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Tunable Multicolor Circularly Polarized Luminescence via Co-assembly of One Chiral Electron Acceptor with Various Donors

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CCS Chemistry 2023年第7期5卷 1592-1601页

作者： Fang Wang chengshuo shen Fuwei Gan Guoli Zhang Huibin Qiu School of Chemistry and Chemical Engineering Zhangjiang Institute for Advanced StudyFrontiers Science Center for Transformative MoleculesState Key Laboratory of Metal Matrix CompositesShanghai Jiao Tong UniversityShanghai 200240 Key Laboratory of Specially Functional Polymeric Materials and Related Technology(Ministry of Education) School of Material Science and EngineeringEast China University of Science and TechnologyShanghai 200237

Chiroptical materials with multicolor and signinvertible circularly polarized luminescence(CPL)are important for advanced optical devices and display ***,a general strategy is developed to generate CPL with highly tunable emission bands and handedness through charge-transfer(CT)complexation and co-assembly of one chiral non-emissive tetranitrofluorene-based acceptor with various achiral purple to blue emissive *** resulting assemblies exhibit intense CPL with a rich array of colors(519-668 nm)and prominent dissymmetry factors(|glum|)in the range of 10^(-3)-10^(-2).Notably,the CPL sign can be readily inverted by slightly changing the molecular structure of achiral ***-crystal analysis reveals that the donor and acceptor molecules are alternately and asymmetrically packed in a lamellar fashion through CT interactions,leading to efficient transfer of ***,the refined packing is mediated by the intensity and manner of CT interactions,rendering an inversion of *** chiral co-assembly not only occurs for planar achiral donor molecules,but is also accessible to nonplanar conjugated molecules such as[4]helicene ***,the CPL feature of the resulting products can be easily and broadly manipulated,aiming at advanced chiroptical systems.

关键词： circularly polarized luminescence charge transfer chirality co-assembly supramolecular chemistry

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Helical Conformation Tunability via Hydrogen Bonding in Supramolecular Frameworks

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CCS Chemistry 2022年第4期4卷 1405-1413页

作者： Zhijie Xue Fuwei Gan Hong Liu chengshuo shen Huibin Qiu Bo Yang Ping Yu School of Physical Science and Technology ShanghaiTech University201210 Shanghai School of Chemistry and Chemical Engineering State Key Laboratory of Metal Matrix CompositesShanghai Jiao Tong University200240 Shanghai

Hydrogen-bonded molecules and their dynamics are significantly important in chemistry and biology due to their widespread *** their natural abundance and diversity,applications of molecules with dynamic conformations in artificial networks allow information storage and molecular motor design on the nanometer ***,we report hydrogen-bonded molecular networks with tunable helical conformation on metal *** dynamics of helical conformation in two-dimensional hydrogenbond networks are triggered and resolved by scanning probe microscopy at the single-molecule *** combinationwith theoretical calculations,the surfacespecific hydrogen bonds are identified as the origin of the dynamic helical *** results provide a distinctive access to molecular architecture with tunable helical conformation driven by hydrogenbond interaction on surfaces.

关键词： scanning probe microscope dynamic conformation hydrogen bond molecular frameworks DFT calculation

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Tunable construction of transition metal-coordinated helicene cages

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Chinese Chemical Letters 2021年第12期32卷 3988-3992页

作者： Yongle Ding chengshuo shen Fuwei Gan Jinghao Wang Guoli Zhang Lingling Li Mouhai Shu Bangshang Zhu Jeanne Crassous Huibin Qiu School of Chemistry and Chemical Engineering Frontiers Science Center for Transformative MoleculesState Key Laboratory of Metal Matrix CompositesShanghai Jiao Tong UniversityShanghai 200240China Instrumental Analysis Center Shanghai Jiao Tong UniversityShanghai 200240China Univ Rennes Institut des Sciences Chimiques de RennesUMR CNRS 6226Campus de BeaulieuRennes 35042France

