MXene has attracted great attention due to its high conductivity, large specific surface area and tunable surface functional groups. However, MXene(e.g., Ti₃C₂) nanosheets tend to stack and mainly offer in-plane sites, showing limited capability in improving the oxygen reduction reaction(ORR) performance of iron phthalocyanine(FePc). In this study, mesoporous Ti₃C₂(Meso-_Ti3C₂) loaded FePc(FePc/Meso-Ti₃C₂) catalysts were prepared by a simple ultrasonic liquid-phase compounding strategy. Meso-Ti3C₂ possesses abundant mesopores and edge sites, which optimize the coordination environment and the electronic structure of the FeN₄ center in FePc. This optimization improves the mass transfer and the accessibility of the active sites, synergistically enhancing the ORR performance of FePc. As a result, FePc/Meso-Ti₃C₂ shows excellent ORR activity and stability under alkaline conditions with a half-wave potential of 0.914 V against the reversible hydrogen electrode(RHE) and a Tafel slope of 57.2 mV/dec. Furthermore, the zinc-air battery assembled with FePc/Meso-Ti₃C₂ delivers a peak power density of 183.1 mW/cm² and a good long-term discharge stability, exceeding those of FePc/Ti₃C₂ and commercial 20% Pt/C catalysts(20% Pt by mass).

Highly reduced molybdenum red(MR) clusters have emerged as a new type of polyoxomolybdates(POMos) and showed great potential as electron/proton reservoirs for energy conversion and storage, as well as for catalysis. However, the limited structural diversity of MR clusters significantly hinders further exploration of their potential as functional materials. Herein, we describe the synthesis of a novel highly reduced MR cluster {Mo₄₉}(compound 1) based on rational assembly of a variety of basic building blocks(BBs). In addition to the well-established BBs found in the family of MR clusters, the unique tetrahedral {Mo^VI₄} BB plays a key role in directing the assembly to afford trigonal pyramid-like structure of compound 1, which consists of 49 Mo and 148 O atoms with a high reduction degree of 73%. Moreover, at 80 ℃ and 98% relative humidity(RH), the pellet sample of compound 1 displays good proton conductivity of 7.88×10^-3 S/cm owing to the efficient hydrogen-bonded network built from the surface oxygen atoms, protons and guest water molecules. This research offers new insights into the assembly and synthesis of MR clusters through a BB strategy and manifests their significant potential for advanced applications.

Vanadium oxide(VO_x) has garnered significant attention in the realm of resistive random-access memory(RRAM) owing to its outstanding resistive switching characteristics.However,the ambiguous mechanisms of resistive switching and inferior stability hinder its practical applications.Herein,an RRAM named Cu/VO_x/TiO₂/n⁺⁺Si device is prepared.It displays bipolar resistive switching behavior and shows superior cycle endurance(>200),a significantly high on/off ratio(> 10²) and long-term stability.The tremendous improvement in the stability of the Cu/VO_x/TiO₂/n⁺⁺Si device compared with the Cu/VO_x/n⁺⁺Si device is due to the p-i-n structure of VO_x/TiO₂/n⁺⁺Si.The switching mechanism of the Cu/VO_x/TiO₂/n⁺⁺Si device is attributed to the growth and annihilation of Cu conductive filaments.

Mechanochromic materials respond to external stimuli and provide early warnings of material damage. Perylene diimide(PDI)-based materials have attracted attention because of their outstanding fluorescence performance. However, the application of PDI in mechanochromism is limited by the difficulty for mechanical forces to disrupt the aggregation of PDI and its derivatives, as well as the fluorescence quenching caused by continuous π-π stacking between PDI molecules. To eliminate the fluorescence quenching effect caused by the aggregation of PDI and broaden its application fields, polyhedral oligomeric silsesquioxane(POSS)-PDI-POSS(PPP) was screened as PDI doping. The photophysical properties of PPP in both monomeric and aggregated states in different solvents were studied. Then, PPP and styrene-butadiene-styrene block copolymer(SBS) were mixed to prepare the PPP/SBS film. The mechanochromic properties of PPP/SBS film were explored. The fluorescence emission spectra confirmed that when the PPP mass fraction increased to 0.30%, the PPP/SBS film exhibited mechanochromic behavior under uniaxial deformation due to the changes in the molecular packing.

