Light-Science & Applications最新文献

Recent advancements show the potential of cascaded metalenses in near-eye display applications, achieving performance that rivals traditional eyepiece systems. By leveraging the human pupil as an aperture and taking into account practical factors such as eye relief, pupil size, and display dimensions, this approach suggests a bright future for the incorporation of meta-optics in cutting-edge near-eye display technologies.

A major challenge in neuroscience is visualizing the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features but suffers from staining variability, tissue damage, and distortion, which impedes accurate 3D reconstructions. The emerging label-free serial sectioning optical coherence tomography (S-OCT) technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical features. Here, we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining (DS) model. This enhanced imaging modality integrates high-throughput 3D imaging, low sample variability and high interpretability, making it suitable for 3D histology studies. We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver staining. We demonstrate DS on various human cerebral cortex samples, achieving consistent staining quality and enhancing contrast across cortical layer boundaries. Additionally, we show that DS preserves geometry in 3D on cubic-centimeter tissue blocks, allowing for visualization of meso-scale vessel networks in the white matter. We believe that our technique has the potential for high-throughput, multiscale imaging of brain tissues and may facilitate studies of brain structures.

Multifunctional materials have attracted tremendous attention in intelligent and interactive devices. However, achieving multi-dimensional sensing capabilities with the same perovskite quantum dot (PQD) material is still in its infancy, with some considering it currently challenging and even unattainable. Drawing inspiration from neurons, a novel multifunctional CsPbBr₃/PDMS nanosphere is devised to sense humidity, temperature, and pressure simultaneously with unique interactive responses. The carefully engineered polydimethylsiloxane (PDMS) shell enables the reversible activity of the core CsPbBr₃, serving a dual role similar to dendrites in conveying and evaluating external stimuli with high sensitivity. Molecular dynamics analysis reveals that the PDMS shell with proper pore density enhances the conductivity in water and heat, imparting CsPbBr₃ with sensitive but reversible properties. By tailoring the crosslinking density of the PDMS shell, nanospheres can surprisingly show customized sensitivity and reversible responses to different level of stimuli, achieving over 95% accuracy in multi-dimensional and wide-range sensing. The regular pressure-sensitive property, discovered for the first time, is attributed to the regular morphology of the nanosphere, the inherent low rigidity of the PDMS shell, and the uniform distribution of the CsPbBr₃ core material in combination. This study breaks away from conventional design paradigms of perovskite core-shell materials by customizing the cross-linked density of the shell material. The reversible response mechanism of nanospheres with gradient shell density is deeply explored in response to environmental stimuli, which offers fresh insights into multi-dimensional sensing and interactive display applications.

Solution-processed quantum dot light-emitting diodes (QLEDs) hold great potential as competitive candidates for display and lighting applications. However, the serious energy disorder between the quantum dots (QDs) and hole transport layer (HTL) makes it challenging to achieve high-performance devices at lower voltage ranges. Here, we introduce “giant” fully alloy CdZnSe/ZnSeS core/shell QDs (size ~ 19 nm) as the emitting layer to build high-efficient and stable QLEDs. The synthesized CdZnSe-based QDs reveal a decreased ground-state band splitting, shallow valence band maximum, and improved quasi-Fermi level splitting, which effectively flatten the energy landscape between the QD layer and hole transport layer. The higher electron concentration and accelerated hole injection significantly promote the carrier radiative recombination dynamics. Consequently, CdZnSe-based device exhibits a high power conversion efficiency (PCE) of 27.3% and an ultra-low efficiency roll-off, with a high external quantum efficiency (EQE) exceeding 25% over a wide range of low driving voltages (1.8-3.0 V) and low heat generation. The record-high luminance levels of 1,400 and 8,600 cd m^-2 are achieved at bandgap voltages of 100% and 120%, respectively. Meanwhile, These LEDs show an unprecedented operation lifetime T₉₅ (time for the luminance to decrease to 95%) of 72,968 h at 1,000 cd m^-2. Our work points to a novel path to flatten energy landscape at the QD-related interface for solution-processed photoelectronic devices.

Large-scale quantum networks require dynamic and resource-efficient solutions to reduce system complexity with maintained security and performance to support growing number of users over large distances. Current encoding schemes including time-bin, polarization, and orbital angular momentum, suffer from the lack of reconfigurability and thus scalability issues. Here, we demonstrate the first-time implementation of frequency-bin-encoded entanglement-based quantum key distribution and a reconfigurable distribution of entanglement using frequency-bin encoding. Specifically, we demonstrate a novel scalable frequency-bin basis analyzer module that allows for a passive random basis selection as a crucial step in quantum protocols, and importantly equips each user with a single detector rather than four detectors. This minimizes massively the resource overhead, reduces the dark count contribution, vulnerability to detector side-channel attacks, and the detector imbalance, hence providing an enhanced security. Our approach offers an adaptive frequency-multiplexing capability to increase the number of channels without hardware overhead, enabling increased secret key rate and reconfigurable multi-user operations. In perspective, our approach enables dynamic resource-minimized quantum key distribution among multiple users across diverse network topologies, and facilitates scalability to large-scale quantum networks.

