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Indium phosphide quantum dots (InP QDs) are nontoxic nanomaterials with potential applications in photocatalytic and optoelectronic fields. Post-synthetic treatments of InP QDs are known to be essential for improving their photoluminescence quantum efficiencies (PLQEs) and device performances, but the mechanisms remain poorly understood. Herein, by applying ultrafast transient absorption and photoluminescence spectroscopies, we systematically investigate the dynamics of photogenerated carriers in InP QDs and how they are affected by two common passivation methods: HF treatment and the growth of a heterostructure shell (ZnS in this study). The HF treatment is found to improve the PLQE up to 16–20% by removing an intrinsic fast hole trapping channel ( τ h,non = 3.4 ± 1 ns) in the untreated InP QDs while having little effect on the band-edge electron decay dynamics ( τ e = 26–32 ns). The growth of the ZnS shell, on the other hand, is shown to improve the PLQE up to 35–40% by passivating both electron and hole traps in InP QDs, resulting in both a long-lived band-edge electron ( τ e > 120 ns) and slower hole trapping lifetime ( τ h,non > 45 ns). Furthermore, both the untreated and the HF-treated InP QDs have short biexciton lifetimes ( τ xx ∼ 1.2 ± 0.2 ps). The growth of an ultra-thin ZnS shell (∼0.2 nm), on the other hand, can significantly extend the biexciton lifetime of InP QDs to 20 ± 2 ps, making it a passivation scheme that can improve both the single and multiple exciton lifetimes. Based on these results, we discuss the possible trap-assisted Auger processes in InP QDs, highlighting the particular importance of trap passivation for reducing the Auger recombination loss in InP QDs. 

Plants mount coordinated immune responses to defend themselves against pathogens. However, the cellular components required for plant immunity are not fully understood. The jasmonate‐mimicking coronatine (COR) toxin produced byPseudomonas syringaepv.tomato(Pst)DC3000 functions to overcome plant immunity. We previously isolated eight Arabidopsis (scord) mutants that exhibit increased susceptibility to aCOR‐deficient mutant ofPstDC3000. Among them, thescord6mutant exhibits defects both in stomatal closure response and in restricting bacterial multiplication inside the apoplast. However, the identity ofSCORD6remained elusive.

In this study, we aim to identify theSCORD6gene.

We identifiedSCORD6via next‐generation sequencing and found it to beMURUS1(MUR1), which is involved in the biosynthesis ofGDP‐l‐fucose.

Discovery ofSCORD6asMUR1led to a series of experiments that revealed a multi‐faceted role ofl‐fucose biosynthesis in stomatal and apoplastic defenses as well as in pattern‐triggered immunity and effector‐triggered immunity, including glycosylation of pattern‐recognition receptors. Furthermore, compromised stomatal and/or apoplastic defenses were observed in mutants of several fucosyltransferases with specific substrates (e.g.O‐glycan,N‐glycan or theDELLAtranscriptional repressors). Collectively, these results uncover a novel and broad role ofl‐fucose and protein fucosylation in plant immunity.

 

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.