With an increased utilization of carbon fiber reinforced polymers(CFRPs) in high temperature environments, investigating their effects on materials becomes exceedingly important. This study presents a comparative investigation of thermo-oxidative aging effects on the flexural performance of two carbon fiber reinforced composite laminates(CFRCLs): a quasi-isotropic plain-woven CFRCL and a quasi-isotropic unidirectional layup CFRCL(designated as PW-CFRCL and UD-CFRCL, respectively). The CFRCLs were subjected to thermo-oxidative aging for specific durations, and their flexural strength was evaluated through three-point bending tests. The flexural strength of the laminates decreased with the prolonged aging duration. Despite having lower fiber content, PW-CFRCLs showed higher flexural strength than UD-CFRCLs. After eight days of aging, the flexural strength of PW-CFRCLs decreased by merely 4%-5%, while that of UD-CFRCLs decreased by 11%-14%. After 32 days of aging, the thinner PW-CFRCL with the lowest fiber content exhibited the highest flexural strength(595.52 MPa), followed by the thinner UD-CFRCL(549.83 MPa), then the thicker PW-CFRCL(445.29 MPa) and finally, the thicker UD-CFRCL(393.90 MPa). The decline in flexural properties of the laminates was primarily attributed to matrix cracking and interface debonding resulting from matrix oxidation. To validate the universality of this result, the finite element method was employed, showing a good correlation with the experimental findings.

This study aims to explore the potential of using a blended pulp from Mikania micrantha(M. micrantha) and waste paper for producing composite paper. The effects of the mass ratio of M. micrantha stem to waste paper(MRMW), the beating time(BT), the water-to-pulp mass ratio(WPMR) and the times of pulp suspension screening(TPSS) on the paper's basic structural, optical and mechanical properties are investigated. It is found that MRMW primarily affects the grammage(mass per unit area), density, bulkness and whiteness; WPMR mainly affects the thickness and density; TPSS mainly affects the thickness and grammage. When MRMW is 3∶ 7, the composite paper shows higher values for thickness, grammage, density and whiteness; whereas when MRMW is 7∶ 3, these values are lower. Extending BT can increase paper density. The tensile strengths of all prepared samples fall in the range of 1.5 to 4.1 kN/m, indicating their excellent strength properties that meet the demands of many paper applications. The artistic bags and lampshades crafted from this composite paper exhibit a more natural texture compared to conventional packaging paper. This research demonstrates the feasibility of papermaking by using M. micrantha, while showcasing the potential for synergistic integration of waste resources with traditional hand papermaking techniques.

Traditional spectrophotometers have a large volume and slow scanning speed, which limits their applicability for rapid on-site detection. Herein, a micro-spectrophotometer(named ATOM) is fabricated, and its performance is verified in water quality testing. An M-type Czerny-Turner light path structure, a broadband light emitting diode(LED) light source, and a linear charge-coupled device(CCD) photodetector were adopted in ATOM. The performance of ATOM was validated through iron content determination by using o-phenanthroline spectrophotometry. The experiment results showed that the linear correlation coefficient of determination R² was 0.9997 for mass concentrations ranging from 0 to 2.0 μg/mL. The relative standard deviation was 0.37%, and the relative error compared to a commercial large-scale spectrophotometer was below 1.4%. The dimensions of ATOM are 75 mm × 60 mm × 25 mm, with hardware costs of approximately 1 000 CNY. ATOM features compact size, low cost, rapid measurement, high integration and high precision, making it suitable for portable on-site rapid detection.

