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A lateral-immobilization zebrafish microfluidic chip-based system for in vivo real-time evaluation of antithrombotic agents

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Chinese Chemical Letters 2024年第3期35卷 312-315页

作者： lijuan He Hongxia Du Yi Yang Zhihua Guan jinjin li Honglin li Xudong lin lili Zhu Shanghai Key Laboratory of New Drug Design School of PharmacyEast China University of Science&TechnologyShanghai 200237China Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument School of Biomedical EngineeringShenzhen Campus of Sun Yat-sen UniversityShenzhen 518000China Innovation Center for AI and Drug Discovery East China Normal UniversityShanghai 200062China

Thrombosis remains a major global health concern mainly characterized by high rates of morbidity and mortality.Animal models serve as an indispensable tool to understand the underlying pathogenesis of thrombosis and assess the efficacy of novel antithrombotic drugs.Currently,zebrafish has emerged as a valuable model organism for thrombosis research.However,the traditional method of studying zebrafish thrombosis requires a laborious and time-consuming procedure,including anesthesia and manual immobilization of zebrafish.In this study,based on hydrodynamic force,a lateral-immobilization zebrafish microfluidic chip(liZMC)was designed to evaluate the cardiovascular system of multiple larvae within a single microscope field of view.Specifically,coupling with microscope imaging,real-time monitoring of the peripheral blood circulation in the tail of phenylhydrazine(PHZ)-induced zebrafish thrombosis was enabled.Furthermore,the reliability of liZMC for in vivo evaluation of antithrombotic agents in zebrafish was verified using aspirin.Collectively,this novel liZMC-based system can be used for in vivo zebrafish thrombosis studies and rapid screening of antithrombotic agents.

关键词： Microfluidic chip Zebrafish Thrombosis In vivo evaluation Real-time monitor

Extremely low friction on gold surface with surfactant molecules induced by surface potential

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Friction 2023年第4期11卷 513-523页

作者： Tianyang GAO jinjin li Weiqi WANG Jianbin LUO State Key Laboratory of Tribology Tsinghua UniversityBeijing 100084China

An extremely low friction state was observed on the gold surface induced by applying a specific negative potential in cationic surfactant solution.The friction force showed a remarkable reduction from 8.3 to 3.5×10−2 nN(reduced by 99.6%)with increasing the period of negative applied potential,and the final friction coefficient could reduce down to 3×10−4.The extremely low friction state was robust,and it also exhibited an excellent load bearing capacity,which cannot be damaged by a high load.Moreover,the extremely low friction state achieved under negative applied potential could keep stable even after the removal of potential,but failed in a short time,once a specific positive potential was applied.It was demonstrated that there was a stable electro-adsorption of surfactant molecules on the gold surface induced by applying a negative potential,leading to the formation of a bilayer structure on the gold surface.The hydration layers of the bilayer on the gold surface and micelles on the silica probe provided a shear plane with an extremely low shear strength,leading to the extremely low friction state on the gold surface.This study provides a method to achieve extremely low friction state by applied potential.

关键词： extremely low friction applied potential surfactant load bearing capacity

Deep Learning Accelerates the Discovery of Two- Dimensional Catalysts for Hydrogen Evolution Reaction

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Energy & Environmental Materials 2023年第1期6卷 138-144页

作者： Sicheng Wu Zhilong Wang Haikuo Zhang Junfei Cai jinjin li National Key Laboratory of Science and Technology on Micro/Nano Fabrication Shanghai Jiao Tong UniversityShanghai 200240China Department of Micro/Nano Electronics School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Two-dimensional materials with active sites are expected to replace platinum as large-scale hydrogen production catalysts.However,the rapid discovery of excellent two-dimensional hydrogen evolution reaction catalysts is seriously hindered due to the long experiment cycle and the huge cost of high-throughput calculations of adsorption energies.Considering that the traditional regression models cannot consider all the potential sites on the surface of catalysts,we use a deep learning method with crystal graph convolutional neural networks to accelerate the discovery of high-performance two-dimensional hydrogen evolution reaction catalysts from two-dimensional materials database,with the prediction accuracy as high as 95.2%.The proposed method considers all active sites,screens out 38 high performance catalysts from 6,531 two-dimensional materials,predicts their adsorption energies at different active sites,and determines the potential strongest adsorption sites.The prediction accuracy of the two-dimensional hydrogen evolution reaction catalysts screening strategy proposed in this work is at the density-functional-theory level,but the prediction speed is 10.19 years ahead of the high-throughput screening,demonstrating the capability of crystal graph convolutional neural networks-deep learning method for efficiently discovering high-performance new structures over a wide catalytic materials space.

