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| 1 | Permian integrative stratigraphy and timescale of China显示文摘A series of global major geological and biological events occurred during the Permian Period. Establishing a highresolution stratigraphic and temporal framework is essential to understand their cause-effect relationship. The official International timescale of the Permian System consists of three series(i.e., Cisuralian, Guadalupian and Lopingian in ascending order) and nine stages. In China, the Permian System is composed of three series(Chuanshanian, Yansingian and Lopingian) and eight stages, of which the subdivisions and definitions of the Chuanshanian and Yangsingian series are very different from the Cisuralian and Guadalupian series. The Permian Period spanned ~47 Myr. Its base is defined by the First Appearance Datum(FAD) of the conodont Streptognathodus isolatus at Aidaralash, Kazakhstan with an interpolated absolute age 298.9±0.15 Ma at Usolka, southern Urals, Russia. Its top equals the base of the Triassic System and is defined by the FAD of the conodont Hindeodus parvus at Meishan D section, southeast China with an interpolated absolute age 251.902±0.024 Ma. Thirty-five conodont, 23 fusulinid, 17 radiolarian and 20 ammonoid zones are established for the Permian in China, of which the Guadalupian and Lopingian conodont zones have been served as the standard for international correlation. The Permian δ13 Ccarbtrend indicates that it is characterized by a rapid negative shift of 3–5‰ at the end of the Changhsingian, which can be used for global correlation of the end-Permian mass extinction interval, but δ13 Ccarbrecords from all other intervals may have more or less suffered subsequent diagenetic alteration or represented regional or local signatures only. Permian δ18 Oapatitestudies suggest that an icehouse stage dominated the time interval from the late Carboniferous to Kungurian(late Cisuralian). However, paleoclimate began to ameriolate during the late Kungurian and gradually shifted into a greenhouse-dominated stage during the Guadalupian.The Changhsingian was a relatively cool stage, followed by a globally-recognizable rapid temperature rise of 8–10°C at the very end of the Changhsingian. The87 Sr/86 Sr ratio trend shows that their values at the beginning of the Permian were between 0.70800,then gradually decreased to the late Capitanian minimum 0.70680–0.70690, followed by a persistent increase until the end of the Permian with the value 0.70708. Magenetostratigraphy suggests two distinct stages separated by the Illawarra Reversal in the middle Wordian, of which the lower is the reverse polarity Kiaman Superchron and the upper is the mixed-polarity Illawarra Superchron. The end-Guadalupian(or pre-Lopingian) biological crisis occurred during the late Capitanian, when faunal changeovers of different fossil groups had different paces, but generally experienced a relatively long time from the Jinogondolella altudensis Zone until the earliest Wuchiapingian. The end-Permian mass extinction was a catastrophic event that is best constrained at the Meishan section, which occurred at 251.941±0.037 Ma and persisted no more than 61±48 kyr. After the major pulse at Bed 25, the extinction patterns are displayed differently in different sections. The global end-Guadalupian regression is manifested between the conodont Jinogondolella xuanhanensis and Clarkina dukouensis zones and the endChanghsingian transgression began in the Hindeodus changxingensis-Clarkina zhejiangensis Zone. The Permian Period is also characterized by strong faunal provincialism in general, which resulted in difficulties in inter-continental and inter-regional correlation of both marine and terrestrial systems. | Shuzhong SHEN Hua ZHANG Yichun ZHANG Dongxun YUAN Bo CHEN Weihong HE Lin MU Wei LIN Wenqian WANG Jun CHEN Qiong WU Changqun CAO Yue WANG Xiangdong WANG | 2019 | Science China Earth Sciences2019，62，1： | 9 |
| 2 | Single-atom Fe Embedded Co_(3)S_(4) for Efficient Electrocatalytic Oxygen Evolution Reaction显示文摘Constructing atomically dispersed active sites with densely exposed and dispersed double metal-Sx catalytic sites for favorable OER catalytic activity remains rare and challenging.Herein,we design and construct a Fe_(1)S_(x)@Co_(3)S_(4) electrocatalyst with Fe single atoms epitaxially confined in Co_(3)S_(4) nanosheets for catalyzing the sluggish alkaline oxygen evolution reaction(OER).Consequently,in ultralow concentration alkaline solutions(0.1 mol/L KOH),such a catalyst is highly active and robust for OER with low overpotentials of 300 and 333 mV at current densities of 10 and 30 mA/cm^(2),respectively,accompanying long-term stability without significant degradation even for 350 h.In addition,Fe_(1)S_(x)@Co_(3)S_(4) shows a turnover frequency(TOF)value of 0.18 s−1,nearly three times that of Co_(3)S_(4)(0.07 s−1),suggesting the higher atomic utilization of Fe single atoms.Mössbauer and in-situ Raman spectra confirm that the OER activity of Fe_(1)S_(x)@Co_(3)S_(4) origins from a thin catalytic layer of Co(Fe)OOH that interacts with trace-level Fe species in the electrolyte,creating dynamically stable active sites.Combined with experimental characterizations,it suggests that the most active S-coordinated dual-metal site configurations are 2S-bridged(Fe-Co)S4,in which Co-S and Fe-S moieties are shared with two S atoms,which can strongly regulate the adsorption energy of reaction intermediates,accelerating the OER reaction kinetics. | QI Yuxue LI Tingting HU Yajie XIANG Jiahong SHAO Wenqian CHEN Wenhua MU Xueqin LIU Suli CHEN Changyun YU Min MU Shichun | 2022 | Chemical Research in Chinese Universities2022，38，5： | 0 |
| 3 | Novel method for identifying wheat leaf disease images based on differential amplification convolutional neural network显示文摘In this study,a differential amplification convolutional neural network(DACNN)was proposed and used in the identification of wheat leaf disease images with ideal accuracy.The branches added between the deep convolutional layers can amplify small differences between the real output and the expected output,which made the weight updating more sensitive to the light errors return in the backpropagation pass and significantly improved the fitting capability.Firstly,since there is no large-scale wheat leaf disease images dataset at present,the wheat leaf disease dataset was constructed which included eight kinds of wheat leaf images,and five kinds of data augmentation methods were used to expand the dataset.Secondly,DACNN combined four classifiers:Softmax,support vector machine(SVM),K-nearest neighbor(KNN)and Random Forest to evaluate the wheat leaf disease dataset.Finally,the DACNN was compared with the models:LeNet-5,AlexNet,ZFNet and Inception V3.The extensive results demonstrate that DACNN is better than other models.The average recognition accuracy obtained on the wheat leaf disease dataset is 95.18%. | Mengping Dong Shaomin Mu Aiju Shi Wenqian Mu Wenjie Sun | 2020 | International Journal of Agricultural and Biological Engineering2020，13，4： | 0 |
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