Metasurfaces offer unique advantages in manipulating the dispersion of optical fields; yet the achievable dispersion of metasurfaces has long been constrained by the limited phase modulation of complex nanostructures. Here we introduce a metasurface design method based on convergence phase that enables ultra-dispersive metasurfaces using structurally simple nanopillars with relaxed fabrication requirements. By overlapping phase of multiple wavelengths with that of a central wavelength, we demonstrated an ultra-dispersive metalens supporting phase variations exceeding 1200π - a more than 30-fold enhancement over existing approaches. Leveraging this method, we fabricated metalenses that exhibit unprecedented dispersion characteristics and implemented the metalens in a miniaturized chromatic confocal sensor for a measurement range of 13 mm with an axial resolution of 50 nm. Additionally, we demonstrated millimeter-scale depth-of-field spectral tomography, highlighting the significant advantage and immense potential of our method. Our research has established a generalizable theoretical foundation for designing ultra-dispersive metasurfaces that can be mass-produced and deployed for practical applications.

Liver cancer is one of the most common malignant tumors globally, primarily including hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). To date, it lacks of an effective liquid biopsy for differential diagnosis. The direct test of tumor-derived small extracellular vesicles (sEVs) via sEV membrane proteins in serum can provide a rapid way to clinical diagnosis of liver cancer. However, the mainstream optical label techniques for quickly detecting serum protein biomarkers simultaneously target sEVs membrane proteins and homologous free proteins, which lowers diagnostic sensitivity and specificity considerably. In this work, we propose using meta-plasmonic biosensors to detect serum sEVs membrane proteins directly for liver cancer diagnosis, and evaluate the influences of homologous free proteins on testing results through a comprehensive biophysical modeling and experimental verifications. Our results show that the meta-plasmonic approach minimizes interference from homologous free proteins owing to its label-free optical readout, and demonstrates the exceptional sensitivity and specificity to distinguish the health control, benign liver disease, HCC, and ICC via the specific sEV membrane proteins of CD63 and GPC3. Our findings indicate that tumor-derived sEVs in serum can be detected directly by meta-plasmonic assays. Moreover, the proposed method for liver cancer detection could be also adaptable to other cancer diagnostics.

The development of lightweight electromagnetic wave-absorbing materials with integrated radar-infrared capabilities is of significant importance for the advancement of stealth technology. The proposed metastructure is hexagonal prism ring shell, inspired by moth-eye architecture. This metastructure was designed and fabricated via 3D printing, using polyetheretherketone-carbon fiber (PEEK-CF) composite filaments. During the design process of the metastructure, an artificial neural network with dual outputs was established to enable simultaneous prediction of reflection loss and effective absorption bandwidth, thereby allowing comprehensive evaluation of the microwave absorption performance across a broad parameter space. The optimized dual-coated sample exhibited a minimum reflection loss of-41.93 dB at 7.8 GHz, accompanied by an effective absorption bandwidth of 8.24 GHz. The Al (aluminum) powder coating reduced the infrared emissivity of the PEEK-CF substrate from 0.876 to 0.254, thereby enabling effective infrared stealth without compromising EMW absorption. This study provides a generalized paradigm for investigating metastructure stealth.

Basalt/FeCoNi composites were fabricated for use as microwave absorbers, leveraging the excellent magnetic properties of FeCoNi and the dielectric properties of basalt. The composites were synthesized through a chemical reduction process, with basalt flakes of different sizes serving as the wave-transmitting substrates. The FeCoNi nanolayers were deposited on the basalt surface and thermally annealed to control crystallinity and optimize the electromagnetic response. The composite materials exhibited a transition from amorphous to mixed BCC/FCC phases as the annealing temperature increased. The optimized sample, unannealed amorphous basalt/FeCoNi-600 (synthesized at room temperature), achieved a minimum reflection loss of −39.05 dB at 4.91 GHz with a 4.85 GHz (RL < −10 dB) effective bandwidth. The microwave absorption performance was attributed to the synergistic effects of conduction loss, interfacial polarization, and magnetic resonance, combined with balanced impedance matching. These results suggest that basalt/FeCoNi composites, with their lightweight and tunable structure, hold significant potential as high-performance microwave absorbers.

