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Inspired by the emerging field of twistronics, moiré photonic structures have attracted great interest. In this paper, we introduce the concept of the moiré Kagome lattice (MKL), which arises from the overlapping of a Kagome lattice at a commensurable angle of 38.211°. MKL exhibits a significantly broader main photonic bandgap (PBG) when compared to the traditional Kagome lattice. Through numerical simulations, we delved into the adjustable properties of the PBG of MKL by varying lattice parameters, including filling factors and materials. Meanwhile, we also observed the presence of photonic flatbands within the MKL structure. Specifically, the emergence of a super flat isolated flatband aids in achieving single-mode omnidirectional lasers and enhancing the omnidirectional electromagnetic interaction of materials, thereby expanding the applications of moiré photonic crystals. [ABSTRACT FROM AUTHOR]

TiO2 nanostructures are synthesized in SiO2 by single implantation of Ti ions and sequential implantation of Ti and Ag ions with subsequent annealing in N2 atmosphere. The surface morphology, structure, and optical and photocatalytic properties of the fabricated TiO2 nanostructures are investigated. The experimental results indicate that the synthesized nanostructures are attributable to TiO2 in the anatase phase as annealing temperature reaches 700 ℃. However, further increase in the annealing temperature leads to the transformation of anatase TiO2 into rutile TiO2. Compared with the samples implanted only with Ti ions, the additional implantation of Ag ions promotes thermal growth of the TiO2 nanostructures and reduces their initial formation temperature and optical band gap. Moreover, results of the photocatalytic experiments demonstrate that additional Ag ion implantation improves the photocatalytic activity of the fabricated TiO2 nanostructures. The possible mechanisms for improvement in photocatalytic activity due to the presence of additional Ag ions is discussed based on measurements of photoluminescence and X-ray photoelectron spectroscopy.

As unique building blocks for advancing optoelectronics, 2D semiconducting transition metal dichalcogenides have garnered significant attention. However, most previously reported MoS2 photodetectors respond only to visible light with limited absorption, resulting in a narrow spectral response and low sensitivity. Here, a surrounding homojunction MoS2 photodetector featuring localized p‐type nitrogen plasma doping on the surface of n‐type MoS2 while preserving a high‐mobility underlying channel for rapid carrier transport is engineered. The establishment of p‐n homojunction facilitates the efficient separation of photogenerated carriers, thereby boosting the device's intrinsic detection performance. The resulting photoresponsivity is 6.94 × 104 A W−1 and specific detectivity is 1.21 × 1014 Jones @ 638 nm, with an optimal light on/off ratio of ≈107 at VGS = −27 V. Notably, the introduction of additional bands within MoS2 bandgap through nitrogen doping leads to an extrinsic broadband response to short‐wave infrared. The device exhibits a photoresponsivity of 34 A W−1 and a specific detectivity of up to 5.92 × 1010 Jones @ 1550 nm. Furthermore, the high‐performance broadband response is further demonstrated through imaging and integration with waveguides, paving the way for next generation of multifunctional imaging systems and high‐performance photonic chips. [ABSTRACT FROM AUTHOR]

In the More‐than‐Moore era, the explosive growth of data and information has driven the exploration of alternative non‐von Neumann computational paradigms. Photonic neuromorphic computing has emerged as a promising approach, offering high speed, wide bandwidth, and massive parallelism. Herein, a high‐resolution optical convolutional neural network (OCNN) is introduced using phase‐change material Ge2Sb2Te5 (GST)‐based microring hybrid waveguides. This on‐chip optical computing platform integrates GST into photonic devices, enabling versatile programming and in‐memory computing capabilities. Central to this platform is a photonic convolutional computational kernel, constructed from photonic switching cells embedded with GST on a microring resonator. This programmable photonic switch leverages the refractive index modulation during the GST phase transition to achieve up to 64 discrete levels of transmission contrast, suitable for representing matrix elements in neural network algorithms with 6‐bit resolution. Using these matrix elements, an OCNN capable of performing parallelized image edge detection and digital recognition tasks with high accuracy is demonstrated. The architecture is scalable for large‐scale photonic neural networks, offering ultrahigh computational throughput, a compact design, complementary metal‐oxide‐semiconductor‐compatible fabrication, and broad bandwidth. [ABSTRACT FROM AUTHOR]

