Indirect X-ray imaging is an indispensable non-destructive testing technology in the medical and industrial fields. However, its quality is restricted by the light output and optical crosstalk of the scintillation screens. Herein, we report a series of hafnium-based organic-inorganic metal halides (OIMHs) with regulated organic cation chain lengths. The thermally activated delayed fluorescence (TADF) is demonstrated in these materials by their anti-thermal quenching luminescence characteristics, fitting of temperature-dependent photoluminescence decay lifetime, and a series of theoretical calculations. These represent non-luminescent triplet excitons that can be emitted by reverse intersystem crossing (RISC), thus improving the utilization rate of excitons and increasing the light output of scintillators. The highest performance of our reported hafnium-based OIMHS shows a light yield of 56563.31±1250 photons/MeV and detection limit of 23.86 nGy_air/s. Moreover, the optical crosstalk is suppressed by developing silicon array scintillation screens, and an ultra-high spatial resolution of 31.41 lp/mm is achieved. These results offer insights into the luminescent mechanism of hafnium-based OIMHs and initiate a new paradigm for exciton-optical co-management for high-quality X-ray imaging.

Investigating the composition ratio of ethanol–water molecular clusters in air, which are responsible for anisotropic behavior, is a significant challenge, primarily owing to the difficulty of detecting Rayleigh scattering, an inherently weak signal highly susceptible to external interference. This study overcame these limitations by integrating laser diffraction focusing with artificial neural networks. We demonstrate a fully non-contact sensing system that circumvents the direct measurement of difficult-to-distinguish Rayleigh scattering signals. Our approach infers ethanol content by utilizing a self-fabricated multi-layered graphene Fresnel lens. An analysis of gaseous ethanol content in the 0.01%–0.1% range revealed that while a wavelength of 405 nm exhibited high sensitivity with up to a 7% intensity change corresponding to ethanol content, a wavelength of 638 nm provided superior stability for deep-learning analysis as its intensity parameter remained fixed. Ultimately, by combining the 638 nm laser with our self-developed self-aware assembly network model, we successfully inferred ethanol content with an R² of 0.884, even under varied power conditions. This study creates a new path for recognizing weak Rayleigh scattering signals that were previously difficult to measure and may facilitate the development of rapid and durable sensing systems, such as for the non-invasive diagnosis of gas molecules.

Unveiling the vacancy oscillation mode in amorphous binary oxides films at nanoscale and its impact on ionic conductivity and conductivity spectra is vital to explore the tightly intertwined connection between reversible oxygen migration and stabilizing and controlling the ferroelectric behavior, to complement traditional ferroelectric doped-HfO₂ materials. Using terahertz time-domain spectroscopy (THz-TDS), infrared reflection spectra, and density functional theory (DFT) calculations, we investigate the optical absorption and reflection spectra of crystalline and amorphous ZrO₂ thin film by varying oxygen vacancy concentrations. Experimental results show that oxygen vacancy migration rather than intrinsic paraelectric nature in films significantly affect the conductivity and polarization behavior of ZrO₂ thin film. Notably, except for the phonon modes induce distinct absorption peaks around 11 THz, additional absorption peaks are observed in the 1–2 THz range, which are caused by localized states originated from the oxygen vacancies, supported by DFT calculations. Temperature-dependent ion migration behavior further confirms the role of vacancy oscillation modes in ionic conductivity. DFT calculations additionally reveal how oxygen vacancies alter infrared absorption and optical modes, leading to a redshift in existing absorption peaks or the introduction of new peaks. Our findings unambiguously clarify the oxygen voltammetry characteristics in amorphous ferroelectric binary oxides films. Furthermore, the strategic deployment of amorphous binary oxides films enables the use of low-temperature atomic layer deposition (ALD) growing process, effectively alleviating routing congestion of ferroelectric oxides and offering additional design flexibility for future memory devices with ultra-low effective oxide thickness (EOT) and low thermal budget, and providing alternative technological routes that are poised to propel the development of next-generation more compact ferroelectric devices for advanced process nodes.

Calculus equations are fundamental mathematical tools, whose numerical solution is crucial. Existing solvers with optical analog computing struggle to simultaneously integrate programmability and parallel processing, thus constraining computational speed and density. Herein, we propose a reconfigurable all-optical platform capable of solving variable-coefficient first-order ordinary differential equations in parallel. We utilize the electrically tunable liquid crystals (LCs) as computing kernels to address these equations. The solver's applicability to canonical scientific problems, such as heat conduction and resistor-capacitor circuit dynamics, is further showcased with simultaneous solving of 158 equations with only one single forward propagation of light. Experimental results confirm the efficacy of the platform in solving equations in an ultra-fast, reconfigurable, broadband, and parallel manner.

As the next-generation human–computer interface, augmented reality display technology has been gradually popular, depending on advances in system architectures. However, existing waveguide and passive retinal projection display solutions are unable to combine their respective advantages to address increasingly demanding performance requirements. Herein, we propose an active retinal projection display (A-RPD) concept based on collimated active-matrix microdisplay panels. By deriving and validating the collimation-dependent depth of focus, the optimization direction of this architecture is emphasized. Through the direct integration of pixel-to-pixel collimators on the microdisplay panels, an A-RPD prototype featuring a balanced design and performance has been successfully constructed. It enables clear retinal imaging from 40 cm to 160 cm, which significantly surpasses that of the uncollimated microdisplay. The proposed active retinal projection architecture retains the advantages of retinal projection while simplifying the architecture. This work highlights its importance and superiority and provides foundations for its further expansion in practical applications.