Vortex beams are currently drawing a great deal of interest, from fundamental research to several promising applications. While their generation in bulky optical devices limits their use in integrated complex systems, metasurfaces have recently proven successful in creating optical vortices, especially in the linear regime. In the nonlinear domain, of strategic importance for the future of classical and quantum information, to date orbital angular momentum has only been created in qualitative ways, without discussing discrepancies between design and experimental results. Here, we demonstrate and analyze the generation of high-purity second harmonic (SH) optical vortices via dielectric meta-holograms. Through full-wave simulations and a proper fabrication protocol, we achieve efficient frequency doubling of an unstructured pump beam into SH vortices with topological charges from 1 to 10. Interferometric and modal-purity measurements confirm the generation of high-quality SH vortices with minimal deviations from the intended design thanks to a quasi-local control over the SH phase. Through systematic comparisons between experimental data and semi-analytical calculations, we also provide a clear insight into the occurrence of ghost vortices in the metasurface-generated harmonic beams, highlighting the importance of simple designs that can be readily transposed into fabricated devices with high fidelity. Our findings underscore the potential of nonlinear dielectric metasurfaces for versatile structured-light generation and manipulation, paving the way for future developments in integrated photonic systems.

Light manipulation and control are essential in various contemporary technologies, and as these technologies evolve, the demand for miniaturized optical components increases. Planar-lens technologies, such as metasurfaces and diffractive optical elements, have gained attention in recent years for their potential to dramatically reduce the thickness of traditional refractive optical systems. However, their fabrication, particularly for visible wavelengths, involves complex and costly processes, such as high-resolution lithography and dry-etching, which has limited their availability. In this study, we present a simplified method for fabricating visible Fresnel zone plate (FZP) planar lenses, a type of diffractive optical element, using an i-line stepper and a special photoresist (color resist) that only necessitates coating, exposure, and development, eliminating the need for etching or other post-processing steps. We fabricated visible FZP lens patterns using conventional photolithography equipment on 8-inch silica glass wafers, and demonstrated focusing of 550 nm light to a diameter of 1.1 μm with a focusing efficiency of 7.2%. Numerical simulations showed excellent agreement with experimental results, confirming the high precision and designability of our method. Our lenses were also able to image objects with features down to 1.1 μm, showcasing their potential for practical applications in imaging. Our method is a cost-effective, simple, and scalable solution for mass production of planar lenses and other optical components operating in the visible region. It enables the development of advanced, miniaturized optical systems to meet modern technology demand, making it a valuable contribution to optical component manufacturing.

We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through nonresonant optical pumping. The network employs a binary framework, where each neuron, facilitated by the spatial coherence of pairwise coupled condensates, performs binary operations. This coherence, emerging from the ballistic propagation of polaritons, ensures efficient, network-wide communication. The binary neuron switching mechanism, driven by the nonlinear repulsion through the excitonic component of polaritons, offers computational efficiency and scalability advantages over continuous weight neural networks. Our network enables parallel processing, enhancing computational speed compared to sequential or pulse-coded binary systems. The system’s performance was evaluated using diverse datasets, including the MNIST dataset for image recognition and the Speech Commands dataset for voice recognition tasks. In both scenarios, the proposed system demonstrates the potential to outperform existing polaritonic neuromorphic systems. For image recognition, this is evidenced by an impressive predicted classification accuracy of up to 97.5%. In voice recognition, the system achieved a classification accuracy of about 68% for the ten-class subset, surpassing the performance of conventional benchmark, the Hidden Markov Model with Gaussian Mixture Model.

The rapid development of internet of things (IoT) urgently needs edge miniaturized computing devices with high efficiency and low-power consumption. In-sensor computing has emerged as a promising technology to enable in-situ data processing within the sensor array. Here, we report an optoelectronic array for in-sensor computing by integrating photodiodes (PDs) with resistive random-access memories (RRAMs). The PD-RRAM unit cell exhibits reconfigurable optoelectronic output and photo-responsivity by programming RRAMs into different resistance states. Furthermore, a 3 × 3 PD-RRAM array is fabricated to demonstrate optical image recognition, achieving a universal architecture with ultralow latency and low power consumption. This study highlights the great potential of the PD-RRAM optoelectronic array as an energy-efficient in-sensor computing primitive for future IoT applications.

In the domain of spectroscopy, miniaturization efforts often face significant challenges, particularly in achieving high spectral resolution and precise construction. Here, we introduce a computational spectrometer powered by a nonlinear photonic memristor with a WSe₂ homojunction. This approach overcomes traditional limitations, such as constrained Fermi level tunability, persistent dark current, and limited photoresponse dimensionality through dynamic energy band modulation driven by palladium (Pd) ion migration. The critical role of Pd ion migration is thoroughly supported by first-principles calculations, numerical simulations, and experimental verification, demonstrating its effectiveness in enhancing device performance. Additionally, we integrate this dynamic modulation with a specialized nonlinear neural network tailored to address the memristor’s inherent nonlinear photoresponse. This combination enables our spectrometer to achieve an exceptional peak wavelength accuracy of 0.18 nm and a spectral resolution of 2 nm within the 630–640 nm range. This development marks a significant advancement in the creation of compact, high-efficiency spectroscopic instruments and offers a versatile platform for applications across diverse material systems.