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.

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.

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.

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.

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.