Yanjun Xin
Qingdao Agricultural University
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Publication
Featured researches published by Yanjun Xin.
Environmental Technology | 2014
Zichao Wang; Mengchun Gao; Yanjun Xin; Dong Ma; Zonglian She; Zhe Wang; Changqing Sun; Yun Ren
The effect of C/N ratio on extracellular polymeric substances (EPS) of activated sludge was investigated in an anoxic–aerobic sequencing batch reactor (SBR) treating saline wastewater. The protein (PN) and protein/polysaccharide (PN/PS) ratio in the loosely bound EPS (LB-EPS) increased with the decrease of C/N ratio, whereas the PS in the LB-EPS decreased. The PS, PN and PN/PS ratio in the tightly bound EPS (TB-EPS) were independent of C/N ratio. Two fluorescence peaks in the LB-EPS and TB-EPS were identified at excitation/emission (Ex/Em) wavelengths of 275–280/335–340 nm and 220–225/330–340 nm by three-dimensional excitation–emission matrix (3D-EEM) fluorescence spectroscopy, respectively. These peaks in LB-EPS and TB-EPS were, respectively, associated with tryptophan protein-like substances and aromatic protein-like substances. The tryptophan protein-like fluorescence peaks in LB-EPS showed blue shift along the Ex axis and red shift along the Em axis with the decrease of C/N ratio. Fourier transform infrared spectra suggested that the variation of C/N ratio had more distinct effect on the functional groups of protein in the LB-EPS than those in the TB-EPS. The sludge volume index value decreased with the increase of LB-EPS, but there was no correlation between SVI and TB-EPS.
Sar and Qsar in Environmental Research | 2016
Xiangwei Zhu; Yanjun Xin; Qinghua Chen
Abstract In this study, recursive random forests were used to build classification models for mouse liver toxicity. The mouse liver toxicity endpoint (67 toxic and 166 non-toxic) was a composition of four in vivo chronic systemic and carcinogenic toxicity endpoints (non-proliferative, neoplastic, proliferative and gross pathology). A multiple under-sampling approach and a shifted classification threshold of 0.288 (non-toxic < 0.288 and toxic ≥ 0.288) were used to cope with the unbalanced data. Our study showed that recursive random forests are very efficient in variable selection and for the development of predictive in silico models. Generally, over 95% redundant descriptors could be reduced from modelling for all the chemical, biological and hybrid models in this study. The predictive performance of chemical models (CCR of 0.73) is comparable with hybrid model performance (CCR of 0.74). Descriptors related to the octanol–water partition coefficient are vital for model performance. The in vitro endpoint of CYP2A2 played a key role in the development and interpretation of hybrid models. Identifying high-throughput screening assays relevant to liver toxicity would be key for improving in silico models of liver toxicity.
Korean Journal of Chemical Engineering | 2015
Chunyue Cui; Juan Wu; Yanjun Xin; Yanhe Han
Palladized TiO2 nanotube array electrode was prepared for the electrocatalytic hydrodehalogenation (HDH) of 2,4,5-trichlorobiphenyl (2,4,5-PCB). The TiO2 nanotube array electrode was successfully fabricated by anodic oxidation method, and Pd was loaded onto the TiO2 nanotubes by electrochemical deposition. The morphology and structure of the nanotube array electrodes with and without Pd catalysts were evaluated by scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). The results showed that the diameters and lengths of the TiO2 nanotubes were 30–50 nm and 200–400 nm, respectively. The particle size of the Pd was about 12 nm. Electrocatalytic HDH of 2,4,5-PCB with the Pd/TiO2 nanotube array electrode was performed in H-cell reactor. Under a constant potential of -1.0 V, the HDH efficiency of 2,4,5-PCB was 90% and the biphenyl yield was 83% (15% current efficiency) within 180min at the Pd/TiO2 nanotube array electrode. Compared with the Pd/Ti electrode, the Pd/TiO2 nanotube array electrode exhibited higher HDH efficiency and stability. Additionally, the effect of the primary HDH factors was also investigated.
Chemical Engineering Journal | 2016
Dong Ma; Juan Wu; Mengchun Gao; Yanjun Xin; Tianjin Ma; Yuying Sun
Chemical Engineering Journal | 2016
Qinghua Chen; Shuna Wu; Yanjun Xin
Chemical Engineering Journal | 2014
Qinghua Chen; Huiling Liu; Yanjun Xin; Xiuwen Cheng
Chemical Engineering Journal | 2014
Zichao Wang; Mengchun Gao; Sen Wang; Yanjun Xin; Dong Ma; Zonglian She; Zhe Wang; Qingbo Chang; Yun Ren
Electrochimica Acta | 2013
Qinghua Chen; Huiling Liu; Yanjun Xin; Xiuwen Cheng
Chemical Engineering Journal | 2017
Dong Ma; Juan Wu; Mengchun Gao; Yanjun Xin; Yuying Sun; Tianjin Ma
Chemical Engineering Journal | 2017
Dong Ma; Juan Wu; Mengchun Gao; Yanjun Xin; Chao Chai