Laser-assisted chemical modification is demonstrated on ultrathin transitionmetal dichalcogenides (TMDs), locally replacing selenium by sulfur atoms. The photoconversion process takes place in a controlled reactive gas environment and the heterogeneous reaction rates are monitored via in situ real-time Raman and photoluminescence spectroscopies. The spatially localized photoconversion results in a heterogeneous TMD structure, with chemically distinct domains, where the initial high crystalline quality of the film is not affected during the process. This has been further confirmed via transmission electron microscopy as well as Raman and photoluminescence spatial maps. This study demonstrates the potential of laser-assisted chemical conversion for on-demand synthesis of heterogeneous 2D materials with applications in nanodevices.The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adfm.201802949. reliable route to tailor the physicochemical properties of 2D materials. However, other than inducing evaporation, oxidation or doping, the potential of postgrowth laserassisted method is yet to be verified for in situ changing of the chemical composition of transition-metal dichalcogenides (TMDs), such as creating localized ternary alloys, or even completely replacing the chalcogen atoms. Here, we report the successful laser-induced chemical modification of suspended TMD monolayer films via local exchange of the chalcogen atoms: selenides to sulfides. With the proposed method, total or partial replacement of the chalcogen atoms was achieved, in both WSe 2 and MoSe 2 suspended films. The time constants associated with the different photochemical mechanisms involved in the conversion process were studied by in situ monitoring the Raman and photoluminescence spectra of the samples. Our results suggest that postgrowth laser-induced chemical modifications could be considered as an alternative route for the fabrication of spatially localized ternary alloys and in-plane 2D heterostructures in a controlled gas environment.
Technological breakthroughs have enhanced our understanding of myocardial mechanics and physiological responses to detect early disease indicators. Using constitutive models to represent myocardium structure is critical for understanding the intricacies of such complex tissues. Several models have been developed to depict both passive response and active contraction of myocardium, however they require careful adjustment of material parameters for patient-specific scenarios and substantial time and computing resources. Thus, most models are unsuitable for employment outside of research. Deep learning (DL) has sparked interest in data-driven computational modeling for complex system analysis. We developed a DL model for assessing and forecasting the behavior of an active contraction model of the left ventricular (LV) myocardium under a patient-specific clinical setting. Our original technique analyzes a context in which clinical measures are limited: as model input, just a handful of clinical parameters and a pressure-volume (PV) loop are required. This technique aims to bridge the gap between theoretical calculations and clinical applications by allowing doctors to use traditional metrics without administering additional data and processing resources. Our DL model's main objectives are to produce a waveform of active contraction property that properly portrays patient-specific data during a cardiac cycle and to estimate fiber angles at the endocardium and epicardium. Our model accurately represented the mechanical response of the LV myocardium for various PV curves, and it applies to both idealized and patient-specific geometries. Integrating artificial intelligence with constitutive-based models allows for the autonomous selection of hidden model parameters and facilitates their application in clinical settings.
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