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SYNTHESIS OF CROSS-CONJUGATED(p-PHENYLENE)S-POLY(p-PHENYLENEVINYLENE)S HYBRIDS WITH LOW CONTENT OF STRUCTURAL DEFECTS

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Chinese Journal of Polymer Science 2007年第2期25卷 171-179页

作者： Shi-jun Zheng qun Xu shao-kui Cao College of the Material Science and Engineertng Zhengzhou University Zhengzhou 450052 China

The novel shish-kebab-type liquid crystalline cross-conjugated （p-phenylene）s-poly（p-phenylenevinylene）s hybrid was synthesized through Gilch polymerization. Their structures and properties were characterized by NMR, GPC, DSC, X-ray diffraction and polarizing optical microscope （POM）. ^1H-NMR investigation of the polymers indicates that the shish-kebab-structure has a strong ability to suppress the structural defects in the polymers. The polymers are enantiotropic liquid crystals. The melting point （Tm） of the polymers decreases when the length of the alkoxy tails of the mesogenic units increases. The mesophase was identified by X-ray diffraction method. They showed not only a smectic LC phase, but also a strong green fluorescence in chloroform. The maximum absorption band of the ＂kebabs＂ of the two, 5-bis（4＇- alkoxyphenyl）benzene at 280 nm did not appear in absorption spectra of the polymers. The same phenomena were also observed in the fluorescence spectra. These results imply that the polymers have formed a cross-conjugated uniform structure and achieved an extended n-conjugation polymer.

关键词： Gilch polymerization Conjugated polymer Liquid crystal Shish-kebab Fluorescence.

Tribological behavior of AISI302 austenitic stainless steel modified by elevated temperature nitrogen plasma immersion ion implantation

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中国有色金属学会会刊：英文版 2004年第Z1期14卷 301-304页

作者： JIANG shao-qun MA Xin-xin SUN Yue School of Materials Science and Engineering Harbin Institute of Technology Harbin 150001 China School of Materials Science and Engineering Harbin Institute of Technology Harbin 150001 China School of Materials Science and Engineering Harbin Institute of Technology Harbin 150001 China

AISI302 stainless steel samples were modified by elevated temperature nitrogen plasma immersion ion implantation at temperature ranging from 330 ℃ to 450 ℃. The tribological behaviors of the implanted layers of the samples were investigated. The samples were characterized by Auger electron spectroscopy (AES), glancing angle X-ray diffraction (GXRD), and nanoindentation. The results show that the implantation temperature plays an important rule on the microstructure and surface properties of the implanted layers. The thickness of the modified layer implanted at 390 ℃ is about 9 μm. It is improved about two orders compared with that of the implanted at room temperature. The surface nanohardness and the wear resistance of elevated temperature implanted layers increase significantly, and the friction coefficient decreases obviously in comparison with the unimplanted one. These data suggests that the improvement results from the formation of new phases such as ε-(Fe, Cr, Ni)2+xN, or noncrystal phase.

关键词： austenite stainless steel elevated temperature plasma immersion ion implantation tribology nanohardness

Measurement and correlation study of silymarin solubility in supercritical carbon dioxide with and without a cosolvent using semi-empirical models and back-propagation artificial neural networks

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Asian Journal of Pharmaceutical Sciences 2017年第5期12卷 456-463页

作者： Gang Yang Zhe Li qun shao Nianping Feng Department of Pharmaceutical Sciences Shanghai University of Traditional Chinese MedicineShanghai201203China Open Innovation University of BradfordWest YorkshireBD71DPUK

The solubility data of compounds in supercritical fluids and the correlation between the experimental solubility data and predicted solubility data are crucial to the development of supercritical technologies. In the present work, the solubility data of silymarin(SM) in both pure supercritical carbon dioxide(SCCO2) and SCCO2 with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from 8 to 22 MPa. The experimental data were fit with three semi-empirical density-based models(Chrastil, Bartle and Mendez-Santiago and Teja models) and a back-propagation artificial neural networks(BPANN) model. Interaction parameters for the models were obtained and the percentage of average absolute relative deviation(AARD%) in each calculation was determined. The correlation results were in good agreement with the experimental data. A comparison among the four models revealed that the experimental solubility data were more fit with the BPANN model with AARDs ranging from 1.14% to 2.15% for silymarin in pure SCCO2 and with added cosolvent. The results provide fundamental data for designing the extraction of SM or the preparation of its particle using SCCO2 techniques.

