An interfacial and bulk charge transport model for dye-sensitized solar cells based on photoanodes consisting of core-shell nanowire arrays.

Dye-sensitized solar cells (DSSCs) based on ordered photoanode morphologies, such as nanotubes and nanowires, are widely gaining attention because these geometries are believed to enhance interfacial charge transfer and bulk charge transport. Unfortunately, experimental results have yet to show substantial improvement to conversion efficiency over nanoparticle-based DSSCs. A model is developed to characterize the performance of an idealized photoanode based on an ordered array of transparent conductive nanowires coated with an anatase titania shell. The role of the interfacial electric field in nanowire-based DSSCs is explored computationally by turning electron migration ON or OFF. The results show that back-reaction rates are most strongly influenced by the electric field. These electron loss mechanisms can be reduced by several orders of magnitude, leading to improvements in short-circuit current, open-circuit voltage, and fill factor.

A large, open source dataset of stroke anatomical brain images and manual lesion segmentations.

Stroke is the leading cause of adult disability worldwide, with up to two-thirds of individuals experiencing long-term disabilities. Large-scale neuroimaging studies have shown promise in identifying robust biomarkers (e.g., measures of brain structure) of long-term stroke recovery following rehabilitation. However, analyzing large rehabilitation-related datasets is problematic due to barriers in accurate stroke lesion segmentation. Manually-traced lesions are currently the gold standard for lesion segmentation on T1-weighted MRIs, but are labor intensive and require anatomical expertise. While algorithms have been developed to automate this process, the results often lack accuracy. Newer algorithms that employ machine-learning techniques are promising, yet these require large training datasets to optimize performance. Here we present ATLAS (Anatomical Tracings of Lesions After Stroke), an open-source dataset of 304 T1-weighted MRIs with manually segmented lesions and metadata. This large, diverse dataset can be used to train and test lesion segmentation algorithms and provides a standardized dataset for comparing the performance of different segmentation methods. We hope ATLAS release 1.1 will be a useful resource to assess and improve the accuracy of current lesion segmentation methods.