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Institution

Dalian University of Technology

EducationDalian, China
About: Dalian University of Technology is a education organization based out in Dalian, China. It is known for research contribution in the topics: Catalysis & Finite element method. The organization has 60890 authors who have published 71921 publications receiving 1188356 citations. The organization is also known as: Dàlián Lǐgōng Dàxué.


Papers
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Journal ArticleDOI
TL;DR: The research and progress of a variety of welding techniques for joining dissimilar Mg alloys and Al alloys are reviewed from different perspectives to provide a basis for follow-up research.
Abstract: Welding of dissimilar magnesium alloys and aluminum alloys is an important issue because of their increasing applications in industries. In this document, the research and progress of a variety of welding techniques for joining dissimilar Mg alloys and Al alloys are reviewed from different perspectives. Welding of dissimilar Mg and Al is challenging due to the formation of brittle intermetallic compound (IMC) such as Mg17Al12 and Mg2Al3. In order to increase the joint strength, three main research approaches were used to eliminate or reduce the Mg-Al intermetallic reaction layer. First, solid state welding techniques which have a low welding temperature were used to reduce the IMCs. Second, IMC variety and distribution were controlled to avoid the degradation of the joining strength in fusion welding. Third, techniques which have relatively controllable reaction time and energy were used to eliminate the IMCs. Some important processing parameters and their effects on weld quality are discussed, and the microstructure and metallurgical reaction are described. Mechanical properties of welds such as hardness, tensile, shear and fatigue strength are discussed. The aim of the report is to review the recent progress in the welding of dissimilar Mg and Al to provide a basis for follow-up research.

195 citations

Journal ArticleDOI
TL;DR: In this paper, 3D interconnected graphene nanocapsules (GNCs) were prepared from diverse aromatic hydrocarbons by a nano-ZnO-template strategy coupled with in-situ KOH activation technique.

194 citations

Journal ArticleDOI
TL;DR: This tutorial review will focus on the recent advancements of chemiluminescent platforms based on luminophore substrates including luminol and its derivatives, cypridina luciferin analogs, peroxyoxalates, and dioxetanes, and systematically summarize the design principles, sensing mechanisms, and bioimaging and therapeutic applications of representative chemilUMinescent probes as well as theranostic agents.
Abstract: Chemiluminescence, the generation of light through chemiexcitation as a result of chemical reactions, has emerged as a novel tool for bioimaging and therapy in vivo. Due to the elimination of external optical excitation, it can effectively avoid background autofluorescence existing in fluorescence techniques, providing extremely high signal-to-noise ratios and sensitivity in bioimaging. Furthermore, in situ emitted photons can replace traditional excitation light to construct chemiexcited photodynamic therapy or drug release systems for the monitoring and treatment of deeply seated diseases or tumors. In this tutorial review, we will focus on the recent advancements of chemiluminescent platforms based on luminophore substrates including luminol and its derivatives, cypridina luciferin analogs, peroxyoxalates, and dioxetanes, and systematically summarize the design principles, sensing mechanisms, and bioimaging and therapeutic applications of representative chemiluminescent probes as well as theranostic agents. Finally, the potential challenges and perspectives of chemiluminescent platforms for bioimaging and therapeutics are also discussed.

194 citations

Journal ArticleDOI
TL;DR: In this article, a new shear-flow testing apparatus with specially designed fluid sealing techniques for rock fractures were developed, under constant normal load (CNL) or constant normal stiffness (CNS) constraint.

194 citations

Journal ArticleDOI
TL;DR: This article constructs an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning and develops a two-sided matching scheme and a deep reinforcementLearning approach to schedule offloading requests and allocate network resources.
Abstract: The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users’ traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users’ Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.

194 citations


Authors

Showing all 61205 results

NameH-indexPapersCitations
Yang Yang1712644153049
Yury Gogotsi171956144520
Hui Li1352982105903
Michael I. Posner134414104201
Anders Hagfeldt12960079912
Jian Zhou128300791402
Chao Zhang127311984711
Bin Wang126222674364
Chi Lin1251313102710
Tao Zhang123277283866
Bo Wang119290584863
Zhenyu Zhang118116764887
Liang Cheng116177965520
Anthony G. Fane11256540904
Xuelong Li110104446648
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023167
2022836
20216,974
20206,457
20196,261
20185,375