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Novel strategy of multidimensional information encryption via multi-color carbon dots aggregation-induced emission

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Nano Research 2024年第6期17卷 5680-5687页

作者： Liu Ding Xilang jin Yuchong Gao Shouwang Kang Haiyan Bai Xuehao Ma Taotao Ai hongwei Zhou Weixing Chen Shaanxi Key Laboratory of Photoelectric Functional Materials and Devices Engineering Research Center of Light Stabilizers for Polymer Materials Universities of Shaanxi Province School of Materials and Chemical EngineeringXi’an Technological UniversityXi’an 710021China School of Material Science and Engineering Shaanxi University of TechnologyHanzhong 723000China Department of Materials Science and Engineering University of PennsylvaniaPhiladelphia 19104USA

Carbon dots(CDs)with aggregation-induced emission(AIE)have sparked significant interest in multidimensional anti-counterfeiting due to their exceptional fluorescence properties.However,the preparation of AIE CDs with multicolor solid-state fluorescence remains a formidable challenge due to its complicated construction.In the present work,a novel class of multicolor AIE CDs(M-CDs)were fabricated using selected precursor(salicylic acid,thiosalicylic acid,and 2,2'-dithiodibenzoic acid),with an eco-friendly,low-cost one-pot solvothermal method.In the dilute organic solution,M-CDs manifested blue emission,but upon aggregation in the presence of water,the red,yellow,green,and blue emissions were displayed due to the AIE effect.Structural analysis,coupled with theoretical calculations,revealed that the increase in the size of sp2 domains would lower the Eg and cause a red-shift emission wavelength.Significantly,the continuous emission of M-CDs from blue to red can be utilized as ink for multimode printing,enabling the creation of a variety of school badges and quick response codes.These findings hold promising implications for multi-information encryption applications.

关键词： aggregation-induced emission(AIE) luminescence mechanism multi-color emission information encryption.

Cooperative prediction method of gas emission from mining face based on feature selection and machine learning

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International Journal of Coal Science & Technology 2022年第4期9卷 135-146页

作者： Jie Zhou Haifei Lin hongwei jin Shugang Li Zhenguo Yan Shiyin Huang College of Safety Science and Engineering Xi’an University of Science and TechnologyXi’an 710054China Coal Industry Engineering Research Center for Western Mine Gas Intelligent Extraction Xi’an 710054China

Collaborative prediction model of gas emission quantity was built by feature selection and supervised machine learning algorithm to improve the scientifc and accurate prediction of gas emission quantity in the mining face.The collaborative prediction model was screened by precision evaluation index.Samples were pretreated by data standardization,and 20 characteristic parameter combinations for gas emission quantity prediction were determined through 4 kinds of feature selection methods.A total of 160 collaborative prediction models of gas emission quantity were constructed by using 8 kinds of classical supervised machine learning algorithm and 20 characteristic parameter combinations.Determination coefcient,normalized mean square error,mean absolute percentage error range,Hill coefcient,mean absolute error,and the mean relative error indicators were used to verify and evaluate the performance of the collaborative forecasting model.As such,the high prediction accuracy of three kinds of machine learning algorithms and seven kinds of characteristic parameter combinations were screened out,and seven optimized collaborative forecasting models were fnally determined.Results show that the judgement coefcients,normalized mean square error,mean absolute percentage error,and Hill inequality coefcient of the 7 optimized collaborative prediction models are 0.969–0.999,0.001–0.050,0.004–0.057,and 0.002–0.037,respectively.The determination coefcient of the fnal prediction sequence,the normalized mean square error,the mean absolute percentage error,the Hill inequality coefcient,the absolute error,and the mean relative error are 0.998%,0.003%,0.022%,0.010%,0.080%,and 2.200%,respectively.The multi-parameter,multi-algorithm,multi-combination,and multijudgement index prediction model has high accuracy and certain universality that can provide a new idea for the accurate prediction of gas emission quantity.

关键词： Gas emission prediction Machine learning Feature selection Cooperative prediction

Multifunctional interfacial molecular bridge enabled by an aggregation-induced emission strategy for enhancing efficiency and UV stability of perovskite solar cells

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Journal of Energy Chemistry 2024年第8期95卷 588-595页

作者： Shuhang Bian Yuqi Wang Fancong Zeng Zhongqi Liu Bin Liu Yanjie Wu Long Shao Yongzhi Shao Huan Zhang Shuainan Liu jin Liang Xue Bai Lin Xu Donglei Zhou Biao Dong hongwei Song State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering Jilin University College of Science Shanghai University

