Switchable-hydrophilicity solvents (SHS) can switch reversibly between one form that is miscible with water and another that forms a biphasic mixture with water. New examples are reported and compared in terms of safety and environmental impact.

A switchable-hydrophilicity solvent (SHS) is a solvent that in one state forms a biphasic mixture with water but can be reversibly switched to another state that is miscible with water. All of the amine SHSs that we have reported previously lie within a particular basicity and hydrophilicity range (9.5 < pKaH < 11 and 1.0 < log Kow < 2.5, respectively). We report an extension of this range by altering the pressure of CO2 as well as the water : SHS volume ratio used in the process. Increasing the pressure of CO2 and/or the water : amine volume ratio allows some amines with pKaH < 9.5 or log Kow > 2.5 to function as SHSs.

A switchable-hydrophilicity solvent (SHS) is a solvent that in one state forms a biphasic mixture with water but can be reversibly switched to another state that is miscible with water. We describe a mathematical model of the behaviour of CO2-triggered SHS that narrows the search field for these solvents in terms of their basicity and hydrophilicity. By its predictive power, the mathematical model can assist in the optimization of processes using SHSs in terms of extrinsic parameters such as pressure and the relative volumes of solvent and water used. Models are presented for both a two-liquid system (consisting of the amine solvent and water) and a three-liquid system (consisting of the amine solvent, water, and 1-octanol). Partitioning data with toluene as the third component is also shown for comparison with 1-octanol.

The g tensor components of the 4,5-dihydro-1,3,2-dithiazolyl (H2DTA•) radical, which is a basic building block for molecular magnets and spintronic devices, is calculated by the coupled-perturbed Kohn-Sham (CPKS) hybrid density functional (HDF) and multireference configuration interaction-sum over states (MRCI-SOS) techniques. In both methods, the diagonalized g tensor principal axes are found to be aligned with the radical's inertial axes. The tensor components are in very good agreement with those determined experimentally by electron paramagnetic resonance (EPR) spectroscopy. The MRCI technique produced g tensor components that are more accurate than those obtained by the CPKS-HDF method. Nonetheless, to get reasonable MRCI results, one must include the in-plane and out-of-plane interactions in an unbiased way. The minimum reference space that satisfies these conditions is generated from a complete active space of nine electrons in six orbitals [CAS(9,6)] and contains a(1), a(2), b(1) and b(2) type orbitals. In addition, the number of roots in the MRCI-SOS g tensor expansion should include all excited states that range from 0 to 56,000 cm(-1). The most accurate results are obtained using an MRCI-SOS/CAS(13,9) calculation. These g tensor components are within the experimental accuracy range of 1000 ppm. The one- and two-electron contributions to the g tensor components are separated and individually analyzed. The very good agreement with experiment opens the door for further accurate calculations of spin Hamiltonian tensors of larger DTA• radicals.

Light sensor probes are useful in experiments that investigate seasonal variations and the nature of light. However, having a dedicated light probe is not always possible or even convenient for many instructors. Modern smartphone technology gives instructors the ability to use built-in light sensors as an inexpensive alternative. This introductory experiment will have students use a smartphone loaded with a light detection app to quantitatively determine how changing latitude on Earth changes flux received. The purpose is to have students discover how the different seasons arise from the Earth-Sun system. While performing the experiment and analyzing the data, students will also discover the following important and relevant physical relationships: distance from light source and light brightness (flux), latitude and flux, and Earth’s orientation and location (latitude) of maximum flux. By piecing all of these relationships together, students are able to explain the origins of the different seasons based on the data they collected.