and thermodynamic analysis suggest that the electrochemical corrosion of tin oxide itself might generate hydrogen peroxide, which is then catalyzed to ozone. The proposed pathways explain both the roles of dopants in NATO and its lack of stability. Our study interrogates the possibility that instability and electrochemical activity are intrinsically linked through the formation of ROS. In doing so, we provide the first mechanism for EOP that is consistent with computational and experimental results and highlight the central challenge of instability as a target for future research efforts.

A Bond-Energy/Bond-Order and Populations Relationship.

We report an analytical bond energy from bond orders and populations (BEBOP) model that provides intramolecular bond energy decompositions for chemical insight into the thermochemistry of molecules. The implementation reported here employs a minimum basis set Mulliken population analysis on well-conditioned Hartree-Fock orbitals to decompose total electronic energies into physically interpretable contributions. The model's parametrization scheme is based on atom-specific parameters for hybridization and atom pair-specific parameters for short-range repulsion and extended Hückel-type bond energy term fitted to reproduce CBS-QB3 thermochemistry data. The current implementation is suitable for molecules involving H, Li, Be, B, C, N, O, and F atoms, and it can be used to analyze intramolecular bond energies of molecular structures at optimized stationary points found from other computational methods. This first-generation model brings the computational cost of a Hartree-Fock calculation using a large triple-ζ basis set, and its atomization energies are comparable to those from widely used hybrid Kohn-Sham density functional theory (DFT, as benchmarked to 109 species from the G2/97 test set and an additional 83 reference species). This model should be useful for the community by interpreting overall ab initio molecular energies in terms of physically insightful bond energy contributions, e.g., bond dissociation energies, resonance energies, molecular strain energies, and qualitative energetic contributions to the activation barrier in chemical reaction mechanisms. This work reports a critical benchmarking of this method as well as discussions of its strengths and weaknesses compared to hybrid DFT (i.e., B3LYP, M062X, PBE0, and APF methods), and other cost-effective approximate Hamiltonian semiempirical quantum methods (i.e., AM1, PM6, PM7, and DFTB3).

Quantum alchemy beyond singlets: Bonding in diatomic molecules with hydrogen.

Bonding energies play an essential role in describing the relative stability of molecules in chemical space. Therefore, methods employed to search chemical space need to capture the bonding behavior for a wide range of molecules, including radicals. In this work, we investigate the ability of quantum alchemy to capture the bonding behavior of hypothetical chemical compounds, specifically diatomic molecules involving hydrogen with various electronic structures. We evaluate equilibrium bond lengths, ionization energies, and electron affinities of these fundamental systems. We compare and contrast how well manual quantum alchemy calculations, i.e., quantum mechanics calculations in which the nuclear charge is altered, and quantum alchemy approximations using a Taylor series expansion can predict these molecular properties. Our results suggest that while manual quantum alchemy calculations outperform Taylor series approximations, truncations of Taylor series approximations after the second order provide the most accurate Taylor series predictions. Furthermore, these results suggest that trends in quantum alchemy predictions are generally dependent on the predicted property (i.e., equilibrium bond length, ionization energy, or electron affinity). Taken together, this work provides insight into how quantum alchemy predictions using a Taylor series expansion may be applied to future studies of non-singlet systems as well as the challenges that remain open for predicting the bonding behavior of such systems.

Evaluating quantum alchemy of atoms with thermodynamic cycles: Beyond ground electronic states.

