Zhejiang Gongshang University

About: Zhejiang Gongshang University is a education organization based out in Hangzhou, China. It is known for research contribution in the topics: Adsorption & Supply chain. The organization has 8258 authors who have published 7670 publications receiving 90296 citations. The organization is also known as: Zhèjiāng Gōngshāng Dàxué.

Performance of biotrickling filters packed with structured or cubic polyurethane sponges for VOC removal.

Abstract: Two identical bench-scale biotrickling filters (BTFs), BTF 1 and BTF 2, were evaluated for toluene removal at various gas empty bed contact times (EBCTs) and organic loadings. BTF 1 and BTF 2 were packed with structured and cubic synthetic polyurethane sponges, respectively. At a constant toluene loading of 16 g/(m 3 ·hr), toluene removal efficiencies decreased from 98.8% to 64.3% for BTF 1 and from 98.4% to 74.1% for BTF 2 as gas EBCT decreased from 30 to 5 sec. When the toluene loading increased from 35 to 140 g/(m 3 ·hr) at a gas EBCT of 30 sec, the removal efficiencies decreased from 99.1% to 77.4% for BTF 1 and from 99.0% to 81.5% for BTF 2. The pressure drop for both BTFs increased with increased air flow rate, and did not significantly vary while the toluene loading was increased under similar operation conditions. BTF 1 and BTF 2 could start up successfully within 19 and 27 days, respectively, when packed with fresh sponge media, and the performances could be restored in 3–7 days after biomass was removed and wasted from the media. BTF 2 displayed higher removal efficiency even under shorter EBCT or higher loading rate than BTF1 when other operation conditions were similar, while it showed lower pressure drop than BTF 1 during the whole period of operation. These results demonstrated that both BTFs could treat waste gas containing toluene effectively.

Critical assessment of high-throughput standalone methods for secondary structure prediction

Abstract: Sequence-based prediction of protein secondary structure (SS) enjoys wide-spread and increasing use for the analysis and prediction of numerous structural and functional characteristics of proteins. The lack of a recent comprehensive and large-scale comparison of the numerous prediction methods results in an often arbitrary selection of a SS predictor. To address this void, we compare and analyze 12 popular, standalone and high-throughput predictors on a large set of 1975 proteins to provide in-depth, novel and practical insights. We show that there is no universally best predictor and thus detailed comparative studies are needed to support informed selection of SS predictors for a given application. Our study shows that the three-state accuracy (Q3) and segment overlap (SOV3) of the SS prediction currently reach 82% and 81%, respectively. We demonstrate that carefully designed consensus-based predictors improve the Q3 by additional 2% and that homology modeling-based methods are significantly better by 1.5% Q3 than ab initio approaches. Our empirical analysis reveals that solvent exposed and flexible coils are predicted with a higher quality than the buried and rigid coils, while inverse is true for the strands and helices. We also show that longer helices are easier to predict, which is in contrast to longer strands that are harder to find. The current methods confuse 1-6% of strand residues with helical residues and vice versa and they perform poorly for residues in the β- bridge and 3(10)-helix conformations. Finally, we compare predictions of the standalone implementations of four well-performing methods with their corresponding web servers.