Type 2 diabetes mellitus (T2DM) significantly elevates fracture risk, a severe complication often underestimated by conventional bone mineral density (BMD) assessments. Here, we applied label-free multimodal nonlinear optical (NLO) imaging with AI-powered texture feature analysis to characterize T2DM-related bone quality alterations. Our results identified aberrant spatial protein distribution, characterized by increased homogeneity and reduced contrast, as a distinctive pathological feature in T2DM bone. The alterations in spatial distribution were also observed in hydroxyapatite (HA) and autofluorescent metabolites. A K-nearest neighbor (KNN) model, trained on fused texture features from these three components, achieved a superior classification accuracy of 93.56% in distinguishing T2DM-related bone tissues, markedly outperforming single-component models (~70%). This demonstrated that fused multi-component spatial distribution features offer enhanced discriminative power for quantifying T2DM-associated pathological changes. Collectively, aberrant molecular spatial distribution, particularly of protein, represents a potentially unappreciated indicator of diabetic bone quality alterations. Integrating multimodal NLO imaging with explainable AI offers a novel approach for unraveling the mechanistic underpinnings of complex pathological alterations, which not only overcomes the limitations of conventional biomarker assessment but also establishes a powerful framework for discovering new pathological targets.

Dissipative Kerr solitons (DKSs) generated in optical microresonators have shown considerable promise across multiple applications, particularly in metrology and spectroscopy. Multistable solitons enable on-chip single-cavity dual-comb generation, and can be excited via multicolor pumping in a microresonator featuring high-quality factor and strong nonlinearity, whose dispersion profile governs the soliton velocity mismatch. However, real-time characterization of complex multistable soliton dynamics remains highly challenging due to their transient nature, large bandwidth, and high repetition rate. In this work, we generate multistable soliton dynamics using a dual-pump scheme targeting distinct cavity modes and report, for the first time, their simultaneous time- and spectral-domain characterization via a chirped coherent detection scheme. These multistable solitons are measured at different carrier-envelope offset frequencies, allowing for the observation of soliton switching and annihilation processes during pump detuning. Furthermore, the gain transfer mechanisms underpinning the influence of pump detuning on soliton dynamics are investigated. This study not only deepens our understanding of complex soliton interactions within optical microresonators but also supports enhanced control and utilization of single-cavity dual-comb sources.

Thermal infrared (IR) detectors represent a crucial technology for various applications, yet achieving high performance without cooling remains challenging. Here, we demonstrate high-performance broadband IR photodetectors by integrating single-walled carbon nanotubes (SWCNTs) with a ferroelectric substrate, leveraging the pyroelectric effect for enhanced photodetection. Using aerosol chemical vapor deposition and capillary transfer techniques, we fabricate sparse SWCNT films on z-cut LiNbO₃ surfaces to create pyroelectrically gated field-effect transistors. The devices exhibit remarkable responsivity across the IR spectrum, with semiconducting channels achieving maximum relative responsivities reaching nearly 100 %/μW at 1550 nm. Our optimized SWCNT networks demonstrate exceptional specific detectivities of 1.7 × 10¹⁰ cm$ {\sqrt{{{{\text{Hz}}}}}/{{{\text{W}}}}}$ at 1550 nm and 1.4 × 10¹⁰ cm$ \sqrt{{{{\text{Hz}}}}}/{{{\text{W}}}}$ at 9.3 μm, surpassing graphene-based alternatives by several orders of magnitude and approaching theoretical limits. These results establish SWCNT-based pyroelectric photodetectors as promising candidates for room-temperature IR detection, eliminating the conventional requirement for cooling.

Achieving realistic depth perception in augmented reality (AR) displays remains a central challenge due to the reliance on single focal planes. In the recent work published in Opto-Electronic Science, researchers from Beijing Institute of Technology and their collaborators report a hybrid metasurface-freeform optical architecture that enables simultaneous multi-focal plane display within a compact, solid-state system. By combining polarization-multiplexed metasurfaces with freeform optics through a joint design framework, the system generates multiple depth cues without time multiplexing and remains compatible with practical display hardware. This work highlights a promising route toward cross-scale optical co-design for realistic near-eye display systems.

The convergence of artificial intelligence (AI) and metaphotonics is creating a new paradigm for controlling light-matter interactions. The synergy of AI's ability to learn complex relationships in multidimensional data and provide ultra-fast inference with the capacity of metaphotonics to engineer optical properties not found in nature is unlocking a new era in computational design, real-time control, and fully automated optical systems. This review provides a comprehensive overview of state-of-the-art AI-driven approaches for metaphotonic systems. We focus on the solutions to real-world problems in accelerating metaphotonic simulations and inverse design, optical data characterization, and the development of fully integrated end-to-end AI-assisted metaphotonic systems. Finally, we provide our perspectives on the future research directions and emerging opportunities at the rapidly evolving intersection of metaphotonics and AI.