CO(2)electroreduction on bimetallic Pd-In nanoparticles

CO(2)electroreduction powered by renewable energy is an attractive strategy to recycle air-based carbon. One of the current challenges for the scale up of the technology is that the catalysts that show high faradaic yield at high current density (post-transitional metals such as In, Sn, Bi, Pb) suffer from very high overpotentials of more than 1 V. On the other hand, Pd can convert CO(2)to formate with almost no overpotential, but is readily poisoned by CO and deactivates when trying to reach industrially relevant currents. In this work we show the effect of the interaction of In and Pd in bimetallic nanoparticles, reaching the conclusion that this interaction causes a loss of selectivity towards formate and at the same time suppresses CO poisoning of Pd sites. The results of the catalyst characterization suggest the formation of intermetallic PdIn compounds that in turn cause the aforementioned behavior. Based on these results, it seems that geometric and electronic effects in Pd based intermetallic compounds can alleviate CO poisoning on Pd sites. In the case of PdIn intermetallics this leads to the loss of CO(2)reduction activity, but this strategy may be useful for other electrochemical reactions that suffer from the same problem of deactivation. It remains to be seen if intermetallic compounds of Pd with other elements can yield viable CO(2)reduction catalysts.Catalysis and Surface Chemistr

Same data, different conclusions : radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis

In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists’ gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for organizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed