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Quantum random number generation (QRNG) leveraging intrinsic quantum uncertainty has attracted significant interest in the field of integrated photonic architecture, with applications in quantum cryptography, tests of quantum nonlocality, and beyond. The demand for compact, low-energy consumption, robust, fast, and cost-effective QRNGs integrated into photonic chips is highlighted, whereas most previous works focused on bulk optics. Here, based on the metalens array entangled source, we experimentally realized a miniaturized, high-dimensional quantum random number generator via a meta-device without post-randomness extraction. Specifically, the device has a high-density output with 100 channels per square millimeter. This chip-scale quantum randomness source can obtain random number arrays without post-randomness extraction and enable compact integration for quantum applications needing secure keys or randomness. Our approach demonstrates potential in secure key generation and randomness for quantum applications. [ABSTRACT FROM AUTHOR]

We demonstrated a practical microfluidic approach to fabricate extremely monodisperse millimeter-sized poly(divinylbenzene) (PDVB) foam shells with the comparable double emulsion templates. At millimeter scale, the emulsification process was more complex and more difficult to control due to the very large characteristic sizes and velocities. A new kind of three-dimension co-axial microfluidic chip including a Y-shaped compound channels was designed to maintain more stable and robust flow field and regular emulsification. The hydrodynamic features of the double-emulsion droplet formation were investigated. The results showed perfect encapsulation and continuous emulsification could be obtained by one-step dripping ([Ca.sub.outer] [member of] (0.018,0.09)). The size of the outlet channel would be comparable with the target droplet. Besides, droplet diameters could similarly be plotted as a function of capillary number close to the general microfluidic ones. After photo-polymerization and supercritical drying, spherical and concentric PDVB foam shells were obtained to satisfy the inertial fusion energy experiments with large diameter (3-5.45 mm), thin wall thickness (50-250 [micro]m), low density (50-300 mg/[cm.sup.3]), and a less than 0.1% polydispersity., INTRODUCTION Due to the particular core-shell structures, millimeter-scale porous polymeric foam shells have a broad range of applications, such as controlled-release, micro-reactors, carriers of biologically active species, ion exchange resins [...]

A multi-responsive Cd(II) coordination polymer (1) has been constructed by introducing a viologen derivative as both the framework backbone and ligand side pendant. Notably, compound 1 exhibits intriguing properties, including photochromism, methanol-assisted photochromism and chemochromism to ammonia. Furthermore, compound 1 also displays fluorescence pH sensing ability in a wide pH range. [ABSTRACT FROM AUTHOR]

The miniature fiber Raman spectroscopy detection technology can reflect the properties of biomolecules through spectral characteristics and has the advantages of noninvasiveness, real-time, safety, label-free operation, and potential for early cancer diagnosis. This technology holds promise for developing portable, low-cost, intraoperative tumor detection instruments. Glioma is one of the most common malignant tumors of the central nervous system with rapid growth and a short disease course. However, the considerable heterogeneity of the glioma sample leads to substantial intraclass variance in collected spectra, coupled with the miniature Raman spectrometer's low signal-to-noise ratio. These factors diminish the accuracy of the brain glioma recognition model. To address this issue, a glioma identification method based on digital multimodal spectra integrated with deep learning features fusion (DMS-DLFF) using the miniature Raman spectrometer is proposed. Different from existing multimodal tumor detection methods employing multiple spectral instruments, DMS-DLFF enhances tumor identification accuracy without increasing hardware costs. The method mathematically decomposes the original spectra to Raman and fluorescence spectra, so as to augment the biospectral information. Then, the deep learning method is used to extract the feature information of the two kinds of spectra, respectively, and the digital multimodal spectral fusion is realized at the feature level. Moreover, a two-layer pattern recognition model is constructed based on the ensemble strategy, amalgamating the strengths of diverse classifiers. Meanwhile, the bagging strategy is introduced to improve support vector machine algorithms, one of the basic classifiers. Compared with traditional methodologies, DMS-DLFF operates at both the feature level and decision level, employing high-information-density feature vectors to train ensemble classification models for increasing overall recognition accuracy. This study collected 260 Raman spectra of glioma and 151 Raman spectra of normal brain tissue. The accuracy, sensitivity, and specificity were 91.9%, 96.7%, and 80.8%, respectively. The proposed method outperforms traditional algorithms in brain glioma detection, which helps doctors formulate precise surgical plans and thereby improve patient prognosis. [ABSTRACT FROM AUTHOR]