I.r. studies on the acidity of dealuminated Y zeolite with different probe molecules

Dealuminated HY zeolites (D-HY), which are partly reexchanged by La ions (D-LaHY), show two i.r. bands at 3625 cm⁻¹ (high frequency) and 3550 cm⁻¹ (low frequency). In contrast to the findings in D-HY and nondealuminated La-exchanged Y zeolite (LaHY), the LF band of D-LaHY is considerably affected by the adsorption of butadiene, in connection with a strongly increased coking. Sorption experiments with pyridine and ammonia confirm the OH groups belonging to the LF band to be acidic and accessible even to pyridine. This can be further confirmed by an HD exchange reaction with C₆D₆ at room temperature.     

基于YOLOv5和FCN-DenseNet水下图像多目标语义分割算法

带视觉系统的水下机器人作业离不开对水下目标准确的分割,但水下环境复杂,场景感知精度和识别精度不高等问题会严重影响目标分割算法的性能.针对此问题本文提出了一种综合YOLOv5和FCN-DenseNet的多目标分割算法.本算法以FCN-DenseNet算法为主要分割框架, YOLOv5算法为目标检测框架.采用YOLOv5算法检测出每个种类目标所在位置;然后输入针对不同类别的FCN-DenseNet语义分割网络,实现多分支单目标语义分割,最后融合分割结果实现多目标语义分割.此外,本文在Kaggle竞赛平台上的海底图片数据集上将所提算法与PSPNet算法和FCN-DenseNet算法两种经典的语义分割算法进行了实验对比.结果表明本文所提的多目标图像语义分割算法与PSPNet算法相比,在MIoU和IoU指标上分别提高了14.9%和11.6%;与FCN-DenseNet算法在MIoU和IoU指标上分别提高了8%和7.7%,更适合于水下图像分割.     

Surfactant-induced modification in photoluminescence properties of Ce0.9-xY0.1DyxO2-δ phosphors for single host white light emitting diodes

Dy³⁺/Y³⁺ codoped spherical ceria particles were synthesized via a sol-gel method using P123 as a surfactant. P123 amount was varied to control particle size around 70–200 nm. Photoluminescence excitation spectra of as-obtained ceria nano phosphors exhibited broadband around 280–305 nm due to O²⁻ to Ce⁴⁺ and Dy³⁺ charge transfer along with several sharp peaks around 325–365 nm due to f-f transitions. Strong UV excitation showed that the current phosphors were apposite for NUV-LEDs. The emission spectra of Ce_0.895Y_0.1 Dy_0.005O_2-δ phosphors comprised 489 (blue) and 579 nm (yellow) emissions corresponding to Dy³⁺ f-f transitions. The increase in emission intensities with P123 concentration was observed due to enhancement in crystallinity and particle size. Low Ω₂ (Judd Ofelt parameter) of 1.1 × 10⁻²⁰ cm² for Ce_0.895Y_0.1Dy_0.005O_2-δ phosphors synthesized without P123 indicated high symmetry neighboring Dy³⁺ ions. The increase in P123 concentration resulted in the elevation of Ω₂ value, leading to asymmetry surrounding Dy³⁺ ions. The local environment of Dy³⁺ was asymmetric, which caused the electric-dipole transition to upend the magnetic-dipole transition. Hence, the blue-to-yellow emission ratio (B/Y) was varied with P123, facilitating white light emission with color-coordinate (0.38,0.34). Photoluminescenc intensity of synthesized phosphors retained 86% of initial intensity at 500K (activation energy of 0.26 eV), indicating high stability at elevated temperature. The LEDs fabricated with present phosphors exhibited a color-rendering index and correlated color temperatures around 76.3 and 6065 K, respectively. The suitable Judd-Ofelt parameters and moderate activation energy suggested the feasibility of Ce_0.885Y_0.1Dy_0.015O_2-δ phosphors for white-light-emitting diodes.     

Study on hydrogen dynamic leakage and flame propagation at normal-temperature and high-pressure

Experiments of two nozzle diameters at three ignition positions under three initial pressure conditions were carried out. The dynamic leakage characteristics and the stagnation parameters of flame propagation under normal temperature and high pressure conditions were studied. Based on van der Waal's equation, a model for predicting stagnation parameters, jet velocity and flow rate of hydrogen leakage was proposed. Compared with the experimental results, it was found that the maximum error occurred when the initial pressure was 200 bar. Theoretical leakage time was 1.66 s, experiment leakage time was 1.84 s, the error was 9.8%. Background-Oriented Schlieren image technology was used to record the flame development and propagation process after ignition. For the same nozzle diameter and ignition location, the higher pressure caused the flame to propagate faster upstream and downstream. For the same initial pressure and ignition position, a flame with a large nozzle diameter propagated faster upstream and downstream. For the same initial pressure and nozzle diameter, the farther the ignition point was, the greater the slope of flame attenuation when propagating upstream. Due to the attenuation of hydrogen concentration and jet velocity, the flame propagation velocity to the downstream decreased linearly with the increase of distance from the ignition location.     

Multi-task spatio-temporal augmented net for industry equipment remaining useful life prediction

Accurate estimating the machine health indicator is an essential part of industrial intelligence. Despite having considerable progress, remaining useful life (RUL) prediction based on deep learning still confronts the following two challenges. Firstly, the length of condition monitoring data obtained from sensors is inconsistent, and the existing fixed window data processing method cannot fully adapt to all individual samples. Secondly, it is challenging to extract local and global features for long-series prediction tasks. To address these issues, this paper proposes a Multi-task Spatio-Temporal Augmented Net(MTSTAN) for industrial RUL prediction, which enhances the local features of different sensors data through channel attention mechanism, and proposes a skip connected causal augmented convolution network to enhance the global feature extraction in time series. For the industrial scenario of inconsistent data lengths, a multi-window multi-task sharing mechanism is set up to capture various time dependencies among different time scales. The robustness and universality of the model are increased by sharing information among tasks and multi-task window mechanism. Finally, a large number of experiments were carried out on the turbofan aircraft engine run-to-failure prognostic benchmark dataset (C-MAPSS) to evaluate the proposed model, and compared with the existing 14 state-of-the-art approaches. The results show that the enhancement of local and global time series features can effectively improve the prediction accuracy. The Multi-task learning strategy has excellent applicability in dealing with the problem of inconsistent data length.     

Mechanical and heat transfer properties of AlN/Cu joints based on nanosecond laser-induced metallization

In this paper, AlN ceramic was directly joined to copper without active brazing filler metal. By exerting the nanosecond laser irradiation on the AlN surface, the AlN ceramic was thermally decomposed to aluminum and the surface was metalized. The as-metalized AlN ceramic was successfully joined to copper through the eutectic reaction between aluminum and copper. The effect of the groove interface structure fabricated with various laser scanning speeds on the mechanical and heat transfer properties of AlN/Cu joints was investigated. The joints obtained optimal shear strength and thermal diffusivity of 10.64 MPa and 51.75 mm²/s at the scanning speed of 250 mm/s. The micro groove structures of the interface contributed to the enhancement of mechanical and heat transfer properties of joints. The approach of laser-irradiation assisted joining proposed in this paper provided a novel thought for the fabrication of metal/ceramic composite structures.