The interface defects between the electron transport layer（ETL） and the perovskite layer,as well as the low ultraviolet（UV） light utilization rate of the perovskite absorption layer,pose significant challenges for the commercialization of perovskite solar cells（PSCs）.To address this issue,this paper proposes an innovative multifunctional interface modulation strategy by introducing aggregation-induced emission（AIE） molecule 5-[4-[1,2,2-tri[4-（3,5-dicarboxyphenyl）phenyl]ethylene] phenyl]benzene-1,3-dicarboxylic acid（H8ETTB） at the SnO2ETL/perovskite interface.Firstly,the interaction of H8ETTB with the SnO2surface,facilitated by its carboxyl groups,is effective in passivating surface defects caused by noncoord inated Sn and O vacancies.This interaction enhances the conductivity of the SnO2film and adjusts energy levels,leading to enhanced charge carrier transport.Simultaneously,H8ETTB can passivate noncoord inated Pb2+ions at the perovskite interface,promoting perovskite crystallization and reducing the interface energy barrier,resulting in a perovskite film with low defects and high crystalline quality.More importantly,the H8ETTB molecule,can convert UV light into light absorbable by the perovskite,thereby reducing damage caused by UV light and improving the device's utilization of UV.Consequently,the champion PSC based on SnO2-H8ETTB achieves an impressing efficiency of 23.32%and significantly improved photostability compared with the control device after continuous exposure to intense UV radiation.In addition,the Cs0.05(FA0.95MA0.05)0.95Pb(I0.95Br0.05)3based device can achieve maximum efficiency of 24.01%,demonstrating the effectiveness and universality of this strategy.Overall,this innovative interface bridging strategy effectively tackles interface defects and low UV light utilization in PSCs,presenting a promising approach for achieving highly efficient and stable PSCs.

关键词： Aggregation-induced emission Defect passivation Efficiency Perovskite solar cells UV stability

Measurement and model of density,viscosity,and hydrogen sulfide solubility in ferric chloride/trioctylmethylammonium chloride ionic liquid

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中国化学工程学报:英文版 2023年第7期59卷 210-221页

作者： Zhonghao Li Yuanyuan Yang Huanong Cheng Yun Teng Chao Li Kangkang Li Zhou Feng hongwei jin Xinshun Tan Shiqing Zheng College of Chemical Engineering Qingdao University of Science and TechnologyQingdao 266042China

The density and viscosity of ferric chloride/trioctylmethylammonium chloride ionic liquid(rFeCl_(3)/[A336]Cl)with different molar ratios(r=0.1-0.8)of FeCl_(3) to[A336]Cl were measured at temperatures from 313.15 to 358.15 K and atmospheric pressure.The density and viscosity data were fitted by the relevant temperature variation equations,respectively.The variation of density and viscosity with temperature and r was obtained.The solubility of rFeCl_(3)/[A336]Cl to H_2S was measured at temperatures from 318.15 to 348.15 K and pressures from 0 to 150 kPa.The effects of temperature,pressure,and r on the solubility of H_(2)S were discussed.The reaction equilibrium thermodynamic model(RETM)was used to fit the H_(2)S solubility data,and the average relative error was less than 1.3%,indicating that the model can relate the solubility data well.And Henry's constant and chemical reaction equilibrium constant were obtained by the RETM fitting.The relationships of Henry's constant and chemical reaction equilibrium constant with temperature and r were analyzed.

关键词： Ionic liquids Hydrogen sulfide Solubility Henry's constant Chemical reaction equilibrium constant Model

Fractional-order heterogeneous memristive Rulkov neuronal network and its medical image watermarking application

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Chinese Physics B 2024年第5期33卷 306-314页

作者：丁大为牛炎张红伟杨宗立王金王威王谋媛 School of Electronics and Information Engineering Anhui UniversityHefei 230601China

This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network(FRHNN),utilizing memristors for emulating neural synapses.The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams,Lyapunov exponents(LEs),and bifurcation diagrams.Secondly,the parameter related firing behaviors are described through two-parameter bifurcation diagrams.Subsequently,local attraction basins reveal multi-stability phenomena related to initial values.Moreover,the proposed model is implemented on a microcomputer-based ARM platform,and the experimental results correspond to the numerical simulations.Finally,the article explores the application of digital watermarking for medical images,illustrating its features of excellent imperceptibility,extensive key space,and robustness against attacks including noise and cropping.

关键词： fractional order memristors Rulkov neuron medical image watermarking

Coffee Flower Identification Using Binarization Algorithm Based on Convolutional Neural Network for Digital Images

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Plant Phenomics 2020年第1期2卷 299-313页

作者： Pengliang Wei Ting Jiang Huaiyue Peng hongwei jin Han Sun Dengfeng Chai jingfeng Huang Institute of Applied Remote Sensing and Information Technology Zhejiang UniversityHangzhou 310058China Jiangsu Radio Scientific Institute Co. Ltd.Wuxi 214073China Innovation Institute of Disaster Prevention and Reduction at Inner Mongolia Huhhot 010051China Institute of Spatial Information Technique Zhejiang UniversityHangzhou 310027China Electrical Engineering and Computer Science University of CaliforniaMercedCA 95343USA