Optical Fourier surfaces (OFSs), featuring sinusoidally profiled diffractive elements, manipulate light through patterned nanostructures and incident angle modulation. Compared to altering structural parameters, tuning elevation and azimuthal angles offers greater design flexibility for light field control and significantly reduces fabrication cost. However, angle-resolved responses of OFSs are often complex due to diverse mode excitations and couplings, complicating the alignment between simulations and practical fabrication. Here, we present a reality-infused deep learning framework, empowered by angle-resolved measurements, to enable real-time and accurate predictions of angular dispersion in quasi-OFSs. This approach captures critical features, including nanofabrication and measurement imperfections, which conventional simulation-based methods typically overlook. Our framework significantly accelerates the design process while achieving predictive performance highly consistent with experimental observations across broad angular and spectral ranges. Our study supports valuable insights into the development of OFS-based devices, and represents a paradigm shift from simulation-driven to reality-infused methods, paving the way for advancements in optical design applications.

Crystalline porous materials have evolved significantly with the advent of metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), yet hydrogen-bonded organic frameworks (HOFs) represent a distinct paradigm shift from static to adaptive porosity. Unlike their coordination- or covalent-bonded counterparts, HOFs are assembled via weak, reversible hydrogen interactions, endowing the framework with intrinsic "softness" and adaptive flexibility. This unique structural nature allows for reversible transformations─such as breathing, gate-opening, and layer sliding─in response to external stimuli. In this perspective, we systematically discuss the design rules of smart HOFs, highlighting how specific flexibility mechanisms are translated into advanced functionalities across four pivotal domains. We explore how adaptive pore environments enable the discrimination of similar-sized molecules and "self-healing" capabilities in separation processes, and how structural perturbations are converted into readable optical or electrical signals for precise sensing. Furthermore, we examine the leveraging of dynamic luminescence and topological switching for smart optoelectronics and information security, as well as the utilization of stimuli-responsive drug release and biocompatibility for precision biomedical therapy. Finally, we provide a critical outlook on the future challenges regarding stability, predictability, and processability, aiming to bridge the gap between theoretical design and practical deployment of smart HOF materials.

In this paper, a new method is proposed for failure diagnosis of programmable metasurfaces, which jointly uses the single-point measurement strategy and Bernoulli-Gaussian (BG) prior. Specifically, leveraging the dynamic tuning property of programmable metasurfaces, the radiated fields is measured with a single fixed probe, therefore reducing the time and error of the measurement process. Moreover, the BG prior inherent in the programmable metasurface under test is exploited during the reconstruction process in order to perform the diagnosis with a small number of measurements without resorting to prior knowledge of the radiation pattern of the failure-free programmable metasurface. The accuracy, efficiency, and robustness of the proposed method are verified through a set of representative numerical experiments, where the results are compared with those from existing diagnostic methods.

Developing a simple and efficient multi-functionally integrated absorber is promising but challenging. In this work, a lightweight but ultrahigh-strength polyvinyl alcohol (PVA)/aramid nanofiber (ANF)/carbon nanotube (CNT) (PAC) composite foam was fabricated through the self-assembly of hydrogen bonds. This composite foam had a hollow skeleton structure, achieving an ultra-high compressive strength of 6.71 MPa and could withstand a weight of more than 27,000 times its own weight. Its internal gradient pore structure provided a large number of reflection and scattering channels for the wastage of electromagnetic waves. Under the cooperation of impedance matching and multiple loss mechanisms, it exhibited a minimum reflection loss (RLmin) value of -59.12 dB and a wide effective absorption bandwidth (EAB) of 7.3 GHz. In addition, the PAC composite foam presented excellent radar and infrared stealth properties, and the radar cross-section scattering attenuation reached 42.92 dBm2, which showed great application advantages in complex environments. Therefore, such a convenient and efficient strategy is expected to provide a new route to design high-performance multifunctional integrated composite materials.

Hepatocellular carcinoma (HCC) is a leading cause of global cancer-related mortality, with delayed diagnosis adversely affecting patient outcomes. Liquid biopsy techniques using small extracellular vesicles (EVs) offer potential for cancer detection, though current methods are often time-consuming and require complex equipment, limiting clinical utility. Here, we report a metasurface-enhanced EV detection chip (metaEVchip) platform for the dynamic monitoring of HCC-specific EVs, enabling rapid detection and purification. This system provides results within 5 min. The platform integrates a plasmonic metasurface with a Kolmogorov-Arnold network (KAN) to facilitate real-time EV capture, enhancing detection speed while achieving an area under the curve (AUC) of 0.914 for HCC screening. By optimizing the purification process and incorporating complementary detection of alpha-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist II (PIVKAII), the AUC for HCC screening reaches 0.961 in an external validation set. These results effectively differentiate HCC from benign liver diseases (BLD) and early-stage HCC from cirrhosis, addressing limitations of conventional EV detection and demonstrating the potential for rapid cancer screening.

