A highly porous metal-organic framework (MOF) incorporating both exposed metal sites and nitrogen-rich triazole groups was successfully constructed via solvothermal assembly of a clicked octcarboxylate ligand and Cu(II) ions, which presents a high affinity toward CO2 molecules clearly verified by gas adsorption and Raman spectral detection. The constructed MOF featuring CO2-adsorbing property and exposed Lewis-acid metal sites could serve as an excellent catalyst for CO2-based chemical fixation. Catalytic activity of the MOF was confirmed by remarkably high efficiency on CO2 cycloaddition with small epoxides. When extending the substrates to larger ones, its activity showed a sharp decrease. These observations reveal that MOF-catalyzed CO2 cycloaddition of small substrates was carried out within the framework, while large ones cannot easily enter into the porous framework for catalytic reactions. Thus, the synthesized MOF exhibits high catalytic selectivity to different substrates on account of the confinement of the pore diameter. The high efficiency and size-dependent selectivity toward small epoxides on catalytic CO2 cycloaddition make this MOF a promising heterogeneous catalyst for carbon fixation.

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.

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