Two-dimensional (2-D) materials are of tremendous interest to integrated photonics given their singular optical characteristics spanning light emission, modulation, saturable absorption, and nonlinear optics. To harness their optical properties, these atomically thin materials are usually attached onto prefabricated devices via a transfer process. In this paper, we present a new route for 2-D material integration with planar photonics. Central to this approach is the use of chalcogenide glass, a multifunctional material which can be directly deposited and patterned on a wide variety of 2-D materials and can simultaneously function as the light guiding medium, a gate dielectric, and a passivation layer for 2-D materials. Besides claiming improved fabrication yield and throughput compared to the traditional transfer process, our technique also enables unconventional multilayer device geometries optimally designed for enhancing light-matter interactions in the 2-D layers. Capitalizing on this facile integration method, we demonstrate a series of highperformance glass-on-graphene devices including ultra-broadband on-chip polarizers, energyefficient thermo-optic switches, as well as graphene-based mid-infrared (mid-IR) waveguideintegrated photodetectors and modulators.

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Regressing the illumination of a scene from the representations of object appearances is popularly adopted in computational color constancy. However, it's still challenging due to intrinsic appearance and label ambiguities caused by unknown illuminants, diverse reflection properties of materials and extrinsic imaging factors (such as different camera sensors). In this paper, we introduce a novel algorithm – Cascading Convolutional Color Constancy (in short, C4) to improve robustness of regression learning and achieve stable generalization capability across datasets (different cameras and scenes) in a unique framework. The proposed C4 method ensembles a series of dependent illumination hypotheses from each cascade stage via introducing a weighted multiply-accumulate loss function, which can inherently capture different modes of illuminations and explicitly enforce coarse-to-fine network optimization. Experimental results on the public Color Checker and NUS 8-Camera benchmarks demonstrate superior performance of the proposed algorithm in comparison with the state-of-the-art methods, especially for more difficult scenes.

Volcano curves have proven to be particularly useful in new catalyst design in the field of heterogeneous catalysis. On the other hand, the further enhancement of the performance of the optimal catalyst for a given reaction is inherently limited by the Sabatier principle. In this work, microkinetic analysis has been carried out to examine the adsorption and catalytic behaviors of single-atom-doped Ga2O3 catalysts in propane dehydrogenation (PDH), which shows that the volcano-shaped activity plot can be broken through by Lewis acid–base interactions, making it possible to achieve better catalytic performance than that of the most active catalyst lying near the summit of the volcano. The reasoning behind this finding is that the presence of the Lewis acid–base interaction over metal-oxide surfaces may strengthen the coadsorption of a pair of amphoteric species at the M–O site, resulting in distinctly different chemisorption energy and transition state energy scaling relations. As a result, the formation energies of H&H coadsorption at the M–O site and H adsorption on top of O are identified as two different reactivity descriptors in the presence and absence of the Lewis acid–base interaction, respectively, with the resulting activity plots exhibiting a straight-line and a volcano-curve pattern. Further experiments verify that the theoretically predicted catalyst candidate Ir1–Ga2O3 is more effective than the previously reported trace-Pt-promoted Ga2O3 catalyst, which opens up a new way to the rational design of metal-oxide catalysts for the PDH process.