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Nucleate boiling is perhaps one of the most efficient cooling methodologies due to its large heat flux with a relatively low superheat. Nucleate boiling often occurs on surfaces oriented at different angles; therefore, understanding the behavior of bubble growth on various surface orientations is of importance. Despite significant advancement, numerous questions remain regarding the fundamentals of bubble growth mechanisms on oriented surfaces, a major source of enhanced heat dissipation. This work aims to accurately measure three-dimensional (3D), space- and time-resolved, local liquid temperature distributions surrounding a growing bubble on oriented surfaces that quantify the heat transfer from the superheated liquid layer during bubble growth. The dual tracer laser-induced fluorescence thermometry technique combined with high-speed imaging captures transient 2D temperature distributions within a 0.3 ºC accuracy at a 30 μm resolution. The results show that the temperature close to the heated surface and bubble interface exhibits an acute transient behavior at the time of bubble departure, and the growing bubble works as a pump to remove heat from the surface with a temperature difference of up to 10 °C during its growth and departure. The experimental results are compared with data available in the literature to validate the accuracy of the technique. It was found that the heat transfer coefficient close to the bubble interface and heater is approximately 1.3 times higher than the heat transfer coefficient in the bulk liquid. 

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.