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Performing reactions in the presence of self-assembled hierarchical structures of amphiphilic macromolecules can accelerate reactions while using water as the bulk solvent due to the hydrophobic effect. We leveraged non-covalent interactions to self-assemble filled-polymer micelle nanoreactors (NR) incorporating gold nanoparticle catalysts into various amphiphilic polymer nanostructures with comparable hydrodynamic nanoreactor size and gold concentration in the nanoreactor dispersion. We systematically studied the effect of the hydrophobic co-precipitant on self-assembly and catalytic performance. We observed that co-precipitants that interact with gold are beneficial for improving incorporation efficiency of the gold nanoparticles into the nanocomposite nanoreactor during self-assembly but decrease catalytic performance. Hierarchical assemblies with co-precipitants that leverage noncovalent interactions could enhance catalytic performance. For the co-precipitants that do not interact strongly with gold, the catalytic performance was strongly affected by the hydrophobic microenvironment of the co-precipitant. Specifically, the apparent reaction rate per surface area using castor oil (CO) was over 8-fold greater than polystyrene (750 g/mol, PS 750); the turnover frequency was higher than previously reported self-assembled polymer systems. The increase in apparent catalytic performance could be attributed to differences in reactant solubility rather than differences in mass transfer or intrinsic kinetics; higher reactant solubility enhances apparent reaction rates. Full conversion of 4-nitrophenol was achieved within three minutes for at least 10 sequential reactions demonstrating that the nanoreactors could be used for multiple reactions. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.