关键词： Silymarin Solubility Supercritical carbon dioxide Cosolvent Artificial neural networks

Bonding characteristics of the Al_2O_3-metal composite coating fabricated onto carbon steel by combustion synthesis

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International Journal of Minerals,Metallurgy and Materials 2014年第9期21卷 886-893页

作者： Xiao-feng Xue Ze-hua Wang Ze-hua Zhou shao-qun Jiang Jiang-bo Cheng Chang-hao Wang Jia shao College of Mechanics and Materials Hohai University

The fabrication of an alumina-metal composite coating onto a carbon steel substrate by using a self-propagating high-temperature synthesis technique was demonstrated. The effects of the type and thickness of the pre-coated layer on the binding structure and surface qual- ity of the coating were systematically investigated. The macrostructure, phase composition, and bonding interface between the coating and the substrate were investigated by scanning electronic microscopy （SEM）, X-ray diffraction （XRD）, and energy-dispersive X-ray spectrometry （EDS）. The diffraction patterns indicated that the coating essentially consisted of α-Al2O3, Fe（Cr）, and FeO-Al2O3. With an increase in the thickness of the pre-coated working layer, the coating became more smooth and compact. The transition layer played an important role in enhancing the binding between the coating and the substmte. When the pre-coated working layer was 10 mm and the pre-coated transition layer was 1 ram, a compact structure and metallurgical bonding with the substrate were obtained. Thermal shock test results indicated that the ceramic coating exhibited good thermal shock resistance when the sample was rapidly quenched from 800℃ to room temperature by plunging into water.

关键词： carbon steel combustion synthesis composite coatings phase composition morphology thermal shock resistance

In situ synthesis of Fe-based alloy clad coatings containing TiB_2–TiN –(h-BN)

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International Journal of Minerals,Metallurgy and Materials 2015年第6期22卷 613-619页

作者： shao-qun Jiang Gang Wang Qing-wen Ren Chuan-duo Yang Ze-hua Wang and Ze-hua Zhou College of Mechanics and Materials Hohai University Changzhou Hohai Institute of Science and Technology Limited Liability Company

Fe-based alloy coatings containing TiB2–TiN –（h-BN） were synthesized in situ on Q235 steel substrates by a plasma cladding process using the powders of Fe901 alloy, Ti, and h-BN as raw materials. The effects of Ti/h-BN mass ratio on interfacial bonds between the coating and substrate along with the microstructures and microhardnesses of the coatings were investigated. The results show that the Ti/h-BN mass ratio is a vital factor in the formation of the coatings. Free h-BN can be introduced into the coatings by adding an excess amount of h-BN into the precursor. Decreases in the Ti/h-BN mass ratio improve the microstructural uniformity and compactness and enhance the interfacial bonds of the coatings. At a Ti/h-BN mass ratio of 10/20, the coating is free of cracks and micropores, and mainly consists of Fe-Cr, Fe3B, TiB2, TiN, Ti2N, TiB, FeN, FeB, Fe2B, and h-BN phases. Its average microhardness in the zone between 0.1–2.8 mm from the coating surface is about Hv0.2 551.5.

关键词： iron-based materials coatings plasma cladding in situ synthesis microhardness

Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

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Petroleum Science 2024年第3期21卷 1632-1648页

作者： Jin-Xiong Shi Xiang-Yuan Zhao Lian-Bo Zeng Yun-Zhao Zhang Zheng-Ping Zhu shao-qun Dong Key Laboratory of Exploration Technologies for Oil and Gas Resources of the Ministry of Education Yangtze UniversityWuhan4310HubeiChina School of Geosciences Yangtze UniversityWuhan430100HubeiChina College of Geosciences China University of Petroleum(Beijing)Beijing102249China Petroleum Exploration and Production Research Institute of SINOPEC Beijing100083China School of Earth Sciences and Engineering Xi'an Shiyou UniversityXi'an 710065ShaanxiChina

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage *** cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging ***,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ***,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data *** address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,*** MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input *** particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD *** evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM ***,the built MKFD model was applied in a blind well test,and a variable importance analysis was *** comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger *** MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

关键词： Reservoir type identification Geophysical logging data Kernel Fisher discriminantanalysis Mixedkernel function Deep carbonates

Fabrication and characterization of Al_2O_3-metal composite coating on steel plate with nonpressure combustion synthesis

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Rare Metals 2013年第4期32卷 390-396页

作者： Chang-Hao Wang Ze-Hua Wang Ze-Hua Zhou shao-qun Jiang Xiao-Feng Xue Jing-Jing Yao College of Mechanics and Materials Hohai University School of Materials Science and Engineering Shanghai University

