Investigating Formate, Sulfate, and Halide Anions in Reversible Zinc Electrodeposition Dynamic Windows.

Reversible metal electrodeposition (RME) is an emerging and promising method for designing dynamic windows with electrically controllable transmission, excellent color neutrality, and wide dynamic range. Zn is a viable option for metal-based dynamic windows due to its fast switching kinetics and reversibility despite its very negative deposition voltage. In this manuscript, we study the effect of the supporting electrolyte anions for Zn electrodeposition on transparent tin-doped indium oxide. Through systematic additions or removal of components of the electrolytes, we are able to establish a link between the anions and the effectiveness of Zn RME. This insight allows us to design practical two-electrode 25 cm2 Zn dynamic windows that switch to <1% within 20 s. Lastly, we demonstrate that the accumulation of Zn(OH)2 species on the working electrode degrades the optical contrast of Zn windows during long-term cycling. However, the elimination of these species through acid immersion allows the windows to cycle at least 500 times. Reversible Zn electrodeposition in the presence of a polyethylene glycol additive further improves the cycle life to greater than 1000 cycles. Taken together, these studies highlight important design principles for the construction of robust dynamic windows based on Zn RME.

A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries.

In the past decade, the volume of "omics" data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information.