The utilization of resonant‐unit‐based metamaterials in beam control and compact stealth applications is inherently limited by the strong correlation between unit in‐plane dimensions and reflection characteristics. Therefore, this study proposes a resonator‐free metamaterial based on ferromagnetic dielectric that decouples the amplitude and phase by regulating interface interference, thereby achieving phase modulation independent of the in‐plane dimensions of the FD units. With the introduction of a constant phase gradient and tuning of unit dimensions, reflected waves can be deflected or even converted into surface waves that propagate along the metamaterial interface. This enables a novel electromagnetic loss mechanism wherein the reflected energy undergoes mandatory attenuation by horizontally propagating within the lossy ferromagnetic dielectric. Simulations and experiments are conducted to prove this phenomenon, yielding an improvement of 36.64% in the average power loss density, a minimum reflection loss of −52 dB. Further, the efficacy of ferromagnetic dielectric units is validated for compact stealth cloaks, and a conformal curved stealth strategy that requires only unit dimension tuning to achieve scattering‐field camouflage for arbitrarily shaped targets is proposed. Given its resonator‐independent operation, the proposed metamaterial exhibits miniaturization advantages of cross‐scale downsizing (in‐plane dimension < λ/12)—a critical advancement for compact electromagnetic defense systems.

Metasurfaces refer to the sub-wavelength nanostructures that are capable of manipulating the amplitude, phase, polarization, and other characteristics of light, to enable diverse applications across the ultraviolet, visible, infrared, and terahertz spectra. In this review paper, we aim to provide an introductory note on metasurfaces, from fundamentals and design methods to applications, such as biosensing, environmental monitoring, metalenses, optical cloaking, electromagnetic scattering, structural color, miniaturized devices, and others. Moreover, we also identify the key challenges and limitations of metasurfaces, such as fabrication, integration, optimization, tuning, signal processing, and analysis, and suggest possible directions and solutions for future research. At last, we envision several emerging and promising trends for metasurfaces, such as new materials and structures, new phenomena and mechanisms, machine learning, and artificial intelligence techniques. The review is expected to inspire future development in this exciting and rapidly evolving field of metasurface devices.

The performance breakthroughs of some stealth materials have benefited from incorporating biomimetic concepts, and the design ideas of wave-absorbing metamaterials have been greatly broadened. However, stealth materials developed based on a single biological structure still have limitations regarding overall performance and design freedom. Herein, a dual-structure element combination model with a butterfly-wing porous structure and moth-eye raised structure arranged in an orderly manner is established. Carbonyl iron and polyurethane are mixed as wave absorbents, and the model is utilized to make a biomimetic metamaterial (CSMA), which has an absorption rate of more than 90% at 6.07–18 GHz, achieving broadband effective absorption. It has been verified that the two biostructures designed after an ordered arrangement show synergistic effects in the combined model, and the cooperation between the structures induces the formation of current vector vortices, which are able to induce microwave losses to broaden the effective absorbing bandwidth. Further, the model has the combined application performance of polarization insensitivity, strong stability of oblique incidence, and low bistatic RCS. Such a thought based on the combination of multiple components provides an effective strategy for the design of broadband-absorbing metamaterials.

Empowering nanophotonic devices via artificial intelligence (AI) has revolutionized both scientific research methodologies and engineering practices, addressing critical challenges in the design and optimization of complex systems. Traditional methods for developing nanophotonic devices are often constrained by the high dimensionality of design spaces and computational inefficiencies. This review highlights how AI-driven techniques provide transformative solutions by enabling the efficient exploration of vast design spaces, optimizing intricate parameter systems, and predicting the performance of advanced nanophotonic materials and devices with high accuracy. By bridging the gap between computational complexity and practical implementation, AI accelerates the discovery of novel nanophotonic functionalities. Furthermore, we delve into emerging domains, such as diffractive neural networks and quantum machine learning, emphasizing their potential to exploit photonic properties for innovative strategies. The review also examines AI's applications in advanced engineering areas, e.g., optical image recognition, showcasing its role in addressing complex challenges in device integration. By facilitating the development of highly efficient, compact optical devices, these AI-powered methodologies are paving the way for next-generation nanophotonic systems with enhanced functionalities and broader applications.

