RESUMO
The rapid development in nanotechnology has necessitated accurate and efficient assembly strategies for nanomaterials. Monolayer assembly of nanomaterials (MAN) represents a challenging and important architecture to manufacture and is critical in understanding interactions among nanomaterials, solvents, and substrates. MAN enables highly tunable performance in electronic and photonic devices. This review summarizes the recent progress on the methods to achieve MAN and discusses important control factors. Moreover, the importance of MAN is elaborated by a broad range of applications in electronics and photonics. In the end, the opportunities as well as challenges in manufacturing and new applications are outlooked.
RESUMO
Resonance structures and features are ubiquitous in optical science. However, capturing their time dynamics in real-world scenarios suffers from long data acquisition time and low analysis accuracy due to slow convergence and limited time windows. Here we report a physics-informed recurrent neural network to forecast the time-domain response of optical resonances and infer corresponding resonance frequencies by acquiring a fraction of the sequence as input. The model is trained in a two-step multi-fidelity framework for high-accuracy forecast, using first a large amount of low-fidelity physical-model-generated synthetic data and then a small set of high-fidelity application-specific data. Through simulations and experiments, we demonstrate that the model is applicable to a wide range of resonances, including dielectric metasurfaces, graphene plasmonics and ultra-strongly coupled Landau polaritons, where our model captures small signal features and learns physical quantities. The demonstrated machine-learning algorithm can help to accelerate the exploration of physical phenomena and device design under resonance-enhanced light-matter interaction.