Due to the sheer size of chemical and materials space, high-throughput computational screening thereof will require the development of new computational methods that are accurate, efficient, and transferable. These methods need to be applicable to electron configurations beyond ground states. To this end, we have systematically studied the applicability of quantum alchemy predictions using a Taylor series expansion on quantum mechanics (QM) calculations for single atoms with different electronic structures arising from different net charges and electron spin multiplicities. We first compare QM method accuracy to experimental quantities, including first and second ionization energies, electron affinities, and spin multiplet energy gaps, for a baseline understanding of QM reference data. Next, we investigate the intrinsic accuracy of "manual" quantum alchemy. This method uses QM calculations involving nuclear charge perturbations of one atom's basis set to model another. We then discuss the reliability of quantum alchemy based on Taylor series approximations at different orders of truncation. Overall, we find that the errors from finite basis set treatments in quantum alchemy are significantly reduced when thermodynamic cycles are employed, which highlights a route to improve quantum alchemy in explorations of chemical space. This work establishes important technical aspects that impact the accuracy of quantum alchemy predictions using a Taylor series and provides a foundation for further quantum alchemy studies.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from computational chemistry methods. However, achieving this requires a confluence and coaction of expertise in computer science and physical sciences. This Review is written for new and experienced researchers working at the intersection of both fields. We first provide concise tutorials of computational chemistry and machine learning methods, showing how insights involving both can be achieved. We follow with a critical review of noteworthy applications that demonstrate how computational chemistry and machine learning can be used together to provide insightful (and useful) predictions in molecular and materials modeling, retrosyntheses, catalysis, and drug design.

Computational predictions of metal-macrocycle stability constants require accurate treatments of local solvent and pH effects.

Rational design of molecular chelating agents requires a detailed understanding of physicochemical ligand-metal interactions in solvent phase. Computational quantum chemistry methods should be able to provide this, but computational reports have shown poor accuracy when determining absolute binding constants for many chelating molecules. To understand why, we compare and benchmark static- and dynamics-based computational procedures for a range of monovalent and divalent cations binding to a conventional cryptand molecule: 2.2.2-cryptand ([2.2.2]). The benchmarking comparison shows that dynamics simulations using standard OPLS-AA classical potentials can reasonably predict binding constants for monovalent cations, but these procedures fail for divalent cations. We also consider computationally efficient static procedure using Kohn-Sham density functional theory (DFT) and cluster-continuum modeling that accounts for local microsolvation and pH effects. This approach accurately predicts binding energies for monovalent and divalent cations with an average error of 3.2 kcal mol-1 compared to experiment. This static procedure thus should be useful for future molecular screening efforts, and high absolute errors in the literature may be due to inadequate modeling of local solvent and pH effects.

Child lead exposure near abandoned lead acid battery recycling sites in a residential community in Bangladesh: Risk factors and the impact of soil remediation on blood lead levels.

Lead is a potent neurotoxin that is particularly detrimental to children's cognitive development. Batteries account for at least 80% of global lead use and unsafe battery recycling is a major contributor to childhood lead poisoning. Our objectives were to assess the intensity and nature of child lead exposure at abandoned, informal used lead acid battery (ULAB) recycling sites in Kathgora, Savar, Bangladesh, as well as to assess the feasibility and effectiveness of a soil remediation effort to reduce exposure. ULAB recycling operations were abandoned in 2016 due to complaints from residents, but the lead contamination remained in the soil after operations ceased. We measured soil and blood lead levels (BLLs) among 69 children living within 200 m of the ULAB recycling site once before, and twice after (7 and 14 months after), a multi-part remediation intervention involving soil capping, household cleaning, and awareness-raising activities. Due to attrition, the sample size of children decreased from 69 to 47 children at the 7-month post-intervention assessment and further to 25 children at 14 months. We conducted non-parametric tests to assess changes in soil lead levels and BLLs. We conducted baseline surveys, as well as semi-structured interviews and observations with residents throughout the study period to characterize exposure behaviors and the community perceptions. We conducted bivariate and multivariate regression analyses of exposure characteristics to determine the strongest predictors of baseline child BLLs. Prior to remediation, median soil lead concentrations were 1400 mg/kg, with a maximum of 119,000 mg/kg and dropped to a median of 55 mg/kg after remediation (p < 0.0001). Among the 47 children with both baseline and post-intervention time 1 measurements, BLLs dropped from a median of 21.3 µg/dL to 17.0 µg/dL at 7 months (p < 0.0001). Among the 25 children with all three measurements, BLLs dropped from a median of 22.6 µg/dL to 14.8 µg/dL after 14 months (p < 0.0001). At baseline, distance from a child's residence to the nearest abandoned ULAB site was the strongest predictor of BLLs and baseline BLLs were 31% higher for children living within 50 m from the sites compared to those living further away (n = 69, p = 0.028). Women and children spent time in the contaminated site daily and relied on it for their livelihoods and for recreation. Overall, this study highlights the intensity of lead exposure associated with the ULAB recycling industry. Additionally, we document the feasibility and effectiveness of a multi-part remediation intervention at a contaminated site embedded within a residential community; substantially reducing child BLLs and soil lead concentrations.

