Lithium doped N,N-dimethyl pyrrolidinium tetrafluoroborate organic ionic plastic crystal electrolytes for solid state lithium batteries

The organic ionic plastic crystal material N,N-dimethyl pyrrolidinium tetrafluoroborate ([C1mpyr][BF4]) has been mixed with LiBF4 from 0 to 8 wt% and shown to exhibit enhanced ionic conductivity, especially in the higher temperature plastic crystal phases (phases II and I). The materials retain their solid state well above 100 °C with the melt not being observed up to 300 °C. Interestingly the conductivity enhancement is highest with the lowest level of LiBF4 addition in phase II, but then the order of enhancement is reversed in phase I. In all cases, a conductivity drop is observed at the II → I phase transition (105 °C) which is associated with increased order in the pure matrix, as previously reported, although the conductivity drop is least for the highest LiBF4 amount (8 wt%). The 8 wt% sample displays different conductivity behaviours compared to the lower LiBF4 concentrations, with a sharp increase above 50 °C, which is apparently not related to the formation of an amorphous phase, based on XRD data up to 120 °C. Symmetric cells, Li/OIPC/Li, were prepared and cycled at 50 °C and showed evidence of significant preconditioning with continued cycling, leading to a lower over-potential and a concomitant decrease in the cell resistivity as measured by EIS. An SEM investigation of the Li/OIPC interfaces before and after cycling suggested significant grain refinement was responsible for the decrease in cell resistance upon cycling, possibly as a result of an increased grain boundary phase.

Hydrogen bonded block copolymer systems : from microphase separation in solid state to self-assembly in aqueous solutions

Block copolymer systems with hydrogen bonding interactions have received relatively little attention. Recently, we have investigated the self-assembly and phase separation in such block copolymer systems with an attempt to elucidate the role of hydrogen bonding interactions both theoretically and experimentally [1-4]. In A-b-B/C diblock copolymer/homopolymer systems, the phase behavior was theoretically analyzed according to the random phase approximation and correlated with hydrogen bonding interactions in terms of the difference in inter-association constants (K). To examine how the hydrogen bonding determines the self-assembly and morphological transitions in these systems, we have introduced the K values as a new variable into the phase diagram which we established for the first time (Fig. 1). Multiple vesicular morphologies were formed in aqueous solution of A-b-B/A-b-C diblock copolymer complexes of PS-b-PAA and PS-b-PEO. Interconnected compound vesicles (ICCVs) were observed for the first time as a new morphology (Fig. 2), along with other aggregated nanostructures including vesicles, multilamellar vesicles, thick-walled vesicles and irregular aggregates. Complexation of two amphiphilic diblock copolymers provides a viable approach to vesicles in aqueous media.

Organic ionic plastic crystal electrolytes; a new class of electrolyte for high efficiency solid state dye-sensitized solar cells

Dye-sensitized solar cells are an increasingly promising alternative to conventional silicon solar cells as a method of converting solar energy to electricity and thus providing an effectively inexhaustible energy source. However, the most efficient of these devices currently utilize liquid electrolytes, which suffer from the associated problems of leakage and evaporation. Hence, significant research is currently focused on the development of solid state alternatives. Here we report a new class of solid state electrolyte for these devices, organic ionic plastic crystal electrolytes, that allow relatively rapid diffusion of the redox couple through the matrix, which is critical to the cell performance. A range of different organic ionic plastic crystal materials, utilizing different cation and anion structures, have been investigated and the conductivities, diffusion rates and photovoltaic performance of the electrolytes are reported. The best material, utilizing the dicyanamide anion, achieves efficiencies of more than 5%.

Porphyrin dye-sensitised solar cells utilising a solid-state electrolyte

We describe a porphyrin dye-sensitised solar cell utilising a solid state electrolyte containing the I¯/I₃¯ redox couple, which yields a performance of 5.3% under moderate light intensity and 4.8% at full sun.

Computer-aided detection of depression from magnetic resonance images

Magnetic resonance imaging (MRI) of the brain is used to detect depression disorder. However, a large number of MRI scans needs to be analyzed for such detection. Manual segmentation of the biomarkers in MRI scans by clinical experts can become time consuming and sometimes erroneous. This paper presents a study on computer-aided detection of depression from MRI scans. These systems have not yet been identified, categorized and compared in the literature. The paper covers fully automated to semi-automated detection systems. It also presents performance comparison for the considered systems.

Towards automated detection of depression from brain structural magnetic resonance images

Introduction : Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI).Methods : Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications.Results : The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification.Conclusion : Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.