Zhigang Duan

Dr. Zhigang Duan

orcid

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

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

Representative Publications

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31 May 2018 in Polymers
Verified
Journal Article
Open access
PDF file
HWHui Wang
Hui Wang
MCMing Cao
Ming Cao
TLTaohong Li
Taohong Li
LYLong Yang
Long Yang
ZDZhigang Duan
Zhigang Duan
GDGuanben Du
Guanben Du
The structural changes during three-step synthesis of low-molar ratio urea–formaldehyde (UF) resin were tracked by quantitative 13C nuclear magnetic resonance (13C NMR) analysis and electrospray ionization-mass spectrometry (ESI–MS). Condensations that produced polymers were found to be linked by ether bonds in addition to hydroxymethylation reactions at the first alkaline stage with a formaldehyde to urea ratio of 2:1. Considerable formation of branched methylene linkages, with the highest content among all the condensed structures, was the key feature of the acidic stage. Notable changes were observed for the chemical structures and molecular masses of the resin components after the formaldehyde to urea molar ratio was lowered to 1.2 by adding post-urea at the final alkaline stage. Specifically, most of the branched hydroxymethyl groups on the polymers were cleaved, resulting in a significant decrease in the branching degree of the polymers. The performance degradation of the UF resin was attributed to this debranching effect and the production of components with low molecular masses. Based on the observations, the curing pattern of low molar ratio UF resin was postulated and branched polymeric formaldehyde catcher bearing urea-reactivity was proposed.

27 September 2022 in Research Square
Verified
Preprint
Open access
PDF file
YXYanjiao Xiong
Yanjiao Xiong
ZZZhipeng Zhao
Zhipeng Zhao
ZDZhigang Duan
Zhigang Duan
Thai is an analytic, isolated language where monosyllabic words occupy most of the basic vocabulary, and differences in vocabulary and grammar are distinguished by differences in tone. Traditional dictionary-based matching methods have difficulty constructing a complete dictionary. We use four machine learning algorithms, namely, a plain Bayesian classifier, a decision tree classifier, a support vector machine classifier, and a conditional random field, to slice Thai language texts for multiclass problems. In addition, we use the above four machine learning algorithms for a two-class problem slicing of Thai texts. In this study, we found that first, the accuracy, recall and F1 values of Thai text slicing using the conditional random field algorithm in the multiclass classification problem are much higher than those of the plain Bayesian classifier, decision tree classifier and support vector machine classifier. Second, the accuracy, recall, and F1 values of the plain Bayesian algorithm, J48 decision tree algorithm, and support vector machine algorithm are higher than those of the multiclass problem classification in both classes. The accuracy of the conditional random field was improved, but its recall and F1 values decreased significantly. In summary, it can be seen that the conditional random field has better results in Thai word separation.