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New routes for the construction of strong metal-support interactions

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Science China Chemistry 2022年第11期65卷 2051-2057页

作者： Hai Wang Liang wang Feng-Shou Xiao Key Lab of Biomass Chemical Engineering of Ministry of Education College of Chemical and Biological EngineeringZhejiang UniversityHangzhou310027China College of Chemistry Zhengzhou UniversityZhengzhou 450001China

Supported metal nanoparticles(NPs)on solid carriers are highly efficient catalysts in many industrial reactions.However,the sintering and/or leaching of metal NPs occurred under harsh reaction conditions that caused the catalyst deactivation.Strong metal-support interactions(SMSIs)serve as an effective method to stabilize the metal NPs against sintering and leaching,which have been extensively studied.In addition to the classical route to construct SMSIs via high-temperature reduction treatments,new routes have emerged recently to extend the scope of catalysts with SMSIs and optimized their catalytic performances.In this review,we briefly summarize these routes that avoid the high-temperature reduction treatments for the construction of SMSIs.Their significant advantages in stabilizing metal NPs,modulating the geometric/electronic structure of metal species,and the mechanism on the SMSI formation are particularly discussed.Finally,the current challenges and developing trends in the construction of SMSIs for achieving more efficient catalysts are outlooked.

关键词： strong metal-support interaction supported catalyst metal nanoparticles heterogeneous catalysis

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Intranasal administration nanosystems for brain-targeted drug delivery

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Nano Research 2023年第12期16卷 13077-13099页

作者： Yue Jiang Xueting Pan Tao Yu Hai Wang CAS Key Laboratory for Biomedical Effects of Nanomaterials&Nanosafety CAS Center for Excellence in NanoscienceNational Center for Nanoscience and TechnologyBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Beijing Institute of Functional Neurosurgery Xuanwu HospitalCapital Medical UniversityBeijing 100053China

The existence of the blood-brain barrier(BBB)restricts the entry of drugs from the circulation into the central nervous system(CNS),which severely affects the treatment of neurological diseases,including glioblastoma,Parkinson’s disease(PD),and Alzheimer’s disease(AD).With the advantage of bypassing the BBB and avoiding systemic distribution,intranasal administration has emerged as an alternative method of delivering drugs to the brain.Drug delivery directly to the brain using intranasal nanosystems represents a new paradigm for neurological disease treatment because of its advantages in improving drug solubility and stability in vivo,enabling targeted drug delivery and controlled release,and reducing non-specific toxicity.And it has shown efficacy in animal models and clinical applications.Herein,this review describes the mechanisms of intranasal delivery of brain-targeted drugs,the properties of nanosystems for intranasal administration(e.g.,liposomes,nanoemulsions,and micelles),and strategies for intranasal drug delivery to enhance brain-targeted drug delivery.Recent applications of nanosystems in intranasal drug delivery and disease treatment have been comprehensively reviewed.Although encouraging results have been reported,significant challenges still need to be overcome to translate these nanosystems into clinics.Therefore,the future prospects of intranasal drug delivery nanosystems are discussed in depth,expecting to provide useful insights and guidance for effective neurological disease treatment.

关键词： intranasal administration nanosystems brain-targeted delivery neurological diseases drug delivery

Study on open charm hadron production and angular correlation in high-energy nuclear collisions

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Nuclear Science and Techniques 2021年第1期32卷 10-16页

作者： Hai Wang Jin-Hui Chen Key Laboratory of Nuclear Physics and Ion-beam Application(MOE) Fudan UniversityShanghai 200433China

We study the production and angular correlationof charm hadrons in hot and dense matter produced in high-energy nuclear-nuclear collisions within a multiphasetransport model(AMPT).By triggering additional charm-anticharm quark pair production in the AMPT,the modeldescribes the D^0 nuclear modification factor in the low andintermediate pr regions in Au+Au collisions at√VSNN=200 GeV reasonably well.Further exploration of the D^0 pair azimuthal angular correlation for different centralitiesshows clear evolution from low-multiplicity to high-mul-tiplicity events,which is associated with the number ofcharm quark interactions with medium partons duringAMPT transport.