Al2O3-metal composite coatings with different reactants and diluents were fabricated on mild steel plate with nonpressure combustion synthesis process. The coat-ings were characterized by means of X-ray diffraction, scanning electron microscopy, and energy-dispersive spec-trometry, respectively. Thermal shock tests were carried out to determine the bond strength of the coating with the steel substrate. The results indicate that the coating is composed of α-A1203, α-（Fe-Cr） and Al2SiO5 as the main phases. It is found that the coating with the diluents of Al2O3-SiO2 and transition layer of Al2O3-Cr presents the *** hardness of 2270 HV0.2 and the lowest porosity of 3.93 %. Owing to a metallurgical bond of the coating-to-substrate, the coating exhibits a good thermal shock resistance.

关键词： Combustion synthesis Al2O3-metalcomposite coating Diluents Thermal shock resistance

How to improve machine learning models for lithofacies identification by practical and novel ensemble strategy and principles

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Petroleum Science 2023年第2期20卷 733-752页

作者： shao-qun Dong Yan-Ming Sun Tao Xu Lian-Bo Zeng Xiang-Yi Du Xu Yang Yu Liang State Key Laboratory of Petroleum Resources and Prospecting China University of PetroleumBeijing102249China College of Science China University of PetroleumBeijing102249China College of Geoscience China University of PetroleumBeijing102249China

Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs labelled by rock cores. However, these methods have accuracy limits to some extent. To further improve their accuracies, practical and novel ensemble learning strategy and principles are proposed in this work, which allows geologists not familiar with ML to establish a good ML lithofacies identification model and help geologists familiar with ML further improve accuracy of lithofacies identification. The ensemble learning strategy combines ML methods as sub-classifiers to generate a comprehensive lithofacies identification model, which aims to reduce the variance errors in prediction. Each sub-classifier is trained by randomly sampled labelled data with random features. The novelty of this work lies in the ensemble principles making sub-classifiers just overfitting by algorithm parameter setting and sub-dataset sampling. The principles can help reduce the bias errors in the prediction. Two issues are discussed, videlicet (1) whether only a relatively simple single-classifier method can be as sub-classifiers and how to select proper ML methods as sub-classifiers;(2) whether different kinds of ML methods can be combined as sub-classifiers. If yes, how to determine a proper combination. In order to test the effectiveness of the ensemble strategy and principles for lithofacies identification, different kinds of machine learning algorithms are selected as sub-classifiers, including regular classifiers (LDA, NB, KNN, ID3 tree and CART), kernel method (SVM), and ensemble learning algorithms (RF, AdaBoost, XGBoost and LightGBM). In this work, the experiments used a published dataset of lithofacies from Daniudi gas field (DGF) in Ordes Basin, China. Based on a series of comparisons between ML algorithms and their corresponding ensemble models using the ensemble strat

关键词： Lithofacies identification Machine learning Ensemble learning strategy Ensemble principle Homogeneous ensemble Heterogeneous ensemble

Characterization of dwarf mutants and molecular mapping of a dwarf locus in soybean

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Journal of Integrative Agriculture 2016年第10期15卷 2228-2236页

作者： CHENG Wen GAO Jin-shan FENG Xing-xing shao qun YANG Su-xin FENG Xian-zhong Key Laboratory of Systems Biology in Universities of Shandong/College of Life Science Shandong Normal University Key Laboratory of Soybean Molecular Design Breeding Northeast Institute of Geography and Agroecology Chinese Academy of Sciences

Plant height is one of the most important traits in soybean. The semi-dwarf soybean cultivars could improve the ability of lodging resistance to obtain higher yield. To broaden the dwarfism germplasm resources in soybean, 44 dwarf mutants were identified from a gamma rays mutagenized M-2 population. Two of these mutants, Gmdwf1（Glycine max dwarf 1） and Gmdwf2（Glycine max dwarf 2）, were investigated in this study. Genetic analysis showed that both mutants were inherited in a recessive manner and their mutated regions were delimited to a 2.610-Mb region on chromosome 1 by preliminary mapping. Further fine mapping study proved that the two mutants had a common deletion region of 1.552 Mb in the target region, which was located in a novel locus site without being reported previously. The dwarfism of Gmdwf1 could not be rescued by gibberellin（GA） and brassinolide（BR） treatments, which indicated that the biosynthesis of these hormones was not deficient in Gmdwf1.

关键词： soybean dwarf mutant mapping BR GA