Quantifying Uncertainties in Solvation Procedures for Modeling Aqueous Phase Reaction Mechanisms.

Computational quantum chemistry provides fundamental chemical and physical insights into solvated reaction mechanisms across many areas of chemistry, especially in homogeneous and heterogeneous renewable energy catalysis. Such reactions may depend on explicit interactions with ions and solvent molecules that are nontrivial to characterize. Rigorously modeling explicit solvent effects with molecular dynamics usually brings steep computational costs while the performance of continuum solvent models such as polarizable continuum model (PCM), charge-asymmetric nonlocally determined local-electric (CANDLE), conductor-like screening model for real solvents (COSMO-RS), and effective screening medium method with the reference interaction site model (ESM-RISM) are less well understood for reaction mechanisms. Here, we revisit a fundamental aqueous hydride transfer reaction-carbon dioxide (CO2) reduction by sodium borohydride (NaBH4)-as a test case to evaluate how different solvent models perform in aqueous phase charge migrations that would be relevant to renewable energy catalysis mechanisms. For this system, quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulations almost exactly reproduced energy profiles from QM simulations, and the Na+ counterion in the QM/MM simulations plays an insignificant role over ensemble averaged trajectories that describe the reaction pathway. However, solvent models used on static calculations gave much more variability in data depending on whether the system was modeled using explicit solvent shells and/or the counterion. We pinpoint this variability due to unphysical descriptions of charge-separated states in the gas phase (i.e., self-interaction errors), and we show that using more accurate hybrid functionals and/or explicit solvent shells lessens these errors. This work closes with recommended procedures for treating solvation in future computational efforts in studying renewable energy catalysis mechanisms.

Elevated Levels of Lead (Pb) Identified in Georgian Spices.

Background: Human lead (Pb) exposure can result in a number of adverse health outcomes, particularly in children. Objective: An assessment of lead exposure sources was carried out in the Republic of Georgia following a nationally representative survey that found elevated blood lead levels (BLLs) in children. Methods: A range of environmental media were assessed in 25 homes and four bazaars spanning five regions. In total, 682 portable X-Ray Fluorescence measurements were taken, including those from cookware (n = 53); paint (n = 207); soil (n = 91); spices (n = 128); toys (n = 78); and other media (n = 125). In addition, 61 dust wipes and 15 water samples were collected and analyzed. Findings: Exceptionally high lead concentrations were identified in multiple spices. Median lead concentrations in six elevated spices ranged from 4-2,418 times acceptable levels. Median lead concentrations of all other media were within internationally accepted guidelines. The issue appeared to be regional in nature, with western Georgia being the most highly affected. Homes located in Adjara and Guria were 14 times more likely to have lead-adulterated spices than homes in other regions. Conclusions: Further study is required to determine the source of lead contamination in spices. Policy changes are recommended to mitigate potential health impacts. The results of this study contribute to a growing body of evidence that points to adulterated spices as a significant source of human lead exposure.

Unveiling the Synergistic Role of Oxygen Functional Groups in the Graphene-Mediated Oxidation of Glutathione.