Magnetic loss in high‐temperature microwave absorbers typically decreases sharply with rising temperature, limited by the Curie temperature ( T C ) . Conventional alloys rely on high‐proportion additions of magnetic metals to enhance T C ; however, this approach increases electrical conductivity and causes impedance mismatch under high temperatures. In this study, a synergistic strategy for high‐entropy alloy (HEA) powders is presented that reduces reliance on high magnetic metal content. This approach involves the formation of magnetic element‐rich nanoparticles (MENPs) and the incorporation of the rare‐earth element Gd, which effectively stabilizes ferromagnetic ordering at high temperatures. The FeCoCrAlGd 0.2 HEA exhibits a T C of 947 °C and retains a saturation magnetization of 102 emu g −1 from room temperature up to 700 °C. These intrinsic magnetic properties enable stable magnetic moment precession under high‐temperature electromagnetic fields. Notably, the FeCoCrAlGd 0.2 alloy demonstrates significant magnetic loss even at 400 °C. Density functional theory (DFT) calculations indicate that the 3d and 4f electron bands in MENPs are closely aligned in energy levels, inducing strong exchange interactions and thermally stable ferromagnetic ordering in MENPs. This work presents a novel design strategy and research approach for magnetic HEAs, identifying a promising material for high‐temperature microwave absorbers.

Overstretched honeycomb absorbers (OH) exhibit inherent mechanical flexibility but suffer from significant polarization-sensitive response due to structural anisotropy, fundamentally limiting concurrent broadband absorption under orthogonal polarizations. Systematic symmetry analysis and electromagnetic field simulations elucidate the origin of polarization anisotropy in OH. To mitigate this limitation, a gradient-designed doublelayer overstretched honeycomb (DH) structure was fabricated. Aramid paper honeycomb substrates were impregnated with carbon black/phenolic resin composites through differential processing cycles: single impregnation for the top layer and four cycles for the bottom layer. This spatial gradient architecture suppresses polarization anisotropy by decoupling impedance matching (primarily governed by the top layer) and dielectric loss (dominated by the bottom layer). The optimized DH20.1.4 sample achieves polarization-insensitive broadband absorption, demonstrating effective bandwidths (reflection loss < -10 dB) of 5.9-18 GHz and 6.9-18 GHz for orthogonal electric field orientations, with peak absorption values of -41.7 dB and -43.6 dB, respectively. Furthermore, the absorber maintains angular stability up to 60 degrees incidence, exhibits significant radar cross section (RCS) reduction, and preserves functionality under mechanical deformation. This work establishes gradient-engineered honeycombs as high-performance microwave absorbers delivering concurrent polarization insensitivity, broadband operation, and environmental adaptability.

Tea is the second most popular beverage globally after water. Identifying fresh tea leaves is significant to ensure product quality in tea industry, since they are often difficult to distinguish via natural colors and morphological features. Traditional component analysis methods of fresh tea leaves are destructive and costly, while emerging non-destructive techniques typically rely on large-scale and time-consuming instruments, limiting their applicability in on-site scenarios. In this study, we propose a portable identification system for fresh tea leaves, which is the combination of a field-spectrometer and an advanced deep learning (DL) architecture for spectral classification. A custom-designed leaf fixation apparatus is introduced to ensure stable and reliable spectra acquisition, enabling robust data acquisition for DL model training in field environments. The DL model adopts a Transformer architecture enhanced by principal component analysis, which not only reduces 99.4 % of training parameters compared to conventional DL methods, but also elevates tea leaf classification accuracy. As a proof of concept, we apply the proposed detection system to identify the Wuyi Rock Tea, a world-renowned type of Chinese tea with the unique flavour profile and rich cultural heritage. Our approach achieves classification accuracies of 99.15 % for tea variety and 100 % for tea quality, outperforming several existing methods. This study provides a convenient solution for rapid identification of fresh tea leaves, and will also highlight the broader potential of our scheme on other leaf-identification-based applications.