We report a facile and tailored method to prepare globally twisted chiral molecular cages through tunable coordination of bis-bipyridine-terminated helicene ligands to a series of transition metals including Fe(Ⅱ), Co(Ⅱ), Ni(Ⅱ) and Zn(Ⅱ). This system shows an efficient remote transfer of stereogenecity from the helicene core to the bipyridine-metal coordination sites and subsequently the entire cages. While the Fe(Ⅱ), Co(Ⅱ) and Ni(Ⅱ)-derived M_(2)L_(3)(M for metal and L for ligand) cages exhibit quasi-reversible redox features, the Zn(Ⅱ) analogues reveal prominent yellow circularly polarized luminescence. Interestingly,with the addition of Na_(2)SO_(4), the Zn_(2)L_(3)cages reassemble into sextuple-stranded Zn_(6)L_(6)(SO_(4))_(4)cages in which three Zn_(2)L_(2) units are bound together by four sulfates and further coalesced by offset inter-ligandπ-π interactions.

关键词： Helicene Coordination cage Chiral Global helicity Circularly polarized luminescence

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两亲聚肽/螺烯的电荷复合自组装行为研究

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有机化学 2019年第10期39卷 2973-2979页

作者：王婧琳沈程硕唐颂超姚远华东理工大学材料科学与工程学院高分子材料与工程教研室上海200237 上海交通大学化学化工学院上海200240

研究了包含聚肽嵌段的两亲共聚物聚乙二醇-b-聚(L-谷氨酸)(PEG-b-PGlu)与4-甲基-4-氮杂[4]螺烯鎓离子(Me[4]H)的电荷复合自组装行为,讨论了聚肽链段长度、谷氨酸单元与螺烯物质的量比、pH值等因素对组装体形貌的影响.研究发现,当刚-柔... 详细信息

研究了包含聚肽嵌段的两亲共聚物聚乙二醇-b-聚(L-谷氨酸)(PEG-b-PGlu)与4-甲基-4-氮杂[4]螺烯鎓离子(Me[4]H)的电荷复合自组装行为,讨论了聚肽链段长度、谷氨酸单元与螺烯物质的量比、pH值等因素对组装体形貌的影响.研究发现,当刚-柔型嵌段聚合物与刚性、大尺寸并具有诱导作用的小分子共同组装时,遵循与柔性大分子电荷复合组装完全不同的规律.PEG-b-PGlu/Me[4]H的电荷复合组装体形貌由π-π堆叠作用、两亲嵌段聚肽的亲疏水比、PGlu链段的刚硬程度共同决定,这些作用在组装过程中相互制约,当π-π堆叠作用占主导地位时,得到以指针状和条带状等平面结构为主的组装形貌.

关键词：电荷复合自组装两亲嵌段共聚物螺烯

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Magnetic diagnostics for magnetohydrodynamic instability research and the detection of locked modes in J-TEXT

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Plasma Science and Technology 2021年第5期23卷 49-58页

作者：韩东良沈呈硕王能超李达毛飞越任正康丁永华 the J-TEXT Team International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China

Magnetohydrodynamic(MHD)instabilities are widely observed during tokamak plasma *** diagnostics provide important information which supports the understanding and control of MHD *** paper presents the current status of the magnetic diagnostics dedicated to measuring MHD instabilities at the J-TEXT tokamak;the diagnostics consist of five Mirnov probe arrays for measuring high-frequency magnetic perturbations and two saddle-loop arrays for low-frequency magnetic perturbations,such as the locked *** recent years,several changes have been made to these *** structure of the probes in the poloidal Mirnov arrays has been optimized to improve their mechanical strength,and the number of in-vessel saddle loops has also been improved to support better spatial *** to the installation of high-field-side(HFS)divertor targets in early 2019,some of the probes were removed,but an HFS Mirnov array was designed and installed behind the *** to its excellent toroidal symmetry,the HFS Mirnov array has,for the first time at J-TEXT,provided valuable new information about the locked mode and the quasi-static mode(QSM)in the ***,various groups of magnetic diagnostics at different poloidal locations have been systematically used to measure the QSM,which confirmed the poloidal mode number m and the helical structure of the *** including the HFS information,the 2/1 resonant magnetic perturbation(RMP)-induced locked mode was measured to have a poloidal mode number m of~2.