This is the first report of an atomic-scale direct oxidation mechanism of the thiol group in glutathione (GSH) by epoxides on graphene oxide (GO) at room temperature. The proposed reaction mechanism is determined using a coupled experimental and computational approach; active sites for the reaction are determined through examination of GO surface chemistry changes before and after exposure to GSH, and density functional theory (DFT) calculations determine the reaction barriers for the possible GO-GSH reaction schemes. The findings build on the previously established catalytic mechanism of GSH oxidation by graphenic nanocarbon surfaces and importantly identify the direct reaction mechanism which becomes important in low-oxygen environments. Experimental results suggest epoxides as the active sites for the reaction with GSH, which we confirm using DFT calculations of reaction barriers and further identify a synergism between the adjacent epoxide and hydroxyl groups on the GO surface. The direct oxidation mechanism at specific oxygen sites offers insight into controlling GO chemical reactivity through surface chemistry manipulations. This insight is critical for furthering our understanding of GO oxidative stress pathways in cytotoxicity as well as for providing rational material design for GO applications that can leverage this reaction.

Using nature's blueprint to expand catalysis with Earth-abundant metals.

Numerous redox transformations that are essential to life are catalyzed by metalloenzymes that feature Earth-abundant metals. In contrast, platinum-group metals have been the cornerstone of many industrial catalytic reactions for decades, providing high activity, thermal stability, and tolerance to chemical poisons. We assert that nature's blueprint provides the fundamental principles for vastly expanding the use of abundant metals in catalysis. We highlight the key physical properties of abundant metals that distinguish them from precious metals, and we look to nature to understand how the inherent attributes of abundant metals can be embraced to produce highly efficient catalysts for reactions crucial to the sustainable production and transformation of fuels and chemicals.

Heteroarylureas with fused bicyclic diamine cores as inhibitors of fatty acid amide hydrolase.

A series of mechanism-based heteroaryl urea fatty acid amide hydrolase (FAAH) inhibitors with fused bicyclic diamine cores is described. In contrast to compounds built around a piperazine core, most of the fused bicyclic diamine bearing analogs prepared exhibited greater potency against rFAAH than the human enzyme. Several compounds equipotent against both species were identified and profiled in vivo.

First-principles modeling of chemistry in mixed solvents: Where to go from here?

Mixed solvents (i.e., binary or higher order mixtures of ionic or nonionic liquids) play crucial roles in chemical syntheses, separations, and electrochemical devices because they can be tuned for specific reactions and applications. Apart from fully explicit solvation treatments that can be difficult to parameterize or computationally expensive, there is currently no well-established first-principles regimen for reliably modeling atomic-scale chemistry in mixed solvent environments. We offer our perspective on how this process could be achieved in the near future as mixed solvent systems become more explored using theoretical and computational chemistry. We first outline what makes mixed solvent systems far more complex compared to single-component solvents. An overview of current and promising techniques for modeling mixed solvent environments is provided. We focus on so-called hybrid solvation treatments such as the conductor-like screening model for real solvents and the reference interaction site model, which are far less computationally demanding than explicit simulations. We also propose that cluster-continuum approaches rooted in physically rigorous quasi-chemical theory provide a robust, yet practical, route for studying chemical processes in mixed solvents.

Machine Learning-Guided Approach for Studying Solvation Environments.

Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, we report a static and all-quantum mechanics-based cluster-continuum approach for calculating single-ion solvation free energies. This approach uses a global optimization procedure to identify low-energy molecular clusters with different numbers of explicit solvent molecules and then employs the smooth overlap for atomic positions learning kernel to quantify the similarity between different low-energy solute environments. From these data, we use sketch maps, a nonlinear dimensionality reduction algorithm, to obtain a two-dimensional visual representation of the similarity between solute environments in differently sized microsolvated clusters. After testing this approach on different ions having charges 2+, 1+, 1-, and 2-, we find that the solvation environment around each ion can be seen to usually become more similar in hand with its calculated single-ion solvation free energy. Without needing either dynamics simulations or an a priori knowledge of local solvation structure of the ions, this approach can be used to calculate solvation free energies within 5% of experimental measurements for most cases, and it should be transferable for the study of other systems where dynamics simulations are not easily carried out.

