Interfacial effects of a ternary heterostructure on 1 T/2H MoS2 for Pt/C-comparable alkaline hydrogen evolution at industrially relevant high current densities - Nan Zhang; Menglin Gao; Wenru Cheng; Zhaocun Song; Xichen Yang (Journal of Environmental Chemical Engineering, 2026)

Rapid particulate microplastics removal from stormwater: Distinct adsorption behavior and data-driven intelligent prediction - Yan Yaping; Song Xichen; Wang Xiaojuan; Liu Xiuyun; Zhou Jianqiang; Wang Gang; Zhang Tingting; Wei Bigui; Zhao Yaqian (Chemical Engineering Journal, 2026)

Wettability-switchable deep bed filtration for efficient oily wastewater treatment - Song Xichen; Yan Yaping; Zhang Tingting; Zhou Jianqiang; Wang Xiaojuan; Wang Zhezhe; Lin Jialong; Wang Ting; Song Xiaosan; Dai Liang; Wang Gang; Gao Suxia; et al. (Journal of Hazardous Materials, 2026)

Efficient pollutant removal by iron-carbon bioretention system in long-term operation: Stability and performance - Zhou Jianqiang; Song Xichen; Wang Xiaojuan; He Jiangtao; Zhou Yawen; Qin Jie; Xiao Yifei; Gao Suxia; Li Hua; Liu Jianlin; Li Wei; Cao Lianbao; et al. (Journal of Environmental Chemical Engineering, 2026)

Removal performance and mechanisms of microplastics by superhydrophobic medium - Zhang Tingting; Cheng Wenqing; Wang Xiaojuan; Yan Yaping; Song Xichen; Zhou Jianqiang; Liu Xiuyun; Gao Suxia; Li Hao; Liu Jianlin; Cao Lianbao; Li Wei; et al. (Journal of Environmental Chemical Engineering, 2026)

Study on the removal and ecological risk of microplastics in road runoff by collapsible loess bioretention facilities - Wang Xiaojuan; Yan Yaping; Song Xichen; Zhou Jianqiang; Liu Xiuyun; Zhang Tingting; Gao Suxia; Li Jiapeng; Liu Jianlin; Cao Lianbao; Li Wei; Wei Bigui (Process Safety and Environmental Protection, 2026)

Preparation of superhydrophobic sand and simultaneous removal of oil and microplastics: Performance and synergistic mechanism - Yifei Xiao; Xiaojuan Wang; Xichen Song; Wenqing Cheng; Jianqiang Zhou; Haiyan Wen; Gang Wang; Liang Dai; Suxia Gao; Tingting Zhang; Bigui Wei (Journal of Environmental Chemical Engineering, 2025)

Dynamic Characteristic (Axial Impedances) of a Novel Sandwich Flexible Insert with Fluid - Leipeng Song; Lulu Chang; Feng Li; Xinjian Xiang; Zhiyong Yin; Xichen Hou; Yongping Zheng; Xiaozhou Xu; Yang Li; Zhihua Huang (Journal of Marine Science and Engineering, 2025)
Piping systems can be analogized to the “vascular systems” of vessels, but their transmission characteristics often result in loud noises and large vibrations. The integration of flexible inserts within these piping systems has been shown to isolate and/or mitigate such vibrations and noise. In this work, a novel sandwich flexible insert (NSFI) was presented specifically to reduce the vibrations and noise associated with piping systems on vessels. In contrast to conventional flexible inserts, the NSFI features a distinctive three-layer configuration, comprising elastic inner and outer layers, along with a honeycomb core exhibiting a zero Poisson’s ratio. The dynamic characteristics, specifically axial impedance, of the fluid-filled NSFI are examined utilizing a fluid–structure interaction (FSI) four-equation model. The validity of the theoretical predictions is corroborated through finite element analysis, experimental results, and comparisons with existing literature. Furthermore, the study provides a comprehensive evaluation of the effects of geometric and structural parameters on the dynamic characteristics of the NSFI. It is worth noting that axial impedance is significantly affected by these parameters, which suggests that the dynamic characteristics of the NSFI can be customized by parameter adjustments.

