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Two elementary models of ocean circulation, the well-known double-gyre stream function model and a single-layer quasi-geostrophic (QG) basin model, are used to generate flow data that sample a range of possible dynamical behavior for particular flow parameters. A reservoir computing (RC) machine learning algorithm then learns these models from the stream function time series. In the case of the QG model, a system of partial differential equations with three physically relevant dimensionless parameters is solved, including Munk- and Stommel-type solutions. The effectiveness of a RC approach to learning these ocean circulation models is evident from its ability to capture the characteristics of these ocean circulation models with limited data including predictive forecasts. Further assessment of the accuracy and usefulness of the RC approach is conducted by evaluating the role of both physical and numerical parameters and by comparison with particle trajectories and with well-established quantitative assessments, including finite-time Lyapunov exponents and proper orthogonal decomposition. The results show the capability of the methods outlined in this article to be applied to key research problems on ocean transport, such as predictive modeling or control. 

Abstract Monolayer (ML) molybdenum disulfide (MoS₂) is a novel 2-dimensional (2D) semiconductor whose properties have many applications in devices. Despite its potential, ML MoS₂ is limited in its use due to its degradation under exposure to ambient air. Therefore, studies of possible degradation prevention methods are important. It is well established that air humidity plays a major role in the degradation. In this paper, we investigate the effects of substrate hydrophobicity on the degradation of chemical vapor deposition (CVD) grown ML MoS 2 . We use optical microscopy, atomic force microscopy (AFM), and Raman mapping to investigate the degradation of ML MoS 2 grown on SiO 2 and Si 3 N 4 that are hydrophilic and hydrophobic substrates, respectively. Our results show that the degradation of ML MoS₂ on Si 3 N 4 is significantly less than the degradation on SiO 2 . These results show that using hydrophobic substrates to grow 2D transition metal dichalcogenide ML materials may diminish ambient degradation and enable improved protocols for device manufacturing. 

It is reported that chemical vapor deposition (CVD) grown bilayer (BL) MoS₂films are significantly more structurally stable in ambient air than CVD‐grown monolayer (ML) MoS₂films that have been reported to structurally degrade in ambient air. The authors present atomic force microscopy (AFM) images of preheated and as‐grown ML and multilayer MoS₂films after exposure to ambient air for periods of up to 2 years. The AFM images show that, in ambient air, preheated and as‐grown BL and thicker‐layer MoS₂films do not exhibit the growth of dendrites that is characteristic of ML degradation. Dendrites are observed to stop at the ML‐BL boundary. Raman spectra of BLs exposed for up to 2 years are similar to those reported for as‐grown BLs. The greater stability of BLs and thicker layers are attributed to their indirect band gaps that suppress Förster resonance energy transfer processes that have been proposed to be responsible for ML degradation. The results show that BL and thicker‐layer transition metal dichalcogenides with indirect band gaps may be structurally stable in air and useful for ambient‐air applications.