Recyclable Cu nanoparticle catalyzed azide-alkyne click polymerization

The Cu(I)-catalyzed alkyne-azide cycloaddition(CuAAC) has been developed into a powerful polymerization reaction for the synthesis of new polytriazoles with versatile properties. However, research on recyclable and reusable copper catalyst for click polymerization to meet the requirement of green chemistry was rarely reported. Copper nanoparticles were reported to be capable catalysts for CuAAC. Replacing conventional copper catalyst with copper nanoparticles may realize the recycle and reuse of the copper catalyst in click polymerization. In this paper, copper nanoparticles were prepared and used as an effective catalyst for click polymerization, and soluble polytriazoles with high molecular weights were obtained in excellent yields under optimized reaction conditions. Importantly, the copper nanoparticles can be recycled and reused for up to 11 times for the click polymerization. Moreover, introducing aggregation-induced emission(AIE)-active moiety of tetraphenylethylene into the monomers makes the resultant polymers retain the AIE feature. This work not only provides an efficient recyclable catalytic system for the azide-alkyne click polymerization, but also might inspire polymer chemists to use recyclable copper species to catalyze other polymerizations.

Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)

Background:Large uncertainty in modeling land carbon(C)uptake heavily impedes the accurate prediction of the global C budget.Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models(ESMs).Here we present a Matrix-based Ensemble Model Inter-comparison Platform(MEMIP)under a unified model traceability framework to evaluate multiple soil organic carbon(SOC)models.Using the MEMIP,we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter(SOM)models.By comparing the model outputs from the C-only and CN modes,the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled.Results:Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation(1900–2000).The SOC difference between the multi-layer models was remarkably higher than between the single-layer models.Traceability analysis indicated that over 80%of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes,while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation.Conclusions:The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction,especially between models with similar process representation.Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences.We stressed the importance of analyzing ensemble outputs from the fundamental model structures,and holding a holistic view in understanding the ensemble uncertainty.