Crop-type identification is one of the most significant applications of agricultural remote sensing,and it is important for yield estimation prediction and field management.At present,crop identification using datasets from unmanned aerial vehicle(UAV)and satellite platforms have achieved state-of-the-art performances.However,accurate monitoring of small plants,such as the coffee flower,cannot be achieved using datasets from these platforms.With the development of time-lapse image acquisition technology based on ground-based remote sensing,a large number of small-scale plantation datasets with high spatial-temporal resolution are being generated,which can provide great opportunities for small target monitoring of a specific region.The main contribution of this paper is to combine the binarization algorithm based on OTSU and the convolutional neural network(CNN)model to improve coffee flower identification accuracy using the time-lapse images(i.e.,digital images).A certain number of positive and negative samples are selected from the original digital images for the network model training.Then,the pretrained network model is initialized using the VGGNet and trained using the constructed training datasets.Based on the well-trained CNN model,the coffee flower is initially extracted,and its boundary information can be further optimized by using the extracted coffee flower result of the binarization algorithm.Based on the digital images with different depression angles and illumination conditions,the performance of the proposed method is investigated by comparison of the performances of support vector machine(SVM)and CNN model.Hence,the experimental results show that the proposed method has the ability to improve coffee flower classification accuracy.The results of the image with a 52.5°angle of depression under soft lighting conditions are the highest,and the corresponding Dice(F1)and intersection over union(IoU)have reached 0.80 and 0.67,respectively.

关键词： network image union

高温合金H80A钢的Al、Ti元素控制技术研究

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中国资源综合利用 2024年第4期42卷 47-50页

作者：靳红伟张宣科技特材研制公司河北张家口075100

高温合金具有优异的高温力学性能、组织稳定性和抗热腐蚀性能,可在高温与复杂应力的环境长期可靠工作,广泛用于制造航空、航天、舰船发动机的热端部件。以高温合金H80A钢为例,分析真空熔炼和电渣重熔过程的Al、Ti元素变化规律,然后提出... 详细信息

高温合金具有优异的高温力学性能、组织稳定性和抗热腐蚀性能,可在高温与复杂应力的环境长期可靠工作,广泛用于制造航空、航天、舰船发动机的热端部件。以高温合金H80A钢为例,分析真空熔炼和电渣重熔过程的Al、Ti元素变化规律,然后提出有效的Al、Ti元素控制措施。采取针对性措施后,H80A钢成分控制能力提高,产品质量大幅提升。

关键词：高温合金渣系铝钛元素控制

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H11Mo模具钢电渣锭补缩工艺应用研究

中国资源综合利用 2024年第5期42卷 82-84页

作者：靳红伟张宣科技特材研制公司河北张家口075100

张宣科技特材研制公司新开发H11Mo模具钢,但电渣锭头部的渣沟与缩孔较深,自耗电极剩余多。有必要分析电渣重熔热封顶阶段自动控制补缩工艺的不足,提出适合、有效的补缩工艺控制新方式。经实践验证,生产问题得以解决,切损量减少,成材率... 详细信息

张宣科技特材研制公司新开发H11Mo模具钢,但电渣锭头部的渣沟与缩孔较深,自耗电极剩余多。有必要分析电渣重熔热封顶阶段自动控制补缩工艺的不足,提出适合、有效的补缩工艺控制新方式。经实践验证,生产问题得以解决,切损量减少,成材率提高。

关键词：模具钢电渣锭补缩工艺热封顶渣沟缩孔

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浇覆温度对铝/钢复合板界面及性能的影响

稀有金属 2024年第4期48卷 487-497页

作者：李超李元东邱谨罗晓梅周宏伟兰州理工大学材料科学与工程学院甘肃兰州730050 兰州理工大学省部共建有色金属先进加工与再利用国家重点实验室甘肃兰州730050 兰州理工大学有色金属合金及加工教育部重点实验室甘肃兰州730050

选用6061铝合金和Q235钢板,采用铸轧工艺技术制备铝/钢双金属复合板,研究6061铝合金不同浇覆温度对铝/钢复合板界面组织以及力学性能的影响。使用光学显微镜(OM)和场发射扫描电子显微镜(SEM)观察复合板界面显微组织形貌,通过能谱仪(EDS)... 详细信息

选用6061铝合金和Q235钢板,采用铸轧工艺技术制备铝/钢双金属复合板,研究6061铝合金不同浇覆温度对铝/钢复合板界面组织以及力学性能的影响。使用光学显微镜(OM)和场发射扫描电子显微镜(SEM)观察复合板界面显微组织形貌,通过能谱仪(EDS)和X射线衍射仪(XRD)进行微区成分分析及分析界面金属间化合物,使用显微硬度仪测量界面附近硬度变化情况,并且对复合板力学性能进行测试。结果表明,随着浇覆温度从650℃升高到730℃,铝/钢复合板界面扩散层厚度逐渐变厚且均匀,界面处形成Al5Fe_(2)和Al_(3)Fe等金属间化合物;当浇覆温度为730℃时,扩散层厚度最厚为5.9μm,界面处显微硬度最高,达到HV 120。抗拉强度和剪切强度随着浇覆温度升高先提高后降低,当浇覆温度为710℃时,复合板的力学性能最好,复合板铝钢厚度比为2∶1,抗拉强度为233 MPa,相比较铸轧态6061铝合金,抗拉强度提高了66.4%,断裂延伸率为25.5%,剪切强度为92.5 MPa,剪切断裂发生在6061铝合金一侧。

关键词：铝/钢复合板浇覆温度铸轧金属间化合物力学性能