Solar thermal decoupled water electrolysis process I: Proof of concept

A new concept for a solar thermal electrolytic process was developed for the production of H-2 from water. A metal oxide is reduced to a lower oxidation state in air with concentrated solar energy. The reduced oxide is then used either as an anode or solute for the electrolytic production of H-2 in either an aqueous acid or base solution. The presence of the reduced metal oxide as part of the electrolytic cell decreases the potential required for water electrolysis below the ideal 1.23 V required when H-2 and O-2 evolve at 1 bar and 298 K. During electrolysis, H-2 evolves at the cathode at 1 bar while the reduced metal oxide is returned to its original oxidation state, thus completing the H-2 production cycle. Ideal sunlight-to-hydrogen thermal efficiencies were established for three oxide systems: Fe2O3-Fe3O4, Co3O4-CoO, and Mn2O3-Mn3O4. The ideal efficiencies that include radiation heat loss are as high or higher than corresponding ideal values reported in the solar thermal chemistry literature. An exploratory experimental study for the iron oxide system confirmed that the electrolytic and thermal reduction steps occur in a laboratory scale environment.

Assessment of Potential Orthodontic Mini-implant Insertion Sites Based on Anatomical Hard Tissue Parameters: A Systematic Review

To estimate the applicability of potential sites for insertion of orthodontic mini-implants (OMIs) by a systematic review of studies that used computed tomography (CT) or cone beam CT to evaluate anatomical bone quality and quantity parameters, such as bone thickness, available space, and bone density.

Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

Optical coherence tomography (OCT) is a well-established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graph-based multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart segmentation scheme. This allows the segmentation to be computed in seconds instead of minutes, without deteriorating the segmentation accuracy, making it ideal for a clinical setup. An extensive evaluation on 20 OCT datasets of healthy eyes was performed and showed a mean unsigned segmentation error of 3.05 ±0.54 μm over all datasets when compared to the average observer, which is lower than the inter-observer variability. Similar performance was measured for the task of drusen segmentation, demonstrating the usefulness of using soft constraints as a tool to deal with pathologies.