关键词： crystal graph convolutional neural network deep learning hydrogen evolution reaction two-dimensional(2D)material

Blockchain in energy systems:values,opportunities,and limitations

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Frontiers in Energy 2022年第1期16卷 9-18页

作者： Sijie CHEN Jian PING Zheng YAN jinjin li Zhen HUANG Key Laboratory of Control of Power Transmission and Conversion(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory for Thin Film and Microfabrication(Ministry of Education) Department of Micro/Nano-electronicsShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory for Power Machinery and Engineering(Ministry of Education) Shanghai Jiao Tong UniversityShanghai 200240China

The structure of a power energy system is becoming more distributed than before.It becomes challenging to manage such a system in a centralized way,because a central authority may not exist or may not be trusted by all parties.Blockchain is a promising tool to address this challenge,by enabling trusted collaboration in the absence of a trusted central authority.Its use in the energy sector has been pioneered by several pilot projects.However,to date the energy sector has not seen large-scale deployment of blockchain,partly because the founders of those pilot projects,the public,and utilities have not reached consensus on the values and limitations of blockchain in energy.This perspective aims to bridge this gap.First,the philosophy and unique values of blockchain are discussed.Second,some promising blockchain-based applications in energy systems are presented.Third,some common misunderstandings of blockchain in energy are discussed.Last,some frequently-asked questions from utilities are discussed.Hopefully this perspective can help advance large-scale deployment of blockchain in energy systems.

关键词： blockchain immutability energy trading energy management data synchronization

Vision for energy material design:A roadmap for integrated data-driven modeling

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Journal of Energy Chemistry 2022年第8期31卷 56-62,I0003页

作者： Zhilong Wang Yanqiang Han Junfei Cai An Chen jinjin li Key Laboratory of Thin Film and Microfabrication of Ministry of Education Department of Micro/Nano ElectronicsShanghai Jiao Tong UniversityShanghai 200240China

The application scope and future development directions of machine learning models(supervised learning, transfer learning, and unsupervised learning) that have driven energy material design are discussed.

关键词： Energy materials Material attributes Machine learning Data driven

An ensemble learning classifier to discover arsenene catalysts with implanted heteroatoms for hydrogen evolution reaction

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Journal of Energy Chemistry 2023年第3期78卷 268-276,I0008页

作者： An Chen Junfei Cai Zhilong Wang Yanqiang Han Simin Ye jinjin li Key Laboratory for Thin Film and Microfabrication of Ministry of Education Department of Micro/Nano-electronicsShanghai Jiao Tong UniversityShanghai 200240China

Accurate regulation of two-dimensional materials has become an effective strategy to develop a wide range of catalytic applications.The introduction of heterogeneous components has a significant impact on the performance of materials,which makes it difficult to discover and understand the structure-property relationships at the atomic level.Here,we developed a novel and efficient ensemble learning classifier with synthetic minority oversampling technique(SMOTE) to discover all possible arsenene catalysts with implanted heteroatoms for hydrogen evolution reaction(HER).A total of 850 doped arsenenes were collected as a database and 140 modified arsenene materials with different doping atoms and doping sites were identified as promising candidate catalysts for HER,with a machine learning prediction accuracy of 81%.Based on the results of machine learning,we proposed 13 low-cost and easily synthesized two-dimensional Fe-doped arsenene catalytic materials that are expected to contribute to high-efficient HER.The proposed ensemble method achieved high prediction accuracy,but millions of times faster to predict Gibbs free energies and only required a small amount of data.This study indicates that the presented ensemble learning classifier is capable of screening high-efficient catalysts,and can be further extended to predict other two-dimensional catalysts with delicate regulation.

关键词： Ensemble learning Implanted heteroatoms Hydrogen evolution reaction Synthetic minority oversampling technique

An end-to-end artificial intelligence platform enables real-time assessment of superionic conductors

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SmartMat 2023年第6期4卷 56-71页

作者： Zhilong Wang Yanqiang Han Junfei Cai An Chen jinjin li National Key Laboratory of Science and Technology on Micro/Nano Fabrication Shanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Thin Film and Microfabrication of Ministry of Education Department of Micro/Nano ElectronicsShanghai Jiao Tong UniversityShanghaiChina

