HUI WANG

Dr. HUI WANG


Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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6 November 2019 in Royal Society Open Science
Verified
Journal Article
Open access
JRJin Rao
Jin Rao
JJJun Jiang
Jun Jiang
NPNayebare Kakwara Prosper
Nayebare Kakwara Prosper
XYXiushu Yang
Xiushu Yang
TLTingsong Liu
Tingsong Liu
FSFangli Sun
Fangli Sun
Round bamboo has drawn more and more attention in architecture, decoration and recreational products. Splitting brings some safety problems, which shorten the service life of round bamboo. In this paper, three schemes were adopted as follows to solve the problem: round bamboo was impregnated in polyethylene glycol (PEG)-1000 solution alone, heat treatment in paraffin alone or treated with the combination of PEG impregnation and paraffin heat treatment (PEG–PH). The treated bamboo was exposed outdoors for 26 weeks to evaluate the development of cracks. Results showed as follows: the initial split of PEG–PH-treated bamboo appeared after 22 weeks, while that of the controls after 2 weeks, the total length of cracks was 2271.31 and 873.5 mm for the control and PEG–PH-treated bamboo, respectively. To reveal the reasons for reduced cracks, scanning electron micrograph (SEM) was employed to observe the microstructure of bamboo; besides, hydrophobicity of bamboo was characterized by the water contact angle. PEG can swell the cell wall and the better hydrophobicity of round bamboo could be achieved after PEG–PH treatment. Therefore, the combination of PEG immersion and paraffin heating is an effective and practicable method in bamboo treatment, especially for round bamboo with high moisture content.

20 November 2018 in Molecules
Verified
Journal Article
Open access
PDF file
HWHUI WANG
HUI WANG
MJMingyue Jiang
Mingyue Jiang
FSFangli Sun
Fangli Sun
Shujun LiShujun Li
Shujun Li
CHChung-Yun Hse
Chung-Yun Hse
CJChunde Jin
Chunde Jin
Development of new drugs is one of the solutions to fight against the existing antimicrobial resistance threat. Cinnamaldehyde-amino acid Schiff base compounds, are newly discovered compounds that exhibit good antibacterial activity against gram-positive and gram-negative bacteria. Quantitative structure–activity relationship (QSAR) methodology was applied to explore the correlation between antibacterial activity and compound structures. The two best QSAR models showed R2 = 0.9354, F = 57.96, and s2 = 0.0020 against Escherichia coli, and R2 = 0.8946, F = 33.94, and s2 = 0.0043 against Staphylococcus aureus. The model analysis showed that the antibacterial activity of cinnamaldehyde compounds was significantly affected by the polarity parameter/square distance and the minimum atomic state energy for an H atom. According to the best QSAR model, the screening, synthesis, and antibacterial activity of three cinnamaldehyde-amino acid Schiff compounds were reported. The experiment value of antibacterial activity demonstrated that the new compounds possessed excellent antibacterial activity that was comparable to that of ciprofloxacin.

17 November 2016 in Molecules
Verified
Journal Article
Open access
PDF file
DYDongmei Yang
Dongmei Yang
HWHUI WANG
HUI WANG
HYHaijian Yuan
Haijian Yuan
Shujun LiShujun Li
Shujun Li
Cinnamaldehyde, of the genius Cinnamomum, is a major constituent of the bark of the cinnamon tree and possesses broad-spectrum antimicrobial activity. In this study, we used best multiple linear regression (BMLR) to develop quantitative structure activity relationship (QSAR) models for cinnamaldehyde derivatives against wood-decaying fungi Trametes versicolor and Gloeophyllun trabeum. Based on the two optimal QSAR models, we then designed and synthesized two novel cinnamaldehyde compounds. The QSAR models exhibited good correlation coefficients: R2Tv = 0.910 for Trametes versicolor and R2Gt = 0.926 for Gloeophyllun trabeum. Small errors between the experimental and calculated values of two designed compounds indicated that these two QSAR models have strong predictability and stability.