Resonances in optical systems are useful for many applications, such as frequency comb generation, optical filtering, and biosensing. However, many of these applications are difficult to implement in optical metasurfaces because traditional approaches for designing multiresonant nanostructures require significant computational and fabrication efforts. To address this challenge, we introduce the concept of Fourier lattice resonances (FLRs) in which multiple desired resonances can be chosen and used to dictate the metasurface design. Because each resonance is supported by a distinct surface lattice mode, each can have a high quality factor. Here, we experimentally demonstrate several metasurfaces with flexibly placed resonances (e.g., at 1310 and 1550 nm) and -factors as high as 800 in a plasmonic platform. This flexible procedure requires only the computation of a single Fourier transform for its design, and is based on standard lithographic fabrication methods, allowing one to design and fabricate a metasurface to fit any specific, optical-cavity-based application. This work represents a step toward the complete control over the transmission spectrum of a metasurface.
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http://dx.doi.org/10.1021/acsnano.1c10710 | DOI Listing |
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January 2025
CNR NANOTEC Institute of Nanotechnology, Via Monteroni, 73100, Lecce, Italy.
Photonics bound states in the continuum (BICs) are peculiar localized states in the continuum of free-space waves, unaffected by far-field radiation loss. Although plasmonic nano-antennas squeeze the optical field to nanoscale volumes, engineering the emergence of quasi-BICs with plasmonic hotspots remains challenging. Here, the origin of symmetry-protected (SP) quasi-BICs in a 2D system of silver-filled dimers, quasi-embedded in a high-index dielectric waveguide, is investigated through the strong coupling between photonic and plasmonic modes.
View Article and Find Full Text PDFPolymers (Basel)
January 2025
Department of Mechanical Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
Metamaterials are pushing the limits of traditional materials and are fascinating frontiers in scientific innovation. Mechanical metamaterials (MMs) are a category of metamaterials that display properties and performances that cannot be realized in conventional materials. Exploring the mechanical properties and various aspects of vibration and damping control is becoming a crucial research area.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Physics and Electronic Engineering, Guangzhou University, Guangzhou 510006, China.
Refractive index (RI) and temperature (T) are both critical environmental parameters for environmental monitoring, food production, and medical testing. The paper develops a D-shaped photonic crystal fiber (PCF) sensor to measure RI and T simultaneously. Its cross-sectional structure encompasses a hexagonal-hole lattice, with one hole selectively filled with toluene for temperature sensing.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
Department of Civil Engineering, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA.
This paper focuses on the theoretical and analytical modeling of a novel seismic isolator termed the Passive Friction Mechanical Metamaterial Seismic Isolator (PFSMBI) system, which is designed for seismic hazard mitigation in multi-story buildings. The PFSMBI system consists of a lattice structure composed of a series of identical small cells interconnected by layers made of viscoelastic materials. The main function of the lattice is to shift the fundamental natural frequency of the building away from the dominant frequency of earthquake excitations by creating low-frequency bandgaps (FBGs) below 20 Hz.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA.
From sequences of discrete events, humans build mental models of their world. Referred to as graph learning, the process produces a model encoding the graph of event-to-event transition probabilities. Recent evidence suggests that some networks are easier to learn than others, but the neural underpinnings of this effect remain unknown.
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