A paramedic treatment for modeling explicitly solvated chemical reaction mechanisms.

We report a static quantum chemistry modeling treatment to study how solvent molecules affect chemical reaction mechanisms without dynamics simulations. This modeling scheme uses a global optimization procedure to identify low energy intermediate states with different numbers of explicit solvent molecules and then the growing string method to locate sequential transition states along a reaction pathway. Testing this approach on the acid-catalyzed Morita-Baylis-Hillman (MBH) reaction in methanol, we found a reaction mechanism that is consistent with both recent experiments and computationally intensive dynamics simulations with explicit solvation. In doing so, we explain unphysical pitfalls that obfuscate computational modeling that uses microsolvated reaction intermediates. This new paramedic approach can promisingly capture essential physical chemistry of the complicated and multistep MBH reaction mechanism, and the energy profiles found with this model appear reasonably insensitive to the level of theory used for energy calculations. Thus, it should be a useful and computationally cost-effective approach for modeling solvent mediated reaction mechanisms when dynamics simulations are not possible.

Thermodynamic Hydricities of Biomimetic Organic Hydride Donors.

Thermodynamic hydricities (Δ GH-) in acetonitrile and dimethyl sulfoxide have been calculated and experimentally measured for several metal-free hydride donors: NADH analogs (BNAH, CN-BNAH, Me-MNAH, HEH), methylene tetrahydromethanopterin analogs (BIMH, CAFH), acridine derivatives (Ph-AcrH, Me2N-AcrH, T-AcrH, 4OH, 2OH, 3NH), and a triarylmethane derivative (6OH). The calculated hydricity values, obtained using density functional theory, showed a reasonably good match (within 3 kcal/mol) with the experimental values, obtained using "potential p Ka" and "hydride-transfer" methods. The hydride donor abilities of model compounds were in the 48.7-85.8 kcal/mol (acetonitrile) and 46.9-84.1 kcal/mol (DMSO) range, making them comparable to previously studied first-row transition metal hydride complexes. To evaluate the relevance of entropic contribution to the overall hydricity, Gibbs free energy differences (Δ GH-) obtained in this work were compared with the enthalpy (Δ HH-) values obtained by others. The results indicate that, even though Δ HH- values exhibit the same trends as Δ GH-, the differences between room-temperature Δ GH- and Δ HH- values range from 3 to 9 kcal/mol. This study also reports a new metal-free hydride donor, namely, an acridine-based compound 3NH, whose hydricity exceeds that of NaBH4. Collectively, this work gives a perspective of use metal-free hydride catalysts in fuel-forming and other reduction processes.

Improving human health outcomes with a low-cost intervention to reduce exposures from lead acid battery recycling: Dong Mai, Vietnam.

This study details the first comprehensive evaluation of the efficacy of a soil lead mitigation project in Dong Mai village, Vietnam. The village's population had been subject to severe lead poisoning for at least a decade as a result of informal Used Lead Acid Battery (ULAB) recycling. Between July 2013 to February 2015, Pure Earth and the Centre for Environment and Community Development (Hanoi, Vietnam) implemented a multi-faceted environmental and human health intervention. The intervention consisted of a series of institutional and low-cost engineering controls including the capping of lead contaminated surface soils, cleaning of home interiors, an education campaign and the construction of a work-clothes changing and bathing facility. The mitigation project resulted in substantial declines in human and environmental lead levels. Remediated home yard and garden areas decreased from an average surface soil concentration of 3940mg/kg to <100mg/kg. One year after the intervention, blood lead levels in children (<6 years old) were reduced by an average of 67%-from a median of 40.4µg/dL to 13.3µg/dL. The Dong Mai project resulted in significantly decreased environmental and biological lead levels demonstrating that low-cost, rapid and well-coordinated interventions could be readily applied elsewhere to significantly reduce preventable human health harm.