Challenges in the synergistic mitigation of air pollutants and CO2 emissions from China's flat glass industry: A plant-level emission inventory analysis - Shaobo Chen; Libin Cao; Jing Guo; Ruxing Wan; Junpeng Lyu; Yan Chen; Jiabao Qu; Qingyu Zhang; Huizhong Shen; Jinnan Wang (Journal of Cleaner Production, 2026)

Solvent-free one-step mechanochemical synthesis of magnetic graphene composite adsorbent for efficient removal of multiple aflatoxins in vegetable oils - Libin Wan; Fanghui Zhao; Ming Lv; Shucai Gao; Haidong Gao; Mantang Chen; Lianjun Song; Wei Liu; Hongwei Liu; Fayun Wang (Food Chemistry, 2026)

Spatial equilibrium of water and land resources under water constraints: Historical and future modes in Henan Province, Yellow River Basin - Fan Xia; Hui Guo; Yu Wang; Dengming Yan; Chen Yang; Fang Wan; Libin Yang; Xiaoqing Cao; Zunyi Xie (Journal of Hydrology, 2025)

Improvements and Persistent Biases in the Ocean Climatology Simulated by the Beijing Climate Center Climate System Models from CMIP5 to CMIP6 - Libin Ma; Chong Wan; Tongwen Wu; Yixiong Lu; Fanghua Wu; Yongjie Fang; Jianglong Li; Xiaoge Xin; Ying He (Advances in Atmospheric Sciences, 2025)

Classification of Underwater Sediments in Lab Based on LiDAR Full-Waveform Data - Libin Du; Dawei Wan; Xiangqian Meng; Wenjing Li; Guangxin Liang; Hongyu Li (Journal of Marine Science and Engineering, 2025)
The classification of shallow sea sediments based on airborne LiDAR bathymetry represents a significant advancement in marine science and engineering. Airborne LiDAR is a highly valuable tool for the classification of seabed sediments, offering high accuracy and mobility. However, accurately classifying shallow marine sediments remains a challenging endeavor due to the difficulties associated with differentiation and the inherent limitations in accuracy. To achieve the accurate classification of underwater sediments, a feature selection method for underwater sediment classification is proposed in this paper and tested in a laboratory environment. The method inputs the original feature set into a classification algorithm that combines Sequential Forward Selection with Random Forests. The study demonstrates that the model achieves an overall classification accuracy of 94.1% and a Kappa coefficient of 91.11%, thereby enabling the accurate and efficient classification of underwater sediment. This approach can be employed as a supplementary technique for the precise classification of shallow marine sediments, offering valuable assistance in the examination of marine ecosystems.

Correction: Impacts of Langmuir turbulence derived from a statistical wave model on the oceanic mean state in the North Atlantic - Libin Ma; Chong Wan (Climate Dynamics, 2025)

Impacts of Langmuir turbulence derived from a statistical wave model on the oceanic mean state in the North Atlantic - Libin Ma; Chong Wan (Climate Dynamics, 2024)

Smartphone-assisted ratiometric FRET aptasensor based on quantum dots and gold nanoparticles for point-of-care testing of zearalenone in cereals - Lianjun Song; Meijun Lu; Puye Liang; Tiange Li; Xianqing Huang; Yan Ma; Libin Wan; Tianlin Wang (Food Control, 2024)

Dominant contribution of combustion-related ammonium during haze pollution in Beijing - Libin Wu; Peng Wang; Qiang Zhang; Hong Ren; Zongbo Shi; Wei Hu; Jing Chen; Qiaorong Xie; Linjie Li; Siyao Yue; Lianfang Wei; Linlin Song; et al. (Science Bulletin, 2024)

Ultrafine powder coating: Smooth surface, dense structure and enhanced corrosion resistance - Jinbao Huang; Marshall Yang; Libin Wan; Keyong Tang; Hui Zhang; Jian Chen; James J Noël; Ivan Barker; Haiping Zhang; Jesse Zhu (Chemical Engineering Journal, 2023)