Continuous programmable mid-infrared thermal emitter and camouflage based on the phase-change material In3SbTe2.

In3SbTe2 (IST), a new non-volatile phase-change material (PCM), promises highly tunable infrared optical properties and offers a distinct path to the significant modulation of its optical scattering fingerprint, suggesting tremendous applications. In this Letter, we demonstrate and optimize a four-layer emitter based on IST, achieving an ultra-wide average emissivity variation of more than 94% in the middle-infrared region (MIR, 3-5 µm). This remarkable emissivity difference can be further continuously modified by changing the structural composition in terms of the amorphous and crystalline states of the IST layers. Based on this continuous programmable emission, the MIR emission characteristics of marble, maple leaf, and blue polyvinyl chloride are successfully imitated together on a desert background, demonstrating the programmable and multi-level MIR optical camouflage capabilities of IST. This work provides a promising platform for continuously modulating emission characteristics and offers a reference for the subsequent application of programmable optical devices.

Deep learning-based inverse design optimization of efficient multilayer thermal emitters in the near-infrared broad spectrum.

This study proposes a deep learning architecture for automatic modeling and optimization of multilayer thin film structures to address the need for specific spectral emitters and achieve rapid design of geometric parameters for an ideal spectral response. Multilayer film structures are ideal thermal emitter structures for thermophotovoltaic application systems because they combine the advantages of large area preparation and controllable costs. However, achieving good spectral response performance requires stacking more layers, which makes it more difficult to achieve fine spectral inverse design using forward calculation of the dimensional parameters of each layer of the structure. Deep learning is the main method for solving complex data-driven problems in artificial intelligence and provides an efficient solution for the inverse design of structural parameters for a target waveband. In this study, an eight-layer thin film structure composed of SiO2/Ti and SiO2/W is rapidly reverse engineered using a deep learning method to achieve a structural design with an emissivity better than 0.8 in the near-infrared band. Additionally, an eight-layer thin film structure composed of 3 × 3 cm SiO2/Ti is experimentally measured using magnetron sputtering, and the emissivity in the 1-4 µm band was better than 0.68. This research provides implications for the design and application of micro-nano structures, can be widely used in the fields of thermal imaging and thermal regulation, and will contribute to developing a new paradigm for optical nanophotonic structures with a fast target-oriented inverse design of structural parameters, such as required spectral emissivity, phase, and polarization.

Design and optimization of mid-infrared hot electron detector based on Al/GaAs fishnet nanostructure for CO2 sensing.

Hot electron detectors (HEDs) based on plasmon resonance can circumvent a semiconductor's bandgap limitation and have high sensitivity, suitable for infrared gas detectors. Unfortunately, there are few literature reports on research in the mid-infrared (MIR) region. Herein, we design and optimize a HED based on Al/GaAs fishnet nanostructure for MIR CO2 sensing, and its optical-electrical properties are numerically studied. Surface plasmons not only achieve strong absorptance at CO2 emission wavelength but also greatly improve the photoelectric responsivity over a plane structure detector (∼42times). By changing the thickness of the GaAs layer, the detection wavelength can also be actively adjusted, achieving a larger range of multi-gas detection. The effect of external voltage is also considered. This work highlights a potential engineering application value and offers a path toward more compact and efficient MIR gas detectors.

Tunable absorption as multi-wavelength at infrared on graphene/hBN/Al grating structure.

This work designs a graphene/hBN/Al grating anisotropic hybrid structure. Formed by strong coupling between plasmonic Magnetic polaritons (MPs) in the metal grating and phonon-plasmon polaritons, hybrid hyperbolic phonon-plasmon polaritons in the graphene/hBN film have been excited, resulting in three sharp, high absorption peaks, which are 0.75, 0.97 and 0.97, formed at 5.92 µm, 6.32 µm, and 7.64 µm respectively. The absorption mechanisms have been theoretically analyzed. Local electromagnetic field and power dissipation density are depicted for further elucidating the underlying mechanisms. The different structural parameters and chemical potential, which affect the absorption peak were discussed. These numerical results can provide potential application in the field of optical detection and optoelectronic.

Graphene plasmonics for surface enhancement near-infrared absorptivity.

Monolayer graphene has poor absorption in the near-infrared region. Its layer is only as thick as a single atom so it cannot have a high absorptivity. In this paper, in order to form a hybrid system, the absorption characteristics of monolayer graphene covering a metal/dielectric/metal substrate has been theoretically analyzed. The magnetic polaritons in the metal/dielectric couple with the plasmonic resonance in the graphene to dramatically enhance the graphene absorptivity. This study analyzes the factors that enhance the absorptivity, including the geometric parameters and the relative positions of the graphene. The local electromagnetic field and the power dissipation density are illustrated to explain the underlying mechanisms further. These numerical results can provide potential application in the field of optical detection and optoelectronic devices.