Hongxing Dong

This work presents an absorptive–transmissive integrated metamaterial window using a quartz substrate and micrometer‐scale metallic meshes. The structure employs a three‐layer design comprising a resonant layer, a dielectric spacer, and a frequency‐selective surface. It achieves an average visible transmittance of 68.5% over 380–780 nm, along with a dual‐band microwave response: an average absorption of 85% in the 5–11.4 GHz band and a transmittance that rises from 20 GHz and peaks around 34 GHz. The low‐frequency absorption originates from localized surface plasmon resonance in the top layer, whereas the high‐frequency transmission results from multilayer interference. This work provides a design strategy for integrated windows capable of simultaneously managing optical and microwave radiation.

A water-based oxalic-acid-assisted evaporation strategy enables synthesis of a lead-free [MnBr ₄ ]BrCs ₃ anti-perovskite with narrow-band green emission, anti-thermal-quenching behavior, and phosphor-converted UV LED performance.

With the increasing application of novel glass materials in the field of optics, traditional empirical and trial-and-error approaches to glass development are gradually becoming insufficient to meet escalating performance demands. In this study, we propose a neural network-based machine learning method for the design of advanced fluoride glass materials. Predictive models for density and refractive index were first developed based on online fluoride glass datasets. Moreover, SHapley Additive exPlanations (SHAP) analysis was adopted to uncover the quantitative composition-property relationship. Then, the well-trained model was employed for inverse design, identifying specific compositions that fulfill desired properties in terms of density and refractive index. Finally, several recommended compositions were experimentally validated and the measured density and refractive index matched well with the corresponding input values, thereby confirming the effectiveness of the proposed method in designing new fluoride glass materials.

With the increasing application of novel glass materials in the field of optics, traditional empirical and trial-and-error approaches to glass development are gradually becoming insufficient to meet escalating performance demands. In this study, we propose a neural network-based machine learning method for the design of fluoride glass materials. Predictive models for density and refractive index were firstly developed by using the existing fluoride glass datasets. Moreover, SHapley Additive exPlanations (SHAP) analysis is adopted to uncover the quantiative compsotion-peformance relationship. The well trained model was then employed for inverse design to identify compositions that fulfill specific requirements. Finally, several recommended compositions were experimentally validated and the measured density and refractive index matches well with the desired/input values, thereby confirming the effectiveness of the proposed method in designing new fluoride glass materials.

Photonic crystals (PhCs) have demonstrated great potential for use in integrated photonic systems. However, traditional design methods often struggle with low efficiency and limited flexibility. While deep learning approaches offer innovative solutions for the inverse design, existing generative models like generative adversarial network and variational autoencoders still face challenges such as training instability or excessive noise. Here, a novel generative design framework based on the diffusion model is presented to achieve the inverse design of high‐precision and customized refraction structures. A comprehensive dataset consisting of operating frequency, refracted angles and corresponding structure patterns is constructed by calculating the equifrequency contours of various PhCs at a resolution of 64 × 64. Based on this dataset, customized PhC structures are successfully generated by using a diffusion model combined with the U‐Net model. This design can predict cell patterns with allowable incident and refraction angles ranging from 0°∼80° and −80°∼80°, respectively. And if the types of structures in the dataset are increased, the solution space can be further expanded. A normalized design approach ensures adaptability to multi‐scale scenarios. Finite‐difference time‐domain simulations and numerical analysis indicate that 85 % of the 1000 tested refracted angle errors measured by L2‐norm are below 0.1. Such strong correlation between targets and simulated results demonstrates the high stability and precision of our diffusion model‐based approach, which may provide a promising avenue for the automated inverse design of photonic devices.

Fluorescent antennas (FAs) serve as promising optical concentrators for optical wireless communication (OWC) by overcoming the Étendue limit. Quantum dots‐polymer nanocomposites exhibit superior optical properties for FAs. However, the poor chemical affinity between the surface ligands of quantum dots (QDs) and the polar functional groups in polymer matrices reduces interfacial compatibility, triggering ligand desorption and interparticle aggregation during photopolymerization. These effects severely degrade the photoluminescence efficiency and long‐term stability, thus limiting the practical application of polymer‐based FAs. Herein, a high‐performance nanocomposite is demonstrated by embedding ODPA‐passivated CdSe/CdS QDs into a polyurethane acrylate (PUA) matrix through doctor‐blade coating and photopolymerization. Molecular dynamics simulations and density functional theory calculations reveal that QDs improve interfacial compatibility with PUA matrix through hydrogen‐bond crosslinking between surface ligands and PUA hard segments, along with alkyl chain‐induced plasticization of soft segments. Optimizing the QD concentration results in nanocomposites with a near‐unity photoluminescence quantum yield of 99.15% and enhanced environmental stability. The sandwich‐structured FA demonstrates a −3 dB bandwidth of 8.6 MHz, a 154 Mbps data rate using orthogonal frequency division multiplexing (OFDM) modulation. This work presents a readily processable nanocomposite system with high efficiency and stability, advancing OWC applications in terrestrial and underwater environments.

Highly polarized micro-lasers are of paramount importance for applications in optical communications, high-resolution imaging, and quantum information. However, their realization remains challenging due to fabrication complexities and the anisotropy limitations of gain materials. In this study, we propose an innovative approach that leverages anisotropic light-emitting nanoplatelets (NPLs) as building blocks, which are orientationally assembled to form microsphere cavities, thereby achieving a highly linearly polarized micro-laser. By the microemulsion templating method, NPLs are self-assembled into microspheres with an edge-up orientation, further enhancing the anisotropy of the assembled spheres. The synergistic orientation of the spherical cavity structure and the oriented NPLs augments light polarization within the cavity. Notably, the fabricated highly linearly polarized (0.89) micro-laser, utilizing CdSe/CdS/ZnS NPLs, exhibits excellent lasing properties, including a low threshold (≈20.4 μJ cm⁻²), and a high cavity quality factor (≈4679). In conclusion, this approach represents a significant advancement in designing highly linearly polarized micro-lasers and paves the way for their application in next-generation photonic integrated devices.