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The influence of thermal treatment under reducing conditions on the structural and hydrogen evolution reaction (HER) properties of Au@MoS 2 structures is reported in this work. After a thermal treatment at 800 °C under H 2 atmosphere, the Au@MoS 2 nanostructures exhibit remarkable performance with an overpotential of 203 mV vs reversible hydrogen electrode (RHE) at 10 mA/cm2 and a Tafel slope of 55 mV/dec which is similar to the value reported for edges sites in MoS 2. The samples were investigated by combining ex-situ and in-situ aberration-corrected transmission electron microscopy (TEM) experiments under controlled atmosphere, Raman and XPS spectroscopies. (S) TEM characterization highlights the formation of the core-shell system as well as the decrease of the number of shell layers up to incomplete layers induced by the thermal treatments. The plasmonic-assisted HER performances of these nanostructures under LED illumination are also explored and an enhancement by about 10 % of the current density, depending on the wavelength used, is also reported. These results open new avenues for the design of core shell nanostructures based on transition metal dichalcogenides for the hydrogen evolution reaction. [Display omitted] • Influence of thermal annealing under H 2 on Au@MoS 2 nanostructures. • Formation of structures with an incomplete MoS 2 monolayer at 800 °C. • HER improved with an overpotential of 203 mV and a Tafel slope of 55 mV/dec. • Prompt, steady, and reversible photoelectrochemical response of the Au@MoS 2. [ABSTRACT FROM AUTHOR]

At present, transmission electron microscopy is regarded as the main option when dealing with phase characterization for materials at a nanometric scale. The development and improvement of complementary techniques such as energy-dispersive X-ray spectroscopy (EDS), electron energy loss spectroscopy (EELS), imaging detectors and associated computational methods provide a huge variety of choices to determine the composition and crystal structure at any region of a specimen. Despite all these advancements, phase identification for some specific materials having phases with similar structure or similar composition might still be a difficult procedure to perform. To overcome this difficulty, a new method is proposed which combines the acquisition of low-loss EELS spectra with a subsequent analysis driven by machine learning based algorithms (clustering). In the present research, this new approach is applied to a set of industrial duplex stainless steels having different compositions and it is contrasted to other characterization alternatives. As opposed to the other options, this method proved to be effective, reliable and time-saving. • Innovative fusion of EELS and machine learning automates duplex stainless steel phase imaging efficiently and accurately. • Our method outperforms conventional approaches, offering comprehensive phase identification in duplex stainless steel. • DFT validation confirms accuracy, ensuring reliable characterization of duplex stainless steel phases. [ABSTRACT FROM AUTHOR]