Light-Science & Applications最新文献

We propose and validate a novel optical semantic transmission scheme using multimode fiber (MMF). By leveraging the frequency sensitivity of intermodal dispersion in MMFs, we achieve high-dimensional semantic encoding and decoding in the frequency domain. Our system maps symbols to 128 distinct frequencies spaced at 600 kHz intervals, demonstrating a seven-fold increase in capacity compared to conventional communication encoding. We further enhance spectral efficiency by implementing 4-level pulse amplitude modulation (PAM-4), achieving 9.12 bits/s/Hz without decoding errors. Additionally, we explore the application of this system for sentiment analysis using the IMDb movie review dataset. By encoding semantically similar symbols to adjacent frequencies, the system’s noise tolerance is effectively improved, facilitating accurate sentiment analysis. This work highlights the potential of MMF-based semantic communication to enhance both capacity and robustness in optical communication systems, offering promising applications in bandwidth-constrained and noisy environments.

A stacked metamaterial MEMS (meta-MEMS) chip is proposed, which can perfectly absorb electromagnetic waves, convert them into mechanical energy, drive movement of the optical micro-reflectors array, and detect millimeter waves. It is equivalent to using visible light to image a millimeter wave. The meta-MEMS adopts the design of upper and lower chip separation and then stacking to achieve the “dielectric-resonant-air-ground” structure, reduce the thickness of the metamaterial and MEMS structures, and improve the performance of millimeter wave imaging. For verification, we designed and prepared a 94 GHz meta-MEMS focal plane array chip, in which the sum of the thickness of the metamaterial and MEMS structures is only 1/2500 wavelength, the pixel size is less than 1/3 wavelength, but the absorption rate is as high as 99.8%. Moreover, a light readout module was constructed to test the millimeter wave imaging performance. The results show that the response speed can reach 144 Hz and the lens-less imaging resolution is 1.5 mm.

Multi-photon polymerization is a well-established, yet actively developing, additive manufacturing technique for 3D printing on the micro/nanoscale. Like all additive manufacturing techniques, determining the process parameters necessary to achieve dimensional accuracy for a structure 3D printed using this method is not always straightforward and can require time-consuming experimentation. In this work, an active machine learning based framework is presented for determining optimal process parameters for the recently developed, high-speed, layer-by-layer continuous projection 3D printing process. The proposed active learning framework uses Bayesian optimization to inform optimal experimentation in order to adaptively collect the most informative data for effective training of a Gaussian-process-regression-based machine learning model. This model then serves as a surrogate for the manufacturing process: predicting optimal process parameters for achieving a target geometry, e.g., the 2D geometry of each printed layer. Three representative 2D shapes at three different scales are used as test cases. In each case, the active learning framework improves the geometric accuracy, with drastic reductions of the errors to within the measurement accuracy in just four iterations of the Bayesian optimization using only a few hundred of total training data. The case studies indicate that the active learning framework developed in this work can be broadly applied to other additive manufacturing processes to increase accuracy with significantly reduced experimental data collection effort for optimization.

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Dear Editor,

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.