关键词： Heavy-ion collision Heavy flavor Nuclear modification factor Two-particle correlations

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Improving thermal stability of LiNbO_(3)based electro-optical electric field sensor by depositing a TiO_(2)film

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High Voltage 2022年第5期7卷 840-846页

作者： She wang Hai Wang Chanxiao Li Chijie Zhuang Rong Zeng Department of Electrical Engineering Tsinghua UniversityBeijingChina Tianfu New Area Power Supply Company State Grid of Sichuan Electric Power CompanyChengduChina State Key Lab of Power Systems Tsinghua UniversityBeijingChina International Joint Laboratory on Low Carbon Clean Energy Innovation Tsinghua UniversityBeijingChina

Electro-optic electric field sensors based on LiNbO3 are widely used for the measurement of electric fields or transient voltages.However,the working bias of the sensor is influenced by the temperature due to the thermo-optic effect of LiNbO_(3).An athermal electrooptic electric field sensor was demonstrated by coating a thin layer of TiO_(2)film which has a negative thermo-optic coefficient,on the common-path interferometer based sensor to compensate LiNbO_(3)'s positive thermo-optic coefficient.Both calculation and experimental studies were conducted.The experimental results reasonably agree well with the calculated values,and the results show the sensor coated with a 640 nm thick TiO_(2)film has a much smaller temperature dependence of 0.011°/℃compared to the initial value of 0.1°/℃.

关键词： film field stability

Characterization of hot deformation behavior of 30Si2MnCrMoVE low-alloying ultra-high-strength steel by constitutive equations and processing maps

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Journal of Iron and Steel Research(International) 2020年第7期27卷 807-819页

作者： Hai Wang Dong Liu Jian-guo wang hai-ping wang Yang Hu Hao-dong Rao School of Materials Science and Engineering Northwestern Polytechnical UniversityXi’an710072ShaanxiChina State Key Laboratory of Solidification Processing Northwestern Polytechnical UniversityXi’an710072ShaanxiChina

Isothermal compression tests of as-forged 30Si2MnCrMoVE low-alloying ultra-high-strength steel were carried out on a Gleeble 3500 thermal simulator at the deformation temperatures of 950-1150℃and strain rates of 0.01-10 s^−1.Based on the classical stress-dislocation density relationship and the kinematics of the dynamic recrystallization,the constitutive equations of the work hardening dynamical recovery period and dynamical recrystallization period were developed by using the work hardening curve and Avrami equation,which shows good agreement with the experimental value.Processing maps at the strain of 0.90 were constructed based on dynamic material model and were analyzed combined with microstructure observation under different conditions.The optimum parameter based on the processing maps was obtained and verified by a supplementary experiment.The power dissipation maps and instability maps at strains of 0.05-0.90 were also constructed,and the evolution law was analyzed in detail.The established constitutive equation and hot processing maps can provide some guidance for hot working process.

关键词： 30Si2MnCrMoVE ultra-high-strength steel Hot deformation Constitutive model Processing map Power dissipation efficiency

Robust model selection for positive and unlabeled learning with constraints

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Science China(Information Sciences) 2022年第11期65卷 94-106页

作者： Tong WEI Hai Wang Weiwei TU Yufeng LI Department of Computer Science and Technology Nanjing University 4Paradigm Inc.

Positive and unlabeled(PU) learning is the problem in which training data contains only PU samples. Although PU learning is widely used in real-world applications, its model selection remains challenging. Specifically, traditional model selection methods are often highly sensitive to the class prior as well as the data size, resulting in human overhead in hyperparameter optimization. In this paper, we present a method called ODE(robust model selection) for robust model selection in PU learning. Two novel model evaluators based on the integral probability metric and area under the curve, which are free of the class prior, are introduced, and a variance reduction method is further employed to improve the quality of model selection. In addition, we perform model selection under user-defined constraints and propose a fast halvingstyle searching algorithm to efficiently identify the most promising model configuration. Extensive empirical studies demonstrate that our proposed method performs more robustly and is more computationally efficient than many state-of-the-art methods.

关键词： positive and unlabeled learning model selection automated machine learning

Shading Fault Detection Method for Household Photovoltaic Power Stations Based on Inherent Characteristics of Monthly String Current Data Mapping

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CSEE Journal of Power and Energy Systems 2023年第4期9卷 1370-1382页

作者： Wenting Ma Mingyao Ma Hai Wang Zhixiang Zhan Rui Zhang Jun wang National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology Hefei University of TechnologyHefei 230009China Murdoch University Murdoch 6150Australia. Sungrow Smart Maintenance Technology Co. Ltd.Hefei 230088China.

