Opto-Electronic Advances (光电进展)•2026•DOI: 10.29026/oea.2026.250216
The intrinsic trade-off between depth-of-focus and lateral resolution in conventional optical systems constrains three-dimensional imaging in compact form factors. This work demonstrates an electrically tunable dual-mode metalens that integrates hydrogenated amorphous silicon (a-Si:H) meta-atoms with a liquid crystal (LC) modulator to independently manipulate left- and right-circularly polarized (LCP/RCP) light at 635 nm. Under LCP illumination, the metalens generates a rotating double-helix point spread function (PSF) encoding depth via rotation angle; under RCP illumination, it produces an extended depth-of-focus with a narrow PSF for high-resolution imaging. Propagation and geometric phases were co-optimized via rigorous coupled-wave analysis (RCWA), yielding high transmittance and precise phase control. Experimental characterization confirmed near-diffraction-limited lateral and axial resolutions. The integrated LC cell enables millisecond-scale polarization switching between depth-sensitive and high-resolution modes. Depth extraction was validated by correlating rotation angles of dual-image focal spots under mixed-polarization illumination, with axial displacements of Δz1 = 30.5 μm, Δz2 = 0 μm, and Δz3 = −48.3 μm corresponding to rotation angles β = −25.6°, 0°, and 16.9°, respectively. Depth-resolved imaging of a rubber-tree leaf, skeletal-muscle cross-section, and live planarian retrieved color-coded depth maps, demonstrating efficacy on complex biological tissues. This polarization-driven platform offers a compact solution for biomedical imaging, three-dimensional sensing, and adaptive optics.
Nano-Micro Letters•2025•DOI: 10.1007/s40820-025-01742-z
The robust respective formations of a solid electrolyte interphase (SEI) and pillar at the surfaces of hard carbon and O3-type positive electrodes are the consequences of integrating LiPF6 salt into a sodium-ion battery electrolyte that considerably strengthens both interfaces of positive and negative electrodes. The improvement of cycle performances due to the formation of highly passivating SEI on the hard carbon electrode is induced by the alternated solvation structure following the addition of Li salt, which inhibits sodium-ion and electron leakage from further electrolyte decomposition. The SEI with incorporated Li is less soluble than Na-based SEI, and the passivation ability of the initially formed SEI can thus be well preserved. Conversely, the gas evolution caused by oxygen release is reduced considerably by the marginal surface intercalation of Li ions at the surface of the O3-positive electrode. Additionally, the LiF layer that forms on the O3 surface diminishes additional deterioration of the electrolyte after formation. Compared with the fluoroethylene carbonate additive that is typically applied, a simultaneously strengthened interface yields major improvements in capacity retention.
Nano-Micro Letters•2025•DOI: 10.1007/s40820-024-01642-8
Amidst the ever-growing interest in high-mass-loading Li battery electrodes, a persistent challenge has been the insufficient continuity of their ion/electron conduction pathways. Here, we propose cellulose elementary fibrils (CEFs) as a class of deagglomerated binder for high-mass-loading electrodes. Derived from natural wood, CEF represents the most fundamental unit of cellulose with nanoscale diameter. The preparation of the CEFs involves the modulation of intermolecular hydrogen bonding by the treatment with a proton acceptor and a hydrotropic agent. This elementary deagglomeration of the cellulose fibers increases surface area and anionic charge density, thus promoting uniform dispersion with carbon conductive additives and suppressing interfacial side reactions at electrodes. Consequently, a homogeneous redox reaction is achieved throughout the electrodes. The resulting CEF-based cathode (overlithiated layered oxide (OLO) is chosen as a benchmark electrode active material) exhibits a high areal-mass-loading (50 mg cm–2, equivalent to an areal capacity of 12.5 mAh cm–2) and a high specific energy density (445.4 Wh kg–1) of a cell, which far exceeds those of previously reported OLO cathodes. This study highlights the viability of the deagglomerated binder in enabling sustainable high-mass-loading electrodes that are difficult to achieve with conventional synthetic polymer binders.
Nano-Micro Letters•2025•DOI: 10.1007/s40820-024-01599-8
Recent advancements in passive wireless sensor technology have significantly extended the application scope of sensing, particularly in challenging environments for monitoring industry and healthcare applications. These systems are equipped with battery-free operation, wireless connectivity, and are designed to be both miniaturized and lightweight. Such features enable the safe, real-time monitoring of industrial environments and support high-precision physiological measurements in confined internal body spaces and on wearable epidermal devices. Despite the exploration into diverse application environments, the development of a systematic and comprehensive research framework for system architecture remains elusive, which hampers further optimization of these systems. This review, therefore, begins with an examination of application scenarios, progresses to evaluate current system architectures, and discusses the function of each component—specifically, the passive sensor module, the wireless communication model, and the readout module—within the context of key implementations in target sensing systems. Furthermore, we present case studies that demonstrate the feasibility of proposed classified components for sensing scenarios, derived from this systematic approach. By outlining a research trajectory for the application of passive wireless systems in sensing technologies, this paper aims to establish a foundation for more advanced, user-friendly applications.
Nano-Micro Letters•2025•DOI: 10.1007/s40820-024-01579-y
To emulate the functionality of the human retina and achieve a neuromorphic visual system, the development of a photonic synapse capable of multispectral color discrimination is of paramount importance. However, attaining robust color discrimination across a wide intensity range, even irrespective of medium limitations in the channel layer, poses a significant challenge. Here, we propose an approach that can bestow the color-discriminating synaptic functionality upon a three-terminal transistor flash memory even with enhanced discriminating capabilities. By incorporating the strong induced dipole moment effect at the excitation, modulated by the wavelength of the incident light, into the floating gate, we achieve outstanding RGB color-discriminating synaptic functionality within a remarkable intensity range spanning from 0.05 to 40 mW cm−2. This approach is not restricted to a specific medium in the channel layer, thereby enhancing its applicability. The effectiveness of this color-discriminating synaptic functionality is demonstrated through visual pre-processing of a photonic synapse array, involving the differentiation of RGB channels and the enhancement of image contrast with noise reduction. Consequently, a convolutional neural network can achieve an impressive inference accuracy of over 94% for Canadian-Institute-For-Advanced-Research-10 colorful image recognition task after the pre-processing. Our proposed approach offers a promising solution for achieving robust and versatile RGB color discrimination in photonic synapses, enabling significant advancements in artificial visual systems.
Nano-Micro Letters•2025•DOI: 10.1007/s40820-024-01545-8
Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities. Unlike existing approaches that often focus on static gestures and require extensive labeled data, the proposed wearable wristband with self-supervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios. It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes, resulting in high-sensitivity capacitance output. Through wireless transmission from a Wi-Fi module, the proposed algorithm learns latent features from the unlabeled signals of random wrist movements. Remarkably, only few-shot labeled data are sufficient for fine-tuning the model, enabling rapid adaptation to various tasks. The system achieves a high accuracy of 94.9% in different scenarios, including the prediction of eight-direction commands, and air-writing of all numbers and letters. The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training. Its utility has been further extended to enhance human–machine interaction over digital platforms, such as game controls, calculators, and three-language login systems, offering users a natural and intuitive way of communication.