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.

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.