The poor outdoor operating conditions of household photovoltaic(PV)make the power station prone to various faults.However,the dispersion of household PV installations often increases the difficulty and cost of operation and maintenance(O&M).Although the remote monitoring and fault detection of a PV power station can be realized by the use of operation data,the particularity of a household power station also brings many problems to fault detection.In this study,we propose a shading fault detection method of household PV power based on inherent characteristics of monthly string current data mapping.The ideal current peak obtained by a new fitting method is used to normalize string current data.The current probability density function(PDF)at each time point is estimated by kernel density estimation(KDE).Through the normalized current data corresponding to the maximum probability density,the inherent characteristics of the strings are obtained,such that whether the strings have shading can be judged and the shading degree can then be evaluated.Not only are no additional sensors needed to collect environmental data,such as irradiation and temperature,but also simulating the detailed parameters of the power station is not required.The interference caused by meteorological factors can thus be eliminated,which can be easily used in old power stations and newly constructed power stations.The effectiveness and performance of the proposed shading fault detection method is verified by experimental data collected from the actual household PV power station.Index Terms-Data fitting,fault detection,household photovoltaic(PV),kernel density estimation(KDE),shading degree.

关键词： Terms-Data fitting fault detection household photovoltaic(PV) kernel density estimation(KDE) shading degree.

ETS-TEE: An Energy-Efficient Task Scheduling Strategy in a Mobile Trusted Computing Environment

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Tsinghua Science and Technology 2023年第1期28卷 105-116页

作者： Hai Wang Lu Cai Xuan Hao Jie Ren Yuhui Ma School of Information Technology Northwest UniversityXi’an 710127China School of Computer Science Shaanxi Normal UniversityXi’an 710061China

A trusted execution environment(TEE)is a system-on-chip and CPU system with a wide security solution available on today’s Arm application(APP)processors,which dominate the smartphone market.Generally,mobile APPs create a trusted application(TA)in the TEE to process sensitive information,such as payment or message encryption,which is transparent to the APPs running in the rich execution environments(REEs).In detail,the REE and TEE interact and eventually send back the results to the APP in the REE through the interface provided by the TA.Such an operation definitely increases the overhead of mobile APPs.In this paper,we first present a comprehensive analysis of the performance of open-source TEE encrypted text.We then propose a high energy-efficient task scheduling strategy(ETS-TEE).By leveraging the deep learning algorithm,our policy considers the complexity of TA tasks,which are dynamically scheduled between modeling on the local device and offloading to an edge server.We evaluate our approach on Raspberry Pi 3B as the local mobile device and Jetson TX2 as the edge server.The results show that compared with the default scheduling strategy on the local device,our approach achieves an average of 38.0%energy reduction and 1.6×speedup.This greatly reduces the performance loss caused by mobile devices in order to protect the safe execution of applications,so that the trusted execution environment has both security and high performance.

关键词： trusted execution environment mobile system task scheduling strategy optimization of energy efficiency

Antibacterial copper-bearing titanium alloy prepared by laser powder b e d fusion for superior mechanical performance

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Journal of Materials Science & Technology 2023年第1期132卷 100-109页

作者： Huan Liu Hai Wang Ling Ren Dong Qiu Ke Yang School of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China Shi-changxu Innovation Center for Advanced Materials Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China Centre for Additive Manufacturing School of EngineeringRMIT UniversityMelbourneVictoriaAustralia Binzhou Institute of Technology Weiqiao-UCAS Science and Technology ParkBinzhouShandong Province256606China Shandong Key Laboratory of Advanced Aluminium Materials and Technology Weiqiao-UCAS Science and Technology ParkBinzhouShandong Province256606China

Copper element was added in pure titanium to produce a new biomedical titanium-copper alloy by laser powder bed fusion(LPBF).Addition of copper can eliminate the mismatch of high strength but poor duc-tility problem caused by lathα’martensite,which is the usual microstructure of nearαtitanium alloy fabricated by LPBF.Instead of by the usual trade-offrelationship between strength and ductility,which is a long-standing challenge for martensitic titanium alloys,in this study,we proposed a boundary engineer-ing strategy and aim to synergistically enhance the strength and ductility of martensitic titanium alloy fabricated by LPBF.It is hypothesized that whilst both low-angle and high-angle grain boundaries are beneficial to the strength,high-angle grain boundary can simultaneously improve the ductility of materi-als.To test this strategy,a Ti-5Cu(wt.%)alloy is selected to compare against pure titanium and Ti-6Al-4V at the same laser processing conditions.EBSD,TEM and XRD analysis show that the as-fabricated LPBF Ti-5Cu alloy is comprised of partially tempered martensite with extraordinarily high number density of both high-angle and low-angle grain boundaries as well as low dislocation density.Such microstructure enables a high tensile strength of 940-1020 MPa,which is at a similar level as LPBF Ti-6Al-4V,and an excellent elongation of 13%-16%,twice as much as that of LPBF Ti-6Al-4V.The mechanism of microstruc-ture refinement in LPBF Ti-5Cu at different levels from prior-βgrains,martensitic packets,blocks to laths is also discussed.

关键词： Titanium alloy Laser powder bed fusion Boundary engineering Martensite Mechanical properties