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Register a new userDeep Learning for The Inverse Design of Mid-infrared Graphene Plasmons
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We theoretically investigate the plasmonic properties of mid-infrared graphene-based metamaterials and apply deep learning of a neural network for the inverse design. These artificial structures have square periodic arrays of graphene plasmonic resonators deposited on dielectric thin films. Optical spectra vary significantly with changes in structural parameters. Our numerical results are in accordance with previous experiments. Then, the theoretical approach is employed to generate data for training and testing deep neural networks. By merging the pre-trained neural network with the inverse network, we implement calculations for inverse design of the graphene-based metameterials. We also discuss the limitation of the data-driven approach.
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We theoretically investigate the plasmonic heating of graphene-based systems under the mid-infrared laser irradiation, where periodic arrays of graphene plasmonic resonators are placed on dielectric thin films. Optical resonances are sensitive to structural parameters and the number of graphene layers. Under mid-infrared laser irradiation, the steady-state temperature gradients are calculated. We find that graphene plasmons significantly enhance the confinement of electromagnetic fields in the system and lead to a large temperature rise compared to the case without graphene. The correlations between temperature change and the optical power, laser spot, and thermal conductivity of dielectric layer in these systems are discussed. Our numerical results are in accordance with experiments.
We exploited graphene nanoribbons based meta-surface to realize coherent perfect absorption (CPA) in the mid-infrared regime. It was shown that quasi-CPA frequencies, at which CPA can be demonstrated with proper phase modulations, exist for the graphene meta-surface with strong resonant behaviors. The CPA can be tuned substantially by merging the geometric design of the meta-surface and the electrical tunability of graphene. Furthermore, we found that the graphene nanoribbon meta-surface based CPA is realizable with experimental graphene data. The findings of CPA with graphene meta-surface can be generalized for potential applications in optical detections and signal processing with two-dimensional optoelectronic materials.
Metamaterials have recently established a new paradigm for enhanced light absorption in state-of-the-art photodetectors. Here, we demonstrate broadband, highly efficient, polarization-insensitive, and gate-tunable photodetection at room temperature in a novel metadevice based on gold/graphene Sierpinski carpet plasmonic fractals. We observed an unprecedented internal quantum efficiency up to 100% from the near-infrared to the visible range with an upper bound of optical detectivity of $10^{11}$ Jones and a gain up to $10^{6}$, which is a fingerprint of multiple hot carriers photogenerated in graphene. Also, we show a 100-fold enhanced photodetection due to highly focused (up to a record factor of $|E/E_{0}|approx20$ for graphene) electromagnetic fields induced by electrically tunable multimodal plasmons, spatially localized in self-similar fashion on the metasurface. Our findings give direct insight into the physical processes governing graphene plasmonic fractal metamaterials. The proposed structure represents a promising route for the realization of a broadband, compact, and active platform for future optoelectronic devices including multiband bio/chemical and light sensors.
Ultrasound detection is one of the most important nondestructive subsurface characterization tools of materials, whose goal is to laterally resolve the subsurface structure with nanometer or even atomic resolution. In recent years, graphene resonators attracted attention as loudspeaker and ultrasound radio, showing its potential to realize communication systems with air-carried ultrasound. Here we show a graphene resonator that detects ultrasound vibrations propagating through the substrate on which it was fabricated. We achieve ultimately a resolution of $approx7$~pm/$mathrm{sqrt Hz}$ in ultrasound amplitude at frequencies up to 100~MHz. Thanks to an extremely high nonlinearity in the mechanical restoring force, the resonance frequency itself can also be used for ultrasound detection. We observe a shift of 120~kHz at a resonance frequency of 65~MHz for an induced vibration amplitude of 100~pm with a resolution of 25~pm. Remarkably, the nonlinearity also explains the generally observed asymmetry in the resonance frequency tuning of the resonator when pulled upon with an electrostatic gate. This work puts forward a sensor design that fits onto an atomic force microscope cantilever and therefore promises direct ultrasound detection at the nanoscale for nondestructive subsurface characterization.
There exists a broad class of sequencing problems, for example, in proteins and polymers that can be formulated as a heuristic search algorithm that involve decision making akin to a computer game. AI gaming algorithms such as Monte Carlo tree search (MCTS) gained prominence after their exemplary performance in the computer Go game and are decision trees aimed at identifying the path (moves) that should be taken by the policy to reach the final winning or optimal solution. Major challenges in inverse sequencing problems are that the materials search space is extremely vast and property evaluation for each sequence is computationally demanding. Reaching an optimal solution by minimizing the total number of evaluations in a given design cycle is therefore highly desirable. We demonstrate that one can adopt this approach for solving the sequencing problem by developing and growing a decision tree, where each node in the tree is a candidate sequence whose fitness is directly evaluated by molecular simulations. We interface MCTS with MD simulations and use a representative example of designing a copolymer compatibilizer, where the goal is to identify sequence specific copolymers that lead to zero interfacial energy between two immiscible homopolymers. We apply the MCTS algorithm to polymer chain lengths varying from 10-mer to 30-mer, wherein the overall search space varies from 210 (1024) to 230 (~1 billion). In each case, we identify a target sequence that leads to zero interfacial energy within a few hundred evaluations demonstrating the scalability and efficiency of MCTS in exploring practical materials design problems with exceedingly vast chemical/material search space. Our MCTS-MD framework can be easily extended to several other polymer and protein inverse design problems, in particular, for cases where sequence-property data is either unavailable and/or is resource intensive.
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