Superionic conductors(SCs)exhibiting low ion migration activation energy(Ea)are critical to the performance of electrochemical energy storage devices such as solid-state batteries and fuel cells.However,it is challenging to obtain Ea experimentally and theoretically,and the artificial intelligence(AI)method is expected to bring a breakthrough in predicting Ea.Here,we proposed an AI platform(named AI-IMAE)to predict the Ea of cation and anion conductors,including li^(+),Na^(+),Ag^(+),Al^(3+),Mg^(2+),Zn^(2+),Cu^((2)+),F^(−),and O^(2−),which is~105 times faster than traditional methods.The proposed AI-IMAE is based on crystal graph neural network models and achieves a holistic average absolute error of 0.19 eV,a median absolute error of 0.09 eV,and a Pearson coefficient of 0.92.Using AI-IMAE,we rapidly discovered 316 promising SCs as solid-state electrolytes and 129 SCs as cathode materials from 144,595 inorganic compounds.AI-IMAE is expected to completely solve the challenge of time-consuming Ea prediction and blaze a new trail for large-scale studies of SCs with excellent performance.As more experimental and high-precision theoretical data become available,AI-IMAE can train custom models and transfer the existing models to new models through transfer learning to constantly meet more demands.

关键词： artificial intelligence cathode material solid-state battery solid-state electrolyte superionic conductor

Understory plant diversity and phenolic allelochemicals across a range of Eucalyptus grandis plantation ages

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林业研究:英文版 2023年第5期34卷 1577-1590页

作者： jinjin li Yumei Huang lianghua Chen Shun Gao Jian Zhang Danju Zhang Sichuan Provincial Key Laboratory of Ecological Forestry Engineering College of ForestrySichuan Agricultural UniversityWenjiang 611130ChengduChina College of Landscape Architecture Sichuan Agricultural UniversityWenjiang 611130ChengduChina

Allelopathy is an important mechanism in Eucalyptus plantations that causes detrimental impacts on understory diversity.Phenolic compounds are the main allelochemicals suppressing understory plants.However,the dynamic changes in phenolic allelochemicals and their relationship with understory diversity with increasing age of Eucalyptus plantations remain largely unclear.In this study,the understory plant diversity was assessed and phenolic compounds identified from leaf litter,roots,and rhizosphere soil samples in a Eucalyptus grandis plantation at two-year intervals for ten years using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS).The abundance and diversity of under story plant species were lowest in 4-year-old plantations and increased significantly with age.Seven phenolic acids and 10 flavonoids were identified from leaf litter,roots,and rhizosphere soils.Most of the potential phenolic allelochemicals,such as salicylic acid,gallic acid,4-hydroxybenzoic acid,and epicatechin,were more abundant in younger plantations,especially at4 years old.The concentrations of phenolic compounds in the rhizosphere zone were significantly lower than in litter and root samples and did not change significantly with an increase in age.Notably,phenolic compounds contributed more to the variation in the understory plants than soil factors.Hydroxyphenyllactic acid,ellagic acid,quercetin,salicylic acid,and 4-hydroxybenzoic acid were the main phenolic compounds explaining the variation in plant diversity with plantation age.These findings indicate that young E.grandis plantations,especially at four years of age,merit a greater focus because of their lower understory plant diversity and higher allelopathic potential.

关键词： Eucalyptus grandis Phenolic compounds Understory plant diversity Plantation age

Two-dimensional molybdenum carbide(MXene)as an efficient nanoadditive for achieving superlubricity under ultrahigh pressure

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Friction 2023年第3期11卷 369-382页

作者： Shuang YI Yitong GUO jinjin li Yuxin ZHANG Aiguo ZHOU Jianbin LUO State Key Laboratory of Tribology Tsinghua UniversityBeijing 100084China College of Materials Science and Engineering Chongqing UniversityChongqing 400044China Henan Key Laboratory of Materials on Deep-Earth Engineering Henan Polytechnic University Jiaozuo 454003China

In this study,a robust macroscale liquid superlubricity with a coefficient of friction of 0.004 was achieved by introducing molybdenum carbide(Mo_(2)CT_(x))MXene nanoparticles as lubricating additives in a lithium hexafluorophosphate-based ionic liquid at Si_(3)N_(4)-sapphire interfaces.The maximal contact pressure in the superlubricity state could reach 1.42 GPa,which far exceeds the limit of the superlubricity regime in previous studies.The results indicate that a composite tribofilm(mainly containing molybdenum oxide and phosphorus oxide)that formed at the interface by a tribochemical reaction contributed to the excellent antiwear performance.Furthermore,the extremely low shear strength of the tribofilm and the interlayers of Mo_(2)CT_(x)MXene contributed to the superlubricity.This work demonstrates the promising potential of Mo_(2)CT_(x)MXene in improving superlubricity properties,which could accelerate the application of superlubricity in mechanical systems.

关键词： molybdenum carbide(Mo_(2)CT_(x))MXene superlubricity additives ultrahigh pressure wear resistance