关键词： J-TEXT tokamak locked mode magnetic diagnostics magnetohydrodynamic instabilities

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An application of the shortest path algorithm for the identification of weak MHD mode

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Plasma Science and Technology 2023年第8期25卷 19-27页

作者：黄卓毛飞越丁永华田微黄名响李达沈呈硕王能超梁云峰 the J-TEXT Team College of Computer Science South-Central Minzu UniversityWuhan 430074People's Republic of China International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China Forschungszentrum Jülich GmbH Institut für Energie-und Klimaforschung-PlasmaphysikPartner of the Trilateral Euregio Cluster(TEC)Jülich 52425Germany 不详

The identification of magnetohydrodynamic(MHD)modes is a crucial issue in the control of magnetically confined *** paper proposes a novel method for identifying the evolution of MHD modes from a signal with a low signal-to-noise *** proposed method generates a weighted directed graph from the time-frequency spectrum and calculates the evolution of the mode frequency by solving the shortest *** method addresses the limitations posed by the lack of data channels and the disturbance of noise in the estimation of mode frequency and yields much better results compared to traditional *** is demonstrated that,using this method,the evolution of an unlocked tearing mode was more accurately calculated on the J-TEXT *** method remains feasible even with a low signal-to-noise ratio of 0.5,as shown by its ***,with appropriate parameters,this method can be applied to not only signals with MHD modes,but also to general signals with continuous modes.

关键词： plasma physics magnetohydrodynamic pattern recognition

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Disruption prediction based on fusion feature extractor on J-TEXT

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Chinese Physics B 2023年第7期32卷 12-23页

作者：郑玮薛凤鸣陈忠勇沈呈硕艾鑫坤钟昱王能超张明丁永华陈志鹏杨州军潘垣 International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074China Institute of Artificial Intelligence Huazhong University of Science and TechnologyWuhan 430074China

Predicting disruptions across different tokamaks is necessary for next generation *** large-scale tokamaks can hardly tolerate disruptions at high performance discharge,which makes it difficult for current data-driven methods to obtain an acceptable result.A machine learning method capable of transferring a disruption prediction model trained on one tokamak to another is required to solve the *** key is a feature extractor which is able to extract common disruption precursor traces in tokamak diagnostic data,and can be easily transferred to other *** on the concerns above,this paper presents a deep feature extractor,namely,the fusion feature extractor(FFE),which is designed specifically for extracting disruption precursor features from common diagnostics on ***,an FFE-based disruption predictor on J-TEXT is *** feature extractor is aimed to extracting disruption-related precursors and is designed according to the precursors of disruption and their representations in common tokamak *** inductive bias on tokamak diagnostics data is *** paper presents the evolution of the neural network feature extractor and its comparison against general deep neural networks,as well as a physics-based feature extraction with a traditional machine learning *** demonstrate that the FFE may reach a similar effect with physics-guided manual feature extraction,and obtain a better result compared with other deep learning methods.

