Multiple electrochemically accessible colour states in surface-confined metal-organic monolayers: Stepwise embedding individual metal centres

Sequential covalent embedding of cobalt, osmium, and iron complexes of 4'-(pyridin-4-yl)-2,2':6',2''-terpyridine to surface-enhanced support pre-functionalized with a templating layer results in hetero-bimetallic (Os-Fe and Co-Fe) and hetero-trimetallic (Co-Os-Fe) monolayer materials....

Fe-N4 and Co-N4 dual sites for boosting oxygen electroreduction in Zn-air batteries

Compared to the individual metal sites, e.g. Fe-N4 site, the synergistic effect of the dual-metal sites can further adjust the interaction of the reactants or intermediates with the active sites,...

A theoretical scenario for Galactic RR Lyrae in the Gaia data base: constraints on the parallax offset

ABSTRACT On the basis of an extended set of non-linear convective RR Lyrae pulsation models we derive the first theoretical light curves in the Gaia bands G, GBP, and GRP and the corresponding intensity-weighted mean magnitudes and pulsation amplitudes. The effects of chemical composition on the derived Bailey diagrams in the Gaia filters are discussed for both Fundamental and first overtone mode pulsators. The inferred mean magnitudes and colours are used to derive the first theoretical Period–Wesenheit relations for RR Lyrae in the Gaia filters. The application of the theoretical Period–Wesenheit relations for both the Fundamental and first overtone mode to Galactic RR Lyrae in the Gaia Data Release 2 data base and complementary information on individual metal abundances allows us to derive theoretical estimates of their individual parallaxes. These results are compared with the astrometric solutions to conclude that a very small offset, consistent with zero, is required in order to reconcile the predicted distances with Gaia results.

Development of predicitve models to distinguish metals from non-metal toxicants, and individual metal from one another

Background Evaluating the toxicity of chemical mixture and their possible mechanism of action is still a challenge for humans and other organisms. Microarray classifier analysis has shown promise in the toxicogenomic area by identifying biomarkers to predict unknown samples. Our study focuses on identifying gene markers with better sensitivity and specificity, building predictive models to distinguish metals from non-metal toxicants, and individual metal from one another, and furthermore helping understand underlying toxic mechanisms. Results Based on an independent dataset test, using only 15 gene markers, we were able to distinguish metals from non-metal toxicants with 100% accuracy. Of these, 6 and 9 genes were commonly down- and up-regulated respectively by most of the metals. 8 out of 15 genes belong to membrane protein coding genes. Function well annotated genes in the list include ADORA2B, ARNT, S100G, and DIO3. Also, a 10-gene marker list was identified that can discriminate an individual metal from one another with 100% accuracy. We could find a specific gene marker for each metal in the 10-gene marker list. Function well annotated genes in this list include GSTM2, HSD11B, AREG, and C8B. Conclusions Our findings suggest that using a microarray classifier analysis, not only can we create diagnostic classifiers for predicting an exact metal contaminant from a large scale of contaminant pool with high prediction accuracy, but we can also identify valuable biomarkers to help understand the common and underlying toxic mechanisms induced by metals.