High-internal-phase water-in-oil (W/O) emulsions generated in situ have garnered considerable attention as novel profile control systems. However, conventional emulsifiers are unreactive and poorly dispersed in water, necessitating large dosages and resulting in poor injectivity. In this study, we synthesized amphiphilic nanoparticles (SiO2–NH2–DAC NPs) containing amine and long-chain alkyl groups using a one-pot method and investigated the stabilized emulsion properties. Our results indicated that W/O emulsions with a water-to-oil ratio (WOR) of 7:3 to 8:2 could be prepared with just 0.1 wt% of SiO2–NH2–DAC NPs under neutral and basic conditions, with demulsification occurring under acidic conditions (pH = 2.1), demonstrating the pH-responsiveness of the W/O emulsions. The emulsion viscosity increased from 150 to 2555 mPa·s at different WORs. An additional 18.7% oil recovery was achieved using SiO2–NH2–DAC NPs in a heterogeneous core, highlighting their potential as a promising profile control candidate. [ABSTRACT FROM AUTHOR]

Intracranial hematoma, a severe brain injury caused by trauma or cerebrovascular disease, can result in blood accumulation and compression of brain tissue. Untreated cases can cause headaches, impaired consciousness, and even brain tissue damage or death. Therefore, early and accurate diagnosis is crucial. Traditional segmentation methods require physicians with extensive clinical experience and expertise to manually mark out the hematoma region, but for hematoma cases with irregular shapes and uneven grey levels, this process is cumbersome, and the segmentation results are not good. Existing deep learning‐based methods are more likely to perform binary segmentation, considering all hematomas as a class and segmenting them, but this segmentation cannot capture more detailed information and lacks the analysis of different types of hematomas. To address these problems, an ICH segmentation network combining CNN and Transformer Encoder is proposed for accurate segmentation of different types of hematomas. The network incorporated edge information and long‐range context into the segmentation process. Experimental results using the CQ500 dataset demonstrate comparable performance to existing methods, with mIoU (0.8705), TPR (0.9273), mAP (0.9300), and DSC (0.9286) as the best metrics achieved by this paper's method. [ABSTRACT FROM AUTHOR]

Bandgap engineering in monoclinic gallium oxide (β‐Ga2O3) is a powerful strategy for designing semiconductor optoelectronic devices with specific functionalities. In this work, aluminum doping is utilized to modulate the bandgap of Ga2O3. By growing epitaxial β‐(AlxGa1‐x)2O3 (0≤ x≤ 0.84) films on c‐plane sapphire substrates using RF magnetron sputtering, it allowed to tune the energy bandgap, achieving values as high as 6.10 eV. The increased luminescence intensity is attributed to the recombination between donor and acceptor transitions induced by Al doping, resulting in more defects. Additionally, the luminescent band experienced blueshifts due to the enhanced bandgaps. Moreover, density of functional theory (DFT) simulations confirmed the sensitivity of the bandgap to Al content, distinguishing between Ga‐dominated (x < 0.5) and Al‐dominated (x > 0.5) β‐(AlxGa1‐x)2O3. Notably, the bandgap increased more rapidly in Ga‐dominated structures compared to Al‐dominated ones. The electronic structure analysis revealed a redistribution of Ga d states from valence to conduction bands, attributed to the introduction of numerous Al p states. These combined experimental and detailed electronic structure investigations proved crucial insights for designing the structure and exploring potential applications of β‐(AlxGa1‐x)2O3 in photonic devices. [ABSTRACT FROM AUTHOR]