Silicon photodetectors are highly desirable for their CMOS compatibility, low cost, and fast response speed. However, their applications in the infrared (IR) regime are inherently limited by the intrinsic bandgap of silicon, which limits the detection wavelengths to being below 1.1 μm. Although several methods have been developed to extend silicon photodetectors further in the IR range, these approaches often introduce additional challenges. Here, we present an approach to overcome these limitations by integrating disordered metasurfaces with upconversion nanoparticles, enabling IR detection by silicon photodetectors. The disordered design consisting of hybrid Mie-plasmonic cavities can enhance both the near-field localization and wide-band light absorption. The measured responsivity of the disordered element for 1550-nm laser is 0.22 A/W at room temperature, corresponding to an external quantum efficiency of 17.6%. Our design not only enhances the photocurrent performance, but also extends the working wavelength of silicon photodetectors to IR spectrum applications.

To address the challenges of dielectric constant surge and impedance mismatch caused by particle agglomeration in highly filled carbonyl iron/polyurethane (CIP/PU) composite films, we developed a surface modification strategy for CIP using a KH550 silane coupling agent (0-2 wt %). Inspired by the adhesive pads and climbing mechanisms of climbing plants, this bioinspired interface design establishes a CIP-PU ″molecular bridge″, enhancing interfacial compatibility. At 1 wt % KH550, CIP dispersion is markedly improved, disrupting the three-dimensional conductive network. This yields a 44% increase in tensile strength (10.104 MPa) and a 36.8% reduction in dielectric constant. Furthermore, the film achieves a minimum reflection loss of -17.09 dB and an effective absorption bandwidth (RL ≤ -10 dB) of 6.7 GHz (10.3-17 GHz) at a thickness of only 1 mm. This bionic interface engineering strategy overcomes the longstanding bottleneck in synchronizing impedance matching and mechanical performance in high-filler-content (85 wt %) composite absorbers.

With the development of electromagnetic detection technology, higher requirements are put forward for waveabsorbing materials. The honeycomb microwave absorber has the advantages of low cost, light weight, strong wave-absorbing ability and wide wave-absorbing band. However, it has a poor absorption capacity in the S and C bands and usually requires a large thickness to achieve a strong absorption capacity. In this study, we use a realistic and symmetric model to reveal the relationship between the absorption/reflection properties of honeycomb wave-absorbing materials and the absorber content and the number of impregnations. The surface of the honeycomb is covered with fiberglass board to reduce the reflectivity in the target band. Simultaneous controlled tuning of the 5 GHz and 12-14 GHz absorption peaks. And a dual-response synergistic gradient honeycomb sandwich structure (GHSS) is constructed using a low-reflective high-frequency response matching layer and a high-absorption low-frequency response absorber layer and adjusting the skin thickness to reduce the reflectivity. The gradient design improves the impedance matching between the honeycomb structure and air to enhance the absorption, and the dual-response synergy broadens the absorption band. The GHSS has comprehensive and effective absorption coverage in 2-18 GHz, with an average reflection loss of -18.8 dB. Average absorption up to -18.0 dB in S and C bands. The prepared lightweight and low-thickness composite honeycomb are expected to have a good application prospect in the field of electromagnetic wave absorption.

In recent years, achieving ultra-wideband electromagnetic absorption has emerged as a critical challenge in confronting advanced broadband electromagnetic detection technologies. This capability is essential for effectively countering sophisticated radar systems. In this study, we present a novel multilayer metamaterial absorber that integrates an FR4 transmission layer, a periodic gradient dielectric structure designed for resonant impedance matching, and a magnetic skin layer for enhanced energy dissipation. By employing asymptotic gradients in both structure and composition, this design achieves dual-gradient electromagnetic parameter modulation, enabling efficient absorption across the X, Ku, and K bands (8.6–26.4 GHz) with a total thickness of 3.5 mm (effective thickness: 2 mm) and a density that is one-third that of conventional magnetic metamaterials. The proposed absorber demonstrates polarization insensitivity, stability across wide incident angles (up to 60°), and an absorption efficiency of 94%, as confirmed by full-wave simulations and experimental validation. Moreover, the fiber-reinforced hierarchical structure addresses the traditional trade-off between broadband absorption performance and mechanical load-bearing capacity.