ABSTRACT
The non-benzenoid aromatic system azulene is sufficiently nucleophilic at C1 that it can react with a protonated aldehyde to form an α-azulenyl alcohol. This in turn may be protonated and undergo loss of water to give an azulene α-carbocation. We report the isolation of such azulenyl cations as salts with non-coordinating anions. The salts have been characterised by NMR, UV/Vis absorption and (in certain cases) X-ray crystallography. Reduction of representative salts to afford azulenyl(aryl) methylenes has been demonstrated.
ABSTRACT
A novel approach based on supervised machine-learning is proposed to predict the solubility of drugs and drug-like molecules in mixtures of organic solvents. Similar to quantitative structure-property relationship (QSPR) models, different solvent types are identified by molecular descriptors, which, in this study, are considered as UNIFAC subgroups. To overcome the potential lack of UNIFAC subgroups for the complex Active Pharmaceutical Ingredients (APIs) currently developed in the pharmaceutical industry, the API molecule is considered as a unique entity in the proposed modelling approach. Therefore, API solubility is predicted as a function of temperature, functional subgroups of the solvents and composition of the solvent mixture; in turn, regressors' correlation is handled through Partial Least-Squares (PLS) regression. The method is developed and tested with experimental data of a real API and 14 organic solvents that are industrially employed for crystallisation. Solubility predictions are accurate and precise for single solvents, binary mixtures and ternary mixtures of organic solvents at different compositions and temperatures, with a determination coefficient R2 ≥ 0.90. To further test the applicability of the model, the proposed approach is applied to 9 literature organic solubility datasets of drugs and drug-like compounds and compared to benchmark solubility models in the literature. Results show that the proposed approach provides satisfactory predictions: the majority of validation and calibration data have R2 = 0.95-0.99; the ratio between RMSE (root mean squared error) of the proposed method and the range of measured solubility values is from 1 to 3 orders of magnitude smaller than the RMSE ratio obtained by the benchmark models.
ABSTRACT
An "indirect" photo-electrochemical sensor is presented for the measurement of a mixture of analytes including reducing sugars (e.g. glucose, fructose) and non-reducing sugars (e.g. sucrose, trehalose). Its innovation relies on the use of a palladium film creating a two-compartment cell to separate the electrochemical and the photocatalytic processes. In this original way, the electrochemical detection is separated from the potential complex matrix of the analyte (i.e. colloids, salts, additives, etc.). Hydrogen is generated in the photocatalytic compartment by a Pt@g-C3N4 photocatalyst embedded into a hydrogen capture material composed of a polymer of intrinsic microporosity (PIM-1). The immobilised photocatalyst is deposited onto a thin palladium membrane, which allows rapid pure hydrogen diffusion, which is then monitored by chronopotentiometry (zero current) response in the electrochemical compartment. The concept is demonstrated herein for the analysis of sugar content in commercial soft drinks. There is no requirement for the analyte to be conducting with electrolyte or buffered. In this way, samples (biological or not) can be simply monitored by their exposition to blue LED light, opening the door to additional energy conversion and waste-to-energy applications.