Browsing by Author "Zhang, Bingbing"

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    Efficient removal of Cd(II) from aqueous solution by pinecone biochar: Sorption performance and governing mechanisms
    (Elsevier, 2020-06-12) Teng, Dongye; Zhang, Bingbing; Xu, Guomin; Wang, Bing; Mao, Kang; Wang, Jianxu; Sun, Jing; Feng, Xinbin; Yang, Zhugen; Zhang, Hua
    Cadmium (Cd) is one of the most harmful and widespread environmental pollutants. Despite decades-long research efforts, the remediation of water contaminated by Cd has remained a significant challenge. A novel carbon material, pinecone biochar, was previously hypothesized to be a promising adsorbent for Cd, while so far, it has received little attention. This study evaluated the sorption capacity of pinecone biochar through isotherm experiments. Based on Langmuir model, the adsorption maximum for Cd(II) was up to 92.7 mg g−1. The mechanism of Cd(II) adsorption on pinecone biochar was also explored through both thermodynamic and kinetics adsorption experiments, as well as both solution and solid-phase microstructure characterization. The solid-solution partitioning behaviour of Cd(II) fitted best with the Tόth model while the adsorption process followed a pseudo-second-order rate, suggesting that the Cd(II) adsorption on the pinecone biochar was mainly a chemisorption process. Microstructure characteristics and mechanism analysis further suggested that coprecipitation and surface complexation were the main mechanisms of Cd adsorption by biochar. Coprecipitation occurred mainly through the forms of Cd(OH)2 and CdCO3. Our results demonstrated that pinecone biochar was an efficient adsorbent which holds a huge potential for Cd(II) removal from aqueous solution.
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    A multimodal psychological, physiological and behavioural dataset for human emotions in driving tasks
    (Nature Publishing Group, 2022-08-06) Li, Wenbo; Tan, Ruichen; Xing, Yang; Li, Guofa; Li, Shen; Zeng, Guanzhong; Wang, Peizhi; Zhang, Bingbing; Su, Xinyu; Pi, Dawei; Guo, Gang; Cao, Dongpu
    Human emotions are integral to daily tasks, and driving is now a typical daily task. Creating a multi-modal human emotion dataset in driving tasks is an essential step in human emotion studies. we conducted three experiments to collect multimodal psychological, physiological and behavioural dataset for human emotions (PPB-Emo). In Experiment I, 27 participants were recruited, the in-depth interview method was employed to explore the driver’s viewpoints on driving scenarios that induce different emotions. For Experiment II, 409 participants were recruited, a questionnaire survey was conducted to obtain driving scenarios information that induces human drivers to produce specific emotions, and the results were used as the basis for selecting video-audio stimulus materials. In Experiment III, 40 participants were recruited, and the psychological data and physiological data, as well as their behavioural data were collected of all participants in 280 times driving tasks. The PPB-Emo dataset will largely support the analysis of human emotion in driving tasks. Moreover, The PPB-Emo dataset will also benefit human emotion research in other daily tasks.