Hierarchical Carbon Network Composites Derived from ZIF-8 for High-Efficiency Microwave Absorption.

Metal-organic framework (MOF)-derived composites have gained wide attention due to their specific structures and enhanced performance. In this work, we prepared carbon nanotubes with Fe nanoparticles connected to two-dimensional (2D) hierarchical carbon network composites via a low-pressure gas-solid reaction strategy. Specifically, the three-dimensional (3D) networks derived from ZIF-8 exploited the carbon nanotubes with the function of charge modulation. Meanwhile, we utilized the interconnected 2D nanostructures to optimize impedance matching and facilitate multiple scattering, ultimately improving the overall microwave absorption performance. Furthermore, based on the well-designed structures, the composites prepared at 800 °C (Fe-N-C@CNTs-800) achieved the best reflection loss (RL) of -58.5 dB, thereby obtaining superior microwave absorption performance. Overall, this study provides a good groundwork for further investigation into the modification and dimension design of novel hierarchical microwave absorbers.

Laser Welding Penetration Monitoring Based on Time-Frequency Characterization of Acoustic Emission and CNN-LSTM Hybrid Network.

In-process penetration monitoring of the pulsed laser welding process remains a great challenge for achieving uniform and reproducible products due to the highly complex nature of the keyhole dynamics within the intense laser-metal interactions. The main purpose of this study is to investigate the feasibility of acoustic emission (AE) measurement for penetration monitoring based on acoustic wave characteristics and deep learning. Firstly, a series of laser welding experiments on aluminum alloys were conducted using high-speed photography and AE techniques. This allowed us to in-situ visualize the complete keyhole dynamics and elucidate the generation mechanism of acoustic waves originating from pressure fluctuations at the keyhole wall. Then, an adaptive time-frequency technique namely VMD (Variational Mode Decomposition) was proposed to characterize the acoustic energy distribution among the nine subsignals with low-frequency and high-frequency components under different welding penetrations. Lastly, a novel hybrid model combing CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory) was designed to deeply mine the spatial and temporal acoustic features from the extracted frequency components. Extensive experiments demonstrate that our proposed approach yields a remarkable classification performance with a test accuracy of 99.8% and a standard deviation of 0.21, which obtains a high recognition rate. This work is a new paradigm in the digitization and intelligence of the laser welding process and contributes to an alternative way of developing an efficient end-to-end penetration monitoring system.

N-(3-Chloro-4-eth-oxy-benzo-yl)-N'-(2-meth-oxy-phen-yl)thio-urea.

In the title compound, C(17)H(17)ClN(2)O(3)S, the central carbonyl-thio-urea unit is nearly planar [maximum atomic deviation = 0.019 (3) Å] and makes dihedral angles of 2.47 (7) and 17.76 (6)° with the terminal benzene rings. An intra-molecular N-H⋯O hydrogen bond occurs. Weak inter-molecular C-H⋯S and C-H⋯Cl hydrogen bonding is observed in the crystal structure.