We present a broadband tunable circular polarization converter composed of a single graphene sheet patterned with butterfly-shaped holes, a dielectric spacer, and a 7-layer graphene ground plane. It can convert a linearly polarized wave into a circularly polarized wave in reflection mode. The polarization converter can be dynamically tuned by varying the Fermi energy of the single graphene sheet. Furthermore, the 7-layer graphene acting as a ground plane can modulate the phase of its reflected wave by controlling the Femi energy, which provides constructive interference condition at the surface of the single graphene sheet in a broad bandwidth and therefore significantly broadens the tunable bandwidth of the proposed polarization converter.

We experimentally demonstrate a highly efficient, broadband linear polarization converter functioning at terahertz frequencies. The linear polarization converter is composed of three metasurfaces and two dielectric layers interlaced with each other. The neighboring unit cells of the central metasurface layer of the linear polarization converter exhibit strong electromagnetic coupling, which increases the number of resonances and results in significant bandwidth broadening. The simulation and experimental results show that in the frequency range of 0.2 to 0.4 THz, the proposed polarization converter has a flat transmission curve and exhibits a transmission efficiency that is higher than 80%. High performance terahertz polarization conversion is desirable in many fields, such as terahertz spectroscopy, imaging, and communications.

Herein, we propose a tunable plasmonic sensor with Fano resonators in an inverted U-shaped resonator. By manipulating the sharp asymmetric Fano resonance peaks, a high-sensitivity refractive index sensor can be realized. Using the multimode interference coupled-mode theory and the finite element method, we numerically simulate the influences of geometrical parameters on the plasmonic sensor. Optimizing the structure parameters, we can achieve a high plasmonic sensor with the maximum sensitivity for 840 nm/RIUand figure of merit for 3.9 × 10⁵. The research results provide a reliable theoretical basis for designing high sensitivity to the next generation plasmonic nanosensor.

In this paper, a multi-parameter integrated detection photonic crystal fiber (PCF) sensor based on surface plasmon resonance (SPR) is proposed for its application in detecting temperature, magnetic field, and refractive index. The air holes on both sides of the fiber core were coated with gold film and introduced to the temperature-sensitive medium (PDMS) and magnetic fluid (MF), detecting temperature and magnetic field, respectively. The graphene layer is also presented on the gold film of the D-type side polished surface to improve the sensor sensitivity. The sensor’s critical parameters’ influence on its performance is investigated using a mode solver based on the finite element method (FEM). Simulation results show when the samples refractive index (RI) detection is a range of 1.36~1.43, magnetic field detection is a range of 20~550 Oe, and the temperature detection is a range of 5~55 °C; the maximum sensor’s sensitivity obtains 76,000 nm/RIU, magnetic field intensity sensitivity produces 164.06 pm/Oe, and temperature sensitivity obtains −5001.31 pm/°C.

Reliability of nonvolatile resistive switching devices is the key point for practical applications of next-generation nonvolatile memories. Nowadays, nanostructured organic/inorganic heterojunction composites have gained wide attention due to their application potential in terms of large scalability and low-cost fabrication technique. In this study, the interaction between polyvinyl alcohol (PVA) and two-dimensional material molybdenum disulfide (MoS₂) with different mixing ratios was investigated. The result confirms that the optimal ratio of PVA:MoS₂ is 4:1, which presents an excellent resistive switching behavior. Moreover, we propose a resistive switching model of Ag/ZnO/PVA:MoS₂/ITO bilayer structure, which inserts the ZnO as the protective layer between the electrode and the composite film. Compared with the device without ZnO layer structure, the resistive switching performance of Ag/ZnO/PVA:MoS₂/ITO was improved greatly. Furthermore, a large resistive memory window up to 10⁴ was observed in the Ag/ZnO/PVA:MoS₂/ITO device, which enhanced at least three orders of magnitude more than the Ag/PVA:MoS₂/ITO device. The proposed nanostructured Ag/ZnO/PVA:MoS₂/ITO device has shown great application potential for the nonvolatile multilevel data storage memory.

The use of BaTiO₃ (BTO) ferroelectric thin films in flexible ferroelectric memory offers a promising pathway for next-generation nonvolatile memory applications, given BTO’s excellent ferroelectric properties, stability, high dielectric constant, and strong fatigue resistance. However, the fabrication of BTO on flexible substrates presents a significant technical challenge. In this study, we achieved high-quality, single-crystalline (111)-oriented BTO films on mica substrates through the design of buffer layers. The BTO films exhibit strong polarization properties (remnant polarization, 2P_r ∼15.63 μC/cm², and saturation polarization, 2P_s ∼36.61 μC/cm²), and the flexible BTO devices maintained exceptional stability under bending radii of 3.5 and 6 mm. After 10⁷ bipolar switching cycles, polarization showed only minor changes, with a retention time exceeding 10⁴ s. We further explored the application of flexible BTO ferroelectric memory in neuromorphic computing. The flexible BTO-based memory demonstrated adjustable synaptic behavior, effectively modulating EPSC (excitatory postsynaptic current) responses through pulse amplitude and width to simulate short-term memory. PPF (paired pulse facilitation) and LTP (long-term potentiation) behaviors verified its synaptic weight modulation capabilities, achieving 91.6% accuracy in neural network-based handwritten digit recognition after 10³ training cycles. These findings underscore the potential of flexible BTO ferroelectric memory for memory devices and neuromorphic computing, offering promising applications for wearable AI systems.