关键词： feature extractor disruption prediction deep learning tokamak diagnostics

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Machine learning application to predict the electron temperature on the J-TEXT tokamak

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Plasma Science and Technology 2021年第8期23卷 47-52页

作者：董蛟龙李建超丁永华张晓卿王能超李达严伟沈呈硕何莹任颉颃夏冬辉 the J-TEXT Team International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of TechnologyWuhan 430205People's Republic of China

The reliability of diagnostic systems in tokamak plasma is of great significance for physics researches or fusion *** some diagnostics fail to detect information about the plasma status,such as electron temperature,they can also be obtained by another method:fitted by other diagnostic signals through machine *** paper herein is based on a machine learning method to predict electron temperature,in case the diagnostic systems fail to detect plasma *** fully-connected neural network,utilizing back propagation with two hidden layers,is utilized to estimate plasma electron temperature approximately on the *** input parameters consist of soft x-ray emission intensity,electron density,plasma current,loop voltage,and toroidal magnetic field,while the targets are signals of electron temperature from electron cyclotron emission and x-ray imaging crystal ***,the temperature profile is reconstructed by other diagnostic signals,and the average errors are within 5%.In addition,generalized regression neural network can also achieve this function to estimate the temperature profile with similar *** electron temperature by neural network reveals that machine learning can be used as backup means for plasma information so as to enhance the reliability of diagnostics.

关键词： neural network plasma electron temperature J-TEXT tokamak

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The spectrum analysis of RMP coils with multiple connection modes and the design of high field side coils on J-TEXT

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Plasma Science and Technology 2022年第12期24卷 224-231页

作者：任正康李达王能超毛飞越黄卓周松贾若何莹沈呈硕 Abba Alhaji BALA 饶波丁永华 the J-TEXT Team International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China College of Computer Science South-Central Minzu UniversityWuhan 430074People's Republic of China Physics Department Federal University DutseDutse 720101Nigeria

The phase difference Δξ between locked islands(2/1 and 3/1) has been found to influence the heat transport on the thermal quench during disruptions by numerical modeling [Hu Q et al *** 59,016005].To verify this experimentally,a set of resonant magnetic perturbation(RMP) coils is required to excite coupled magnetic islands with different Δξ.The spectrum analysis shows that the current RMP coils on J-TEXT can only produce sufficient 2/1 and 3/1RMP fields with a limited phase difference of Δξ∈[-75°,75°].In order to broaden the adjustable range of Δξ,a set of coils on the high field side(HFS) is proposed to generate 2/1 and 3/1 RMP fields with Δξ=180°.As a result,RMPs with adjustable Δξ∈[-180°,180°] and sufficient amplitudes could be achieved by applying the HFS coils and the low field side(LFS)*** work provides a feasible solution for flexible adjustment of the phase difference between m and m+1 RMP,which might facilitate the study of major disruptions and their control.

关键词： resonant magnetic perturbation spectrum analysis phase difference between 2/1 and 3/1 islands

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Overview of machine learning applications in fusion plasma experiments on J-TEXT tokamak

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Plasma Science and Technology 2022年第12期24卷 27-38页

作者：郑玮薛凤鸣沈呈硕钟昱艾鑫坤陈忠勇丁永华张明杨州军王能超张智超董蛟龙唐畴尧潘垣 International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and TechnologySchool of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhan 430074People's Republic of China Institute of Artificial Intelligence Huazhong University of Science and TechnologyWuhan 430074People's Republic of China

Machine learning research and applications in fusion plasma experiments are one of the main subjects on *** 2013,various kinds of traditional machine learning,as well as deep learning methods have been applied to fusion plasma *** applications in the real-time experimental environment have proved the feasibility and effectiveness of the *** disruption prediction,we started by predicting disruptions of limited classes with a short warning time that could not meet the requirements of the mitigation *** years of study,nowadays disruption prediction methods on J-TEXT are able to predict all kinds of disruptions with a high success rate and long enough warning ***,cross-device disruption prediction methods have obtained promising *** analysis of the models are *** diagnostics data processing,efforts have been made to reduce manual work in processing and to increase the robustness of the diagnostic *** based on both traditional machine learning and deep learning have been applied to real-time experimental *** models have been cooperating with the plasma control system and other systems,to make joint decisions to further support the experiments.

关键词： machine learning disruption prediction diagnostics data processing J-TEXT

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