Counterintuitive solvation effect of ionic-liquid/DMSO solvents on acidic C-H dissociation and insight into respective solvation

Hydrogen-Bonded Complexes in Binary Mixture of Imidazolium-Based Ionic Liquids with Organic Solvents

Though local structures in ionic liquids are dominated by strong Coulomb forces, directional hydrogen bonds can also influence the physicochemical properties of imidazolium-based ionic liquids. In particular, the C-2 position of the imidazolium cation is acidic and can bind with suitable hydrogen bond acceptor sites of molecular solvents dissolved in imidazolium-based ionic liquids. In this report, we identify hydrogen-bonded microenvironments of the model ionic liquid, 1-ethyl-3-methylimidazolium tris(pentafluoroethyl) trifluorophosphate, and the changes that occur when molecular solvents are dissolved in it by using a C-D infrared reporter at the C-2 position of the cation. Our linear and nonlinear infrared experiments, along with computational studies, indicate that the molecular solvent dimethyl sulfoxide can form strong hydrogen-bonded dimers with the cation of the ionic liquid at the C-2 position. In contrast, acetone, which is also a hydrogen bond acceptor similar to dimethyl sulfoxide, does not show evidence of cation-solvent hydrogen-bonded conformers at the C-2 position. The outcome of our study on a broad scale strengthens the importance of cation-solute interactions in ionic liquids.

Acidity Scale in a Choline Chloride- and Ethylene Glycol-Based Deep Eutectic Solvent and Its Implication on Carbon Dioxide Absorption

An equilibrium acidity (pK_a) scale that comprises 16 Brönsted organic acids, including phenols, carboxylic acids, azoles, and phenylmalononitriles, was established in a choline chloride/EG-based deep eutectic solvent (DES) ([Ch][Cl]:2EG) by ultraviolet-visible (UV-Vis) spectroscopic methods. The established acidity scale spans about 6 pK units in the DES, which is similar to that for these acids in water. The acidity comparisons and linear correlations between the DES and other solvents show that the solvent property of [Ch][Cl]:2EG is quite different from those of amphiphilic protic and dipolar aprotic molecular solvents. The carbon dioxide absorption capabilities as well as apparent absorption kinetics for a series of anion-functionalized DESs ([Ch][X]:2EG) were measured, and the results show that the basicity of comprising anion [X] of choline salt is essential for the maximum carbon dioxide absorption capacity, i.e., a stronger basicity leads to a greater absorption capacity. The possible absorption mechanisms for carbon dioxide absorption in these DESs were also discussed based on the spectroscopic evidence.

Dosing metric in cellular experiments: The mol/cell metric has its limitations

It has been argued that the mol/cell metric is more universal than concentration of the toxic agent since in many cases the effect of dose expressed as mol/cell is independent of ex-perimental setup. We confirmed it for hemolysis of erythrocytes in phosphate-buffered saline induced by hypochlorite where the amount of femtomoles/cell of hypochlorite needed for 50% hemolysis was independent of erythrocyte concentration. However, in the presence of blood plasma this metric became dependent on cell concentration. Similarly, the effect of 3-bromopyruvic acid (3-BP) on PEO1 cells as a function of mol/cell ratio depended on the volume of the 3-BP containing medium, due to the reaction of 3-BP with components of the medium. Hemolytic amounts of sodium dodecyl sulfate and Triton X-100 expressed as mol/cell decreased with increasing cell concentration while the effect of DMSO on the viability of a constant number of fibroblasts was independent of the volume of DMSO-containing medium. These results demonstrate that the mol/cell metric is still dependent on experimental conditions when the toxic agent interacts with components of the medium or when its physical state is modified by the target cells, and the effect is independent of the mol/per cell ratio for high excess of a cell damaging agent.

Acid Catalysis with Alkane/Water Microdroplets in Ionic Liquids

Ionic liquids are composed of an organic cation and a highly delocalized perfluorinated anion, which remain tight to each other and neutral across the extended liquid framework. Here we show that n-alkanes in millimolar amounts enable a sufficient ion charge separation to release the innate acidity of the ionic liquid and catalyze the industrially relevant alkylation of phenol, after generating homogeneous, self-stabilized, and surfactant-free microdroplets (1-5 μm). This extremely mild and simple protocol circumvents any external additive or potential ionic liquid degradation and can be extended to water, which spontaneously generates microdroplets (ca. 3 μm) and catalyzes Brönsted rather than Lewis acid reactions. These results open new avenues not only in the use of ionic liquids as acid catalysts/solvents but also in the preparation of surfactant-free, well-defined ionic liquid microemulsions.

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules

A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (−1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model's predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.

Prediction of bond dissociation energies for charged molecules with a graph neural network enabled by global molecular features and reaction difference features between products and reactants.