Preprocessing LOFARgram through U-Net++ neural network - Dan Peng; Xichen Xu; Wenhua Song; Dazhi Gao (Frontiers in Marine Science, 2025)
The study of the low-frequency analysis and recording spectrum (LOFARgram) of ship-radiated noise is essential for extracting critical information, such as target motion trajectories. However, the quality of LOFARgrams often degrades due to the inherent stochasticity of ship noise and the interference of environmental noise. We significantly enhance the clarity and quality of LOFARgrams by employing the U-Net++ neural network model for preprocessing. Effective training of neural network models usually requires large datasets, but the available actual LOFARgrams are often limited and costly to collect. To ensure an adequate dataset for neural network training, this paper introduces an innovative forward model that simulates LOFARgrams from stochastic noise sources. This model uses explosive decaying cosine pulses as basic units to simulate ship noise sources and employs the KRAKEN normal mode model to simulate the underwater acoustic channel’s transfer function, thereby efficiently creating high-fidelity ship noise LOFARgrams. The forward model supplies sufficient data to train the U-Net++ neural network, enabling it to demonstrate effective recovery of LOFARgrams. Additionally, we introduce a new algorithm that utilizes data prior to the Closest Point of Approach (CPA) to predict the CPA parameters, applied to both the original LOFARgrams and those processed with U-Net++. Results indicate that predictions based on U-Net++ enhanced LOFARgrams are more accurate. Our work demonstrate the effectiveness of the forward model and U-Net++ enhanced LOFARgrams for ship-radiated noise analysis and precise prediction of target motion.

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness - Chentao Song; Jiang Zhu; Xichen Li (Advances in Atmospheric Sciences, 2024)

Vibration Isolation Performance of a Constrained Damping Base for a High-Pressure Plunger Pump - Zepeng Zheng; Shuqing Wang; Xiancang Song; Xichen Wang (Journal of Marine Science and Engineering, 2024)
Based on the effect of damped shear deformation on energy dissipation, a new constrained damping base for a polymer injection platform deck is proposed to reduce the excessive vibrations caused when multiple plunger pumps are jointly operated. A model for analyzing the vibration response of an I-beam-constrained damping base for a polymer injection platform with multiple plunger pumps was established using Abaqus 6.14 software and compared with rigid base and traditional rubber vibration isolators in terms of its vibration isolation performance. Furthermore, the effects of the damping material’s loss factor, the thickness of the damping layer, and the number of expansion layers on the vibration isolation characteristics of the constrained damping base were explored. This study shows that, with an increase in the damping material’s loss factor, the thickness of the damping layer and the number of extended layers, the vibration isolation performance of the constrained damping base is gradually enhanced. When the damping material’s loss factor is 1.0, the thickness of the damping layer is 20 mm, and the number of extended layers is 3, the constrained damping base’s vibration damping effect is optimized, and its vibration isolation rate becomes as high as 46.63%, which can significantly reduce the vibration response of the polymer injection platform.

The technology of suppression containment for the SMR in floating nuclear power plants against hydrogen risk - Jialei Chen; Xuefeng Lyu; Fang Liu; Yu Yu; Fenglei Niu; Qingyao Song; Xichen Li (Annals of Nuclear Energy, 2024)

A broadscale shift in antarctic temperature trends - Meijiao Xin; Xichen Li; Sharon E. Stammerjohn; Wenju Cai; Jiang Zhu; John Turner; Kyle R. Clem; Chentao Song; Wenzhu Wang; Yurong Hou (Climate Dynamics, 2023)

China’s Recent Progresses in Polar Climate Change and Its Interactions with the Global Climate System - Xichen Li; Xianyao Chen; Bingyi Wu; Xiao Cheng; Minghu Ding; Ruibo Lei; Di Qi; Qizhen Sun; Xiaoyu Wang; Wenli Zhong; Lei Zheng; Meijiao Xin; et al. (Advances in Atmospheric Sciences, 2023)

Protective effect of gossypol on lipopolysaccharide-induced acute lung injury in mice - Zhicheng Liu; Zhengtao Yang; Yunhe Fu; Fenyang Li; Dejie Liang; Ershun Zhou; Xiaojing Song; Wen Zhang; Xichen Zhang; Yongguo Cao; Naisheng Zhang (Inflammation Research, 2013)