During the sizing process, yarn congestion fault occurs at the reed teeth of a sizing machine. At present, the yarn congestion fault is generally handled by manual detection. The sizing production line operates on a large scale and runs continuously. Untimely handling of the yarn congestion fault causes a large amount of yarn waste. In this research, a machine vision-based algorithm for yarn congestion fault detection is developed. Through the analysis of the congestion fault and interference contour characteristics, the basic idea of image phase subtraction to identify the congestion fault is determined. To address the interference information appearing after image phase subtraction, the image pre-processing methods of Canny edge extraction and mean filtering are employed. According to the fault size and location characteristics, the fault contour detection algorithm based on inter-frame difference is designed. To mitigate the camera vibration interference, the anti-vibration interference algorithm based on affine transformation is studied, and the fault detection algorithm for the total yarn congestion fault is determined. The detection of 20 sets of field data is carried out, and the detection rate reaches 90%. This fault detection algorithm realizes the automatic detection of yarn congestion fault of sizing machine with certain real-time performance and accuracy.

Accurate detection of fashion design attributes is essential for trend analyses and recommendation systems. Among these attributes, the neckline style plays a key role in shaping garment aesthetics. However, the presence of complex backgrounds and varied body postures in real-world fashion images presents challenges for reliable neckline detection. To address this problem, this research builds a comprehensive fashion neckline database from online shop images and proposes an efficient fashion neckline detection model based on the YOLOv8 architecture(FN-YOLO). First, the proposed model incorporates a BiFormer attention mechanism into the backbone, enhancing its feature extraction capability. Second, a lightweight multi-level asymmetry detector head(LADH) is designed to replace the original head, effectively reducing the computational complexity and accelerating the detection speed. Last, the original loss function is replaced with Wise-IoU, which improves the localization accuracy of the detection box. The experimental results demonstrate that FN-YOLO achieves a mean average precision(mAP) of 81.7%, showing an absolute improvement of 3.9% over the original YOLOv8 model, and a detection speed of 215.6 frame/s, confirming its suitability for real-time applications in fashion neckline detection.

The objective of this work is to develop an innovative system(ROSGPT) that merges large language models(LLMs) with the robot operating system(ROS), facilitating natural language voice control of mobile robots. This integration aims to bridge the gap between human-robot interaction(HRI) and artificial intelligence(AI). ROSGPT integrates several subsystems, including speech recognition, prompt engineering, LLM and ROS, enabling seamless control of robots through human voice or text commands. The LLM component is optimized, with its performance refined from the open-source Llama2 model through fine-tuning and quantization procedures. Through extensive experiments conducted in both real-world and virtual environments, ROSGPT demonstrates its efficacy in meeting user requirements and delivering user-friendly interactive experiences. The system demonstrates versatility and adaptability through its ability to comprehend diverse user commands and execute corresponding tasks with precision and reliability, thereby showcasing its potential for various practical applications in robotics and AI. The demonstration video can be viewed at https://iklxo6z9yv.feishu.cn/docx/Lux3dmTDxoZ5YnxWJTZcxUCWnTh.

In the vision transformer(ViT) architecture, image data are transformed into sequential data for processing, which may result in the loss of spatial positional information. While the self-attention mechanism enhances the capacity of ViT to capture global features, it compromises the preservation of fine-grained local feature information. To address these challenges, we propose a spatial positional enhancement module and a wavelet transform enhancement module tailored for ViT models. These modules aim to reduce spatial positional information loss during the patch embedding process and enhance the model's feature extraction capabilities. The spatial positional enhancement module reinforces spatial information in sequential data through convolutional operations and multi-scale feature extraction. Meanwhile, the wavelet transform enhancement module utilizes the multi-scale analysis and frequency decomposition to improve the ViT's understanding of global and local image structures. This enhancement also improves the ViT's ability to process complex structures and intricate image details. Experiments on CIFAR-10, CIFAR-100 and ImageNet-1k datasets are done to compare the proposed method with advanced classification methods. The results show that the proposed model achieves a higher classification accuracy, confirming its effectiveness and competitive advantage.