Search for: All records

Large scale synthesis of cycloparaphenyleneacetylenes has been challenging due to low macrocyclization yields and harsh aromatization methods that often decompose strained alkynes. Herein, a cis -stilbene-based building block is subjected to alkyne metathesis macrocylization. The following sequence of alkene-selective bromination and dehydrobromination afforded a [8]cycloparaphenyleneacetylene derivative in high yield with good scalability. X-Ray crystal structure and computational analysis revealed a unique same-rim conformation for the eight methyl groups on the nanohoop. 

Quantum defects are an emerging class of synthetic single‐photon emitters that hold vast potential for near‐infrared imaging, chemical sensing, materials engineering, and quantum information processing. Herein, we show that it is possible to optically direct the synthetic creation of molecularly tunable fluorescent quantum defects in semiconducting single‐walled carbon nanotube hosts through photochemical reactions. By exciting the host semiconductor with light that resonates with its electronic transition, we find that halide‐containing aryl groups can covalently bond to the sp²carbon lattice. The introduced quantum defects generate bright photoluminescence that allows tracking of the reaction progress in situ. We show that the reaction is independent of temperature but correlates strongly with the photon energy used to drive the reaction, suggesting a photochemical mechanism rather than photothermal effects. This type of photochemical reactions opens the possibility to control the synthesis of fluorescent quantum defects using light and may enable lithographic patterning of quantum emitters with electronic and molecular precision.

 

Quantum defects are an emerging class of synthetic single‐photon emitters that hold vast potential for near‐infrared imaging, chemical sensing, materials engineering, and quantum information processing. Herein, we show that it is possible to optically direct the synthetic creation of molecularly tunable fluorescent quantum defects in semiconducting single‐walled carbon nanotube hosts through photochemical reactions. By exciting the host semiconductor with light that resonates with its electronic transition, we find that halide‐containing aryl groups can covalently bond to the sp²carbon lattice. The introduced quantum defects generate bright photoluminescence that allows tracking of the reaction progress in situ. We show that the reaction is independent of temperature but correlates strongly with the photon energy used to drive the reaction, suggesting a photochemical mechanism rather than photothermal effects. This type of photochemical reactions opens the possibility to control the synthesis of fluorescent quantum defects using light and may enable lithographic patterning of quantum emitters with electronic and molecular precision.

 

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.