Resolving X-Ray Photoelectron Spectra of Ionic Liquids with Difference Spectroscopy

X-ray photoelectron spectroscopy (XPS) is a powerful element-specific technique to determine the composition and chemical state of all elements in an involatile sample. However, for elements such as carbon, the wide variety of chemical states produce complex spectra that are difficult to interpret, consequently concealing important information due to the uncertainty in signal identity. Here we report a process whereby chemical modification of carbon structures with electron withdrawing groups can reveal this information, providing accurate, highly refined fitting models far more complex than previously possible. This method is demonstrated with functionalised ionic liquids bearing chlorine or trifluoromethane groups that shift electron density from targeted locations. By comparing the C 1s spectra of non-functional ionic liquids to their functional analogues, a series of difference spectra can be produced to identify exact binding energies of carbon photoemissions, which can be used to improve the C 1s peak fitting of both samples. Importantly, ionic liquids possess ideal chemical and physical properties, which enhance this methodology to enable significant progress in XPS peak fitting and data interpretation

Controlling the outcome of SN2 reactions in ionic liquids: from rational data set design to predictive linear regression models

Rate constants for a bimolecular nucleophilic substitution (SN2) process in a range of ionic liquids are correlated with calculated parameters associated with the charge localisation on the cation of the ionic liquid (including the molecular electrostatic potential). Simple linear regression models proved effective, though the interdependency of the descriptors needs to be taken into account when considering generality. A series of ionic liquids were then prepared and evaluated as solvents for the same process; this data set was rationally chosen to incorporate homologous series (to evaluate systematic variation) and functionalities not available in the original data set. These new data were used to evaluate and refine the original models, which were expanded to include simple artificial neural networks. Along with showing the importance of an appropriate data set and the perils of overfitting, the work demonstrates that such models can be used to reliably predict ionic liquid solvent effects on an organic process, within the limits of the data set

B(C6F5)3‑Catalyzed Dehydrogenation of Pyrrolidines to Form Pyrroles

Pyrroles are important N-heterocycles found in medicines and materials. The formation of pyrroles from widely accessible pyrrolidines is a potentially attractive strategy but is an underdeveloped approach due to the sensitivity of pyrroles to the oxidative conditions required to achieve such a transformation. Herein, we report a catalytic approach that employs commercially available B(C6F5)3 in an operationally simple procedure that allows pyrrolidines to serve as direct synthons for pyrroles. Mechanistic studies have revealed insights into borane-catalyzed dehydrogenative processes

B(C6F5)3‑Catalyzed Dehydrogenation of Pyrrolidines to Form Pyrroles

Pyrroles are important N-heterocycles found in medicines and materials. The formation of pyrroles from widely accessible pyrrolidines is a potentially attractive strategy but is an underdeveloped approach due to the sensitivity of pyrroles to the oxidative conditions required to achieve such a transformation. Herein, we report a catalytic approach that employs commercially available B(C6F5)3 in an operationally simple procedure that allows pyrrolidines to serve as direct synthons for pyrroles. Mechanistic studies have revealed insights into borane-catalyzed dehydrogenative processes