Search

Your search keyword '"Zhang, Lizhi"' showing total 2,996 results
Start Over Author "Zhang, Lizhi"
2,996 results on '"Zhang, Lizhi"'

2. Doping transition-metal atoms in graphene for atomic-scale tailoring of electronic, magnetic, and quantum topological properties

Author

Dyck, Ondrej, Zhang, Lizhi, Yoon, Mina, Swett, Jacob L., Hensley, Dale, Zhang, Cheng, Rack, Philip D., Fowlkes, Jason D., Lupini, Andrew R., and Jesse, Stephen

Subjects
Condensed Matter - Materials Science
Abstract

Atomic-scale fabrication is an outstanding challenge and overarching goal for the nanoscience community. The practical implementation of moving and fixing atoms to a structure is non-trivial considering that one must spatially address the positioning of single atoms, provide a stabilizing scaffold to hold structures in place, and understand the details of their chemical bonding. Free-standing graphene offers a simplified platform for the development of atomic-scale fabrication and the focused electron beam in a scanning transmission electron microscope can be used to locally induce defects and sculpt the graphene. In this scenario, the graphene forms the stabilizing scaffold and the experimental question is whether a range of dopant atoms can be attached and incorporated into the lattice using a single technique and, from a theoretical perspective, we would like to know which dopants will create technologically interesting properties. Here, we demonstrate that the electron beam can be used to selectively and precisely insert a variety of transition metal atoms into graphene with highly localized control over the doping locations. We use first-principles density functional theory calculations with direct observation of the created structures to reveal the energetics of incorporating metal atoms into graphene and their magnetic, electronic, and quantum topological properties.

Published
2023
Full Text
View/download PDF

3. Doping of Cr in Graphene Using Electron Beam Manipulation for Functional Defect Engineering

Author

Dyck, Ondrej, Yoon, Mina, Zhang, Lizhi, Lupini, Andrew R., Swett, Jacob L., and Jesse, Stephen

Subjects
Condensed Matter - Materials Science
Abstract

Chromium atoms in graphene have been proposed to exhibit magnetic moments and spin-selective conducting states depending on the local bonding geometry within the graphene structure, which could lead to interesting applications in spintronics. Despite this interest, there are few direct experimental reports of Cr dopants in graphene even though it is theorized to be stable. Here, we demonstrate the introduction of single Cr dopant atoms into the graphene lattice and onto graphene edges through the controlled use of a focused electron beam in a scanning transmission electron microscope. We show local control of doping locations and when coupled with targeted in situ milling during scanning of the e-beam, these strategies demonstrate an important component of the fabrication of tailored nanostructured devices in the electron microscope. The approach is validated with first principles calculations to understand synthesis pathways during fabrication and reveal the energetics and local properties of Cr atoms embedded in graphene; e.g., Cr doping can convert graphene into a magnetic and semiconducting material, which suggests Cr-doped graphene can be used as a building block for potential electronic devices and a means to construct them.

Published
2023
Full Text
View/download PDF

4. Study on risk control system of abnormal gas emission based on biological immune system

Author

ZHANG Lizhi and SHI Shiliang

Subjects
abnormal gas emission, immune mechanism, prevention and control system, coupling, inducing factors, Mining engineering. Metallurgy, TN1-997
Abstract

In order to further improve the prevention and control level of abnormal gas emission risk in coal mines, the feasibility of combining the prevention and control system of abnormal gas emission risk with immune mechanism was explored. On the basis of expatiating the structure, function and defense mechanism of the risk prevention and control system of abnormal gas emission and biological immune system, the coupling between the risk prevention and control system of abnormal gas emission and biological immune system is studied from the aspects of action object, action mechanism, function and corresponding relationship. The results show that the mechanism of immune system against antigens can be used as a reference for the prevention and control of abnormal gas emission risk. The risk prevention and control system of abnormal gas emission also has “specific immunity” and “nonspecific immunity”. The inducing factor of abnormal gas emission is “antigen”, and the occurrence of abnormal gas emission is “disease” produced in the system.

Published
2024
Full Text
View/download PDF

5. Mechanical Properties and Additive Manufacturing of Alkali-Activated Lunar Regolith in Artificial Lunar Environments

Author

Geng, Zifan, Zhang, Lizhi, Wu, Zhiwen, Tay, Yi Wei Daniel, Lim, Sean Gip, Tan, Ming Jen, de Amorim Almeida, Henrique, Series Editor, Al-Tamimi, Abdulsalam Abdulaziz, Editorial Board Member, Bernard, Alain, Editorial Board Member, Boydston, Andrew, Editorial Board Member, Koc, Bahattin, Editorial Board Member, Stucker, Brent, Editorial Board Member, Rosen, David W., Editorial Board Member, de Beer, Deon, Editorial Board Member, Pei, Eujin, Editorial Board Member, Gibson, Ian, Editorial Board Member, Drstvensek, Igor, Editorial Board Member, de Ciurana, Joaquim, Editorial Board Member, Lopes da Silva, Jorge Vicente, Editorial Board Member, da Silva Bártolo, Paulo Jorge, Editorial Board Member, Bibb, Richard, Editorial Board Member, Alvarenga