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Advances in Triboelectric Nanogenerators for Blue Energy Harvesting and Marine Environmental Monitoring

Engineering 2024年第2期33卷 204-224页

作者： Yang jiang Xi Liang tao jiang Zhong Lin Wang CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro–Nano Energy and SensorsBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijing 101400China School of Nanoscience and Technology University of Chinese Academy of SciencesBeijing 100049China Georgia Institute of Technology AtlantaGA 30332USA

Blue energy,which includes rainfall,tidal current,wave,and water-flow energy,is a promising renewable resource,although its exploitation is limited by current technologies and thus remains low.This form of energy is mainly harvested by electromagnetic generators(EMGs),which generate electricity via Lorenz force-driven electron flows.Triboelectric nano genera tors(TENGs)and TENG networks exhibit superiority over EMGs in low-frequency and high-entropy energy harvesting as a new approach for blue energy harvesting.A TENG produces electrical outputs by adopting the mechanism of Maxwell’s displacement current.To date,a series of research efforts have been made to optimize the structure and performance of TENGs for effective blue energy harvesting and marine environmental applications.Despite the great progress that has been achieved in the use of TENGs in this context so far,continuous exploration is required in energy conversion,device durability,power management,and environmental applications.This review reports on advances in TENGs for blue energy harvesting and marine environmental monitoring.It introduces the theoretical foundations of TENGs and discusses advanced TENG prototypes for blue energy harvesting,including TENG structures that function in freestanding and contact-separation modes.Performance enhancement strategies for TENGs intended for blue energy harvesting are also summarized.Finally,marine environmental applications of TENGs based on blue energy harvesting are discussed.

关键词： Triboelectric nanogenerator TENG networks Blue energy Energy harvesting Ocean sensors

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Assembly of peptide nanostructures with controllable sizes

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Nano Research 2024年第1期17卷 151-161页

作者： Dan Cheng Fan Jia Yun-Bao jiang Vincent P.Conticello tao jiang The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation Department of ChemistryCollege of Chemistry and Chemical EngineeringXiamen UniversityXiamen 361005China Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province(IKKEM) Xiamen 361005China Department of Chemistry Emory UniversityAtlanta 30033USA

Controlled peptide assembly offers significant promise to develop synthetic supramolecular nanostructures to display material and biological properties that mimic protein assemblies in nature.Despite the progress in forming peptide nanostructures of various morphology,there exists a distinct gap between natural and synthetic assembly systems in terms of size control.Constructing nanostructures with a narrow size distribution that can be tuned over a wide range of length-scales is essential for applications that require precise spacing between objects.This approach provides the opportunity to correlate materials and biological properties of interest with assembly size.In this review,we discuss representative endeavors over the past two decades for design of size-controllable peptide nanostructures using tunable building blocks.Other mechanisms for size control,e.g.,molecular frustration,template-directed peptide assembly,and multi-component peptide co-assembly,will also be discussed.We also demonstrate the applicable scopes of these strategies and suggest potential future avenues for scientific advances in this field.

关键词： peptide assembly controllable size template multi-component

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Methods,Mechanisms,and Application Prospects for Enhancing Extracellular Vesicle Uptake

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Current Medical Science 2024年第2期44卷 247-260页

作者： Ying-peng XU tao jiang Xiao-fan YANG Zhen-bing CHEN Department of Hand Surgery Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China

Extracellular vesicles(EVs)are considered to be a new generation of bioinspired nanoscale drug delivery systems due to their low immunogenicity,natural functionality,and excellent biocompatibility.However,limitations such as low uptake efficiency,insufficient production,and inhomogeneous performance undermine their potential.To address these issues,numerous researchers have put forward various methods and applications for enhancing EV uptake in recent decades.In this review,we introduce various methods for the cellular uptake of EVs and summarize recent advances on the methods and mechanisms for enhancing EV uptake.In addition,we provide further understanding regarding enhancing EV uptake and put forward prospects and challenges for the development of EV-based therapy in the future.

关键词： extracellular vesicles exosomes extracellular vesicle uptake extracellular vesiclebased therapy

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Machine Learning-Aided Data Analysis in Single-Protein Conductance Measurement with Electron Tunneling Probes

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Chinese Journal of Chemistry 2024年第1期42卷 67-72页

作者： Yuxin Yang tao jiang Ye Tian Biaofeng Zeng Longhua Tang State Key Laboratoryof Extreme Photonics and Instrumentation College of Optical Science and EngineeringZhejiang University HangzhouZhejiang 310027China Nanhu Brain-computer Interface Institute HangzhouZhejiang 311100China State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical EngineeringZhejiang University HangzhouZhejiang 310027China

The electrical tunneling sensors have excellent potential in the next generation of single-molecule measurement and sequencing technologies due to their high sensitivity and spatial resolution capabilities.Electrical tunneling signals that have been measured at a high sampling rate may provide detailed molecular information.Despite the extraordinarily large amount of data that has been gathered,it is still difficult to correlate signal transformations with molecular processes,which creates great obstacles for signal analysis.Machine learning is an effective tool for data analysis that is currently gaining more significance.It has demonstrated promising results when used to analyze data from single-molecule electrical measurements.In order to extract meaningful information from raw measurement data,we have combined intelligent machine learning with tunneling electrical signals.For the purpose of analyzing tunneling electrical signals,we investigated the clustering approach,which is a classic algorithm in machine learning.A clustering model was built that combines the advantages of hierarchical clustering and Gaussian mixture model clustering.Additionally,customized statistical algorithms were designed.It has been proven to efficiently gather molecular information and enhance the effectiveness of data analysis.

关键词： Tunneling sensor Single-molecule measurement Machine learning Single-protein conductance Molecular electrochemistry Nanotechnology Molecular electronics

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Deep learning-based optical aberration estimation enables offline digital adaptive optics and super-resolution imaging

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Photonics Research 2024年第3期12卷 474-484页

作者： CHANG QIAO HAOYU CHEN RUN WANG tao jiang YUWANG WANG DONG LI Department of Automation Tsinghua UniversityBeijing 100084China Institute for Brain and Cognitive Sciences Tsinghua UniversityBeijing 100084China National Laboratory of Biomacromolecules New Cornerstone Science LaboratoryCAS Center for Excellence in BiomacromoleculesInstitute of BiophysicsChinese Academy of SciencesBeijing 100101China College of Life Sciences University of Chinese Academy of SciencesBeijing 100049China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China

Optical aberrations degrade the performance of fluorescence microscopy.Conventional adaptive optics(AO)leverages specific devices,such as the Shack–Hartmann wavefront sensor and deformable mirror,to measure and correct optical aberrations.However,conventional AO requires either additional hardware or a more complicated imaging procedure,resulting in higher cost or a lower acquisition speed.In this study,we proposed a novel space-frequency encoding network(SFE-Net)that can directly estimate the aberrated point spread functions(PSFs)from biological images,enabling fast optical aberration estimation with high accuracy without engaging extra optics and image acquisition.We showed that with the estimated PSFs,the optical aberration can be computationally removed by the deconvolution algorithm.Furthermore,to fully exploit the benefits of SFE-Net,we incorporated the estimated PSF with neural network architecture design to devise an aberration-aware deeplearning super-resolution model,dubbed SFT-DFCAN.We demonstrated that the combination of SFE-Net and SFT-DFCAN enables instant digital AO and optical aberration-aware super-resolution reconstruction for live-cell imaging.

关键词： optics resolution enable

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Robust cooperative multi-agent reinforcement learning via multi-view message certification

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Science China(Information Sciences) 2024年第4期67卷 131-145页

作者： Lei YUAN tao jiang Lihe LI Feng CHEN Zongzhang ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir Technologies

Many multi-agent scenarios require message sharing among agents to promote coordination,hastening the robustness of multi-agent communication when policies are deployed in a message perturbation environment. Major relevant studies tackle this issue under specific assumptions, like a limited number of message channels would sustain perturbations, limiting the efficiency in complex scenarios. In this paper,we take a further step in addressing this issue by learning a robust cooperative multi-agent reinforcement learning via multi-view message certification, dubbed CroMAC. Agents trained under CroMAC can obtain guaranteed lower bounds on state-action values to identify and choose the optimal action under a worst-case deviation when the received messages are perturbed. Concretely, we first model multi-agent communication as a multi-view problem, where every message stands for a view of the state. Then we extract a certificated joint message representation by a multi-view variational autoencoder(MVAE) that uses a product-of-experts inference network. For the optimization phase, we do perturbations in the latent space of the state for a certificate guarantee. Then the learned joint message representation is used to approximate the certificated state representation during training. Extensive experiments in several cooperative multi-agent benchmarks validate the effectiveness of the proposed CroMAC.

关键词： multi-agent reinforcement learning robust communication adversarial training multi-view learning message certification

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A novel multi-channel porous structure facilitating mass transport towards highly efficient alkaline water electrolysis

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Journal of Energy Chemistry 2024年第6期93卷 511-518,I0013页

作者： Xinge jiang Vasileios Kyriakou Chen Song Xianbin Wang Sophie Costil Chunming Deng Taikai Liu tao jiang Hanlin Liao Universitéde technologie de Belfort Montbéliard Centre national de la recherche scientifiqueLaboratoire Interdisciplinaire Carnot de BourgogneBelfort 90010France Department of Chemical Engineering Engineering and Technology Institute Groningen(ENTEG)University of GroningenGroningen 9747 AGThe Netherlands The Institute of New Materials Guangdong Academy of ScienceGuangzhou 510650GuangdongChina

An advantageous porous architecture of electrodes is pivotal in significantly enhancing alkaline water electrolysis(AWE)efficiency by optimizing the mass transport mechanisms.This effect becomes even more pronounced when aiming to achieve elevated current densities.Herein,we employed a rapid and scalable laser texturing process to craft novel multi-channel porous electrodes.Particularly,the obtained electrodes exhibit the lowest Tafel slope of 79 mV dec^(-1)(HER)and 49 mV dec^(-1)(OER).As anticipated,the alkaline electrolyzer(AEL)cell incorporating multi-channel porous electrodes(NP-LT30)exhibited a remarkable improvement in cell efficiency,with voltage drops(from 2.28 to 1.97 V)exceeding 300 mV under 1 A cm^(-1),compared to conventional perforated Ni plate electrodes.This enhancement mainly stemmed from the employed multi-channel porous structure,facilitating mass transport and bubble dynamics through an innovative convection mode,surpassing the traditional convection mode.Furthermore,the NP-LT30-based AEL cell demonstrated exceptional durability for 300 h under 1.0 A cm^(-2).This study underscores the capability of the novel multi-channel porous electrodes to expedite mass transport in practical AWE applications.

关键词： Alkaline water electrolysis Mass transport Bubble dynamics Innovative convection mode Multi-channel porous structure

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Bio-inspired Design and Inverse Kinematics Solution of an Omnidirectional Humanoid Robotic Arm with Geometric and Load Capacity Constraints

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Journal of Bionic Engineering 2024年第2期21卷 778-802页

作者： Zhichao Zhu Zirong Luo Yiming Zhu tao jiang Minghai Xia Shanjun Chen Boyu Jin College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina

Inspired by the driving muscles of the human arm,a 4-Degree of Freedom(DOF)concentrated driving humanoid robotic arm is proposed based on a spatial double parallel four-bar mechanism.The four-bar mechanism design reduces the inertia of the elbow-driving unit and the torque by 76.65%and 57.81%,respectively.Mimicking the human pose regulation strategy that the human arm picks up a heavy object by adjusting its posture naturally without complicated control,the robotic arm features an integrated position-level closed-form inverse solution method considering both geometric and load capacity limitations.This method consists of a geometric constraint model incorporating the arm angle(φ)and the Global Configuration(GC)to avoid joint limits and singularities,and a load capacity model to constrain the feasible domain of the arm angle.Further,trajectory tracking simulations and experiments are conducted to validate the feasibility of the proposed inverse solution method.The simulated maximum output torque,maximum output power and total energy consumption of the robotic arm are reduced by up to 2.0%,13.3%,and 33.3%,respectively.The experimental results demonstrate that the robotic arm can bear heavy loads in a human-like posture,effectively reducing the maximum output torque and energy consumption of the robotic arm by 1.83%and 5.03%,respectively,while avoiding joints beyond geometric and load capacity limitations.The proposed design provides a high payload–weight ratio and an efficient pose control solution for robotic arms,which can potentially broaden the application spectrum of humanoid robots.

关键词： Humanoid robotic arm Bio-inspired design Inverse kinematics solution Load capacity constraint Geometric constraint

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High-chromium vanadium-titanium magnetite all-pellet integrated burden optimization and softening-melting behavior based on flux pellets

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International Journal of Minerals,Metallurgy and Materials 2024年第3期31卷 498-507页

作者： Bojian Chen tao jiang Jing Wen Guangdong Yang Tangxia Yu Fengxiang Zhu Peng Hu School of Metallurgy Northeastern UniversityShenyang 110819China Key Laboratory for Ecological Metallurgy of Multimetallic Mineral(Ministry of Education) Northeastern UniversityShenyang 110819China Liaoning Key Laboratory for Metallurgical Sensor and Technology Shenyang 110819China State key laboratory of vanadium and titanium resources comprehensive utilization Panzhihua 617000China

High-chromium vanadium-titanium magnetite(HVTM)is a crucial polymetallic-associated resource to be developed.The allpellet operation is a blast furnace trend that aims to reduce carbon dioxide emissions in the future.By referencing the production data of vanadium-titanium magnetite blast furnaces,this study explored the softening-melting behavior of high-chromium vanadium-titanium magnetite and obtained the optimal integrated burden based on flux pellets.The results show that the burden with a composition of 70wt%flux pellets and 30wt%acid pellets exhibits the best softening-melting properties.In comparison to that of the single burden,the softening-melting characteristic temperature of this burden composition was higher.The melting interval first increased from 307 to 362℃and then decreased to 282℃.The maximum pressure drop(ΔPmax)decreased from 26.76 to 19.01 kPa.The permeability index(S)dropped from 4643.5 to 2446.8 kPa·℃.The softening-melting properties of the integrated burden were apparently improved.The acid pellets played a role in withstanding load during the softening process.The flux pellets in the integrated burden exhibited a higher slag melting point,which increased the melting temperature during the melting process.The slag homogeneity and the TiC produced by over-reduction led to the gas permeability deterioration of the single burden.The segregation of the flux and acid pellets in the HVTM proportion and basicity mainly led to the better softening-melting properties of the integrated burden.

关键词： high-chromium vanadium-titanium magnetite softening-melting properties all pellets integrated burden flux pellets

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Towards Semi-supervised Classification of Abnormal Spectrum Signals Based on Deep Learning

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Chinese Journal of Electronics 2024年第3期33卷 721-731页

作者： tao jiang Wanqing CHEN Hangping ZHOU Jinyang HE Peihan QI School of Cyber Engineering Xidian University State Key Laboratory of Integrate Service Networks Xidian University Guangzhou Institute of Technology Xidian University

In order to cope with the heavy labor cost challenge of the manual abnormal spectrum classification and improve the effectiveness of existing machine learning schemes for spectral datasets with interference-to-signal ratios,we proposes a semi-supervised classification of abnormal spectrum signals(SSC-ASS),aimed at addressing some of the challenges in abnormal spectrum signal(ASS) classification tasks.A significant advantage of SSC-ASS is that it does not require manual labeling of every abnormal data,but instead achieves high-precision classification of ASSs using only a small number of labeled data.Furthermore,the method can to some extent avoid the introduction of erroneous information resulting from the complex and variable nature of abnormal signals,thereby improving classification accuracy.Specifically,SSC-ASS uses a memory AutoEncoder module to efficiently extract features from abnormal spectrum signals by learning from the reconstruction error.Additionally,SSC-ASS combines convolutional neural network and the K-means using a DeepCluster framework to fully utilize the unlabeled data.Furthermore,SSC-ASS also utilizes pre-training,category mean memory module and replaces pseudo-labels to further improve the classification accuracy of ASSs.And we verify the classification effectiveness of SSC-ASS on synthetic spectrum datasets and real on-air spectrum dataset.

关键词： Deep learning Costs Memory management Memory modules Manuals Feature extraction Labeling

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