Circular dichroism (CD) is extensively used in various material systems for applications including biological detection, enantioselective catalysis, and chiral separation. This paper introduces a chiral absorptive metasurface that exhibits a circular polarization-selective effect in dual bands—positive and negative CD peaks at short wavelengths and long wavelengths, respectively. Significantly, we uncover that this phenomenon extends beyond the far-field optical response, as it is also observed in the photothermal effect and the dynamics of thermally induced fluid motion. By carefully engineering the metasurface design, we achieve two distinct CD signals with high g factors (∼1) at the wavelengths of 877 nm and 1045 nm, respectively. The findings presented in this study advance our comprehension of CD and offer promising prospects for enhancing chiral light–matter interactions in the domains of nanophotonics and optofluidics.
Non-coding RNAs (ncRNAs) are RNAs that do not encode proteins but play important roles in regulating cellular processes. Multiple studies over the past decade have demonstrated the role of microRNAs (miRNAs) in cancer, in which some miRNAs can act as biomarkers or provide therapy target. Accumulating evidence also points to the importance of long non-coding RNAs (lncRNAs) in regulating miRNA-mRNA networks. An increasing number of ncRNAs have been shown to be involved in the regulation of cellular processes, and dysregulation of ncRNAs often heralds disease. As the population ages, the incidence of neurodegenerative diseases is increasing, placing enormous pressure on global health systems. Given the excellent performance of ncRNAs in early cancer screening and treatment, here we attempted to aggregate and analyze the regulatory functions of ncRNAs in neuronal development and disease. In this review, we summarize current knowledge on ncRNA taxonomy, biogenesis, and function, and discuss current research progress on ncRNAs in relation to neuronal development, differentiation, and neurodegenerative diseases.
Abstract Photothermal superhydrophobic coatings are essential for a variety of applications including anti‐icing and light‐driven self‐propelled motion. However, achieving a flexible and durable superhydrophobic coating with high photothermal efficiency and long‐term stability is still challenging. Herein, a facile and eco‐friendly approach to realizing a superhydrophobic coating with excellent flexibility is proposed. The coating is obtained by spraying titanium nitride (TiN) nanoparticles embedded in polydimethylsiloxane (PDMS) solution onto various substrates. A tight binding between the substrate and nanoparticles occurs that offers the coating the mechanical robustness to endure bending, twisting, abrasion, and tape peeling. The water repellency is retained even after 500 cycles of bending–twisting tests. Combined with the micro–nanoscale porous structure of the surface and plasmonic property of TiN nanoparticles, the coating shows excellent superhydrophobicity and high photothermal conversion properties. The equilibrium temperature of the coating is as high as 130 °C at room temperature under 1 W cm −2 of 808 nm near‐infrared laser irradiation. Due to its flexible property, the coating can be easily applied to irregular surfaces, which, together with the excellent anticorrosion, anti‐icing, and defrosting performances, makes it a reliable resource for multifunctional applications. This work offers a novel technological approach to flexible devices, wearable electronics, and smart textiles.
Automatically extracting urban buildings from remote sensing images has essential application value, such as urban planning and management. Gaofen-7 (GF-7) provides multi-perspective and multispectral satellite images, which can obtain three-dimensional spatial information. Previous studies on building extraction often ignored information outside the red–green–blue (RGB) bands. To utilize the multi-dimensional spatial information of GF-7, we propose a dual-stream multi-scale network (DMU-Net) for urban building extraction. DMU-Net is based on U-Net, and the encoder is designed as the dual-stream CNN structure, which inputs RGB images, near-infrared (NIR), and normalized digital surface model (nDSM) fusion images, respectively. In addition, the improved FPN (IFPN) structure is integrated into the decoder. It enables DMU-Net to fuse different band features and multi-scale features of images effectively. This new method is tested with the study area within the Fourth Ring Road in Beijing, and the conclusions are as follows: (1) Our network achieves an overall accuracy (OA) of 96.16% and an intersection-over-union (IoU) of 84.49% for the GF-7 self-annotated building dataset, outperforms other state-of-the-art (SOTA) models. (2) Three-dimensional information significantly improved the accuracy of building extraction. Compared with RGB and RGB + NIR, the IoU increased by 7.61% and 3.19% after using nDSM data, respectively. (3) DMU-Net is superior to SMU-Net, DU-Net, and IEU-Net. The IoU is improved by 0.74%, 0.55%, and 1.65%, respectively, indicating the superiority of the dual-stream CNN structure and the IFPN structure.
Colloidal copper chalcogenide-based CuAlS2 (CAS) quantum dots (QDs) composed of earth-abundant and benign elements are emerging building blocks for optoelectronic technologies. However, their potential applications in high-performance optoelectronic devices are still unexplored due to limited optical properties and poor charge transfer efficiency. Here, a new class of CAS/ZnSe QDs exhibiting decent visible light absorption/emission was prepared and associative QD-gold nanoclusters (Au NCs) heterostructures were rationally constructed for improved charge separation and transport efficiency. Both experimental and theoretical studies revealed that such metal nanocluster decoration enables effective charge transfer from QDs to Au NCs. Photodetectors (PDs) fabricated using CAS/ZnSe QDs and Au-QD heterostructures were further demonstrated, wherein the Au-QDs PD show enhanced device performance with a responsivity of 7.57 A W–1 and a detectivity of 2.48 × 1011 Jones (405 nm, 2.8 mW cm–2) compared to that of the CAS/ZnSe QD PDs. We found that the Au NCs conjunction is key to setting intermediate energy levels and facilitating photoinduced charge transfer from QDs to the electron-transport layers (TiO2) employed in PD devices. Besides, as-assembled, Au-QD PDs have demonstrated optoelectronic synapse application by emulating the learning and forgetting processes under optical stimulation, representing a promising prototype device to achieve future solution-processed neuromorphic electronics.
The detection of acetone in the gaseous form in exhaled breath using an integrated sensor can provide an effective tool for disease diagnostics as acetone is a marker for monitoring human metabolism. An on-chip acetone gas sensor based on the principle of Mach-Zehnder interferometer is proposed and demonstrated. The sensing arm of the device is activated with a composite film of polyethyleneimine and amido-graphene oxide as the gas-sensitive adsorption layer. The composite film demonstrates good selectivity to acetone gas, can be used repeatedly, and is stable in long-term use. Room temperature operation has been demonstrated for the sensor with high sensitivity under a 20 ppm acetone environment. The detection limit can reach 0.76 ppm, making it feasible to be used for the clinical diagnosis of diabetes and the prognosis of heart failure.
A typical Tesla thermomagnetic engine employs a solid magnetic wheel to convert thermal energy into mechanical energy, while thermomagnetic convection in ferrofluid is still challenging to observe because it is a volume convection that occurs in an enclosed space. Using a water-based ferrofluid, a liquid Tesla thermomagnetic engine is demonstrated and reports the observation of thermomagnetic convection on a free surface. Both types of fluid motions are driven by light and observed by simply placing ferrofluid on a cylindrical magnet. The surface thermomagnetic convection on the free surface is made possible by eliminating the Marangoni effect, while the spinning of the liquid wheel is achieved through the solid-like behavior of the ferrofluid under a strong magnetic field. Increasing the magnetic field reveals a transition from simple thermomagnetic convection to a combination of the central spin of the spiky wheel surrounded by thermomagnetic convection in the outer region of the ferrofluid. The coupling between multiple ferrofluid wheels through a fluid bridge is further demonstrated. These demonstrations not only unveil the unique properties of ferrofluid but also provide a new platform for studying complex fluid dynamics and thermomagnetic convection, opening up exciting opportunities for light-controlled fluid actuation and soft robotics.
Introduction The accurate extraction of navigation paths is crucial for the automated navigation of agricultural robots. Navigation line extraction in complex environments such as Panax notoginseng shade house can be challenging due to factors including similar colors between the fork rows and soil, and the shadows cast by shade nets. Methods In this paper, we propose a new method for navigation line extraction based on deep learning and least squares (DL-LS) algorithms. We improve the YOLOv5s algorithm by introducing MobileNetv3 and ECANet. The trained model detects the seven-fork roots in the effective area between rows and uses the root point substitution method to determine the coordinates of the localization base points of the seven-fork root points. The seven-fork column lines on both sides of the plant monopoly are fitted using the least squares method. Results The experimental results indicate that Im-YOLOv5s achieves higher detection performance than other detection models. Through these improvements, Im-YOLOv5s achieves a mAP (mean Average Precision) of 94.9%. Compared to YOLOv5s, Im-YOLOv5s improves the average accuracy and frame rate by 1.9% and 27.7%, respectively, and the weight size is reduced by 47.9%. The results also reveal the ability of DL-LS to accurately extract seven-fork row lines, with a maximum deviation of the navigation baseline row direction of 1.64°, meeting the requirements of robot navigation line extraction. Discussion The results shows that compared to existing models, this model is more effective in detecting the seven-fork roots in images, and the computational complexity of the model is smaller. Our proposed method provides a basis for the intelligent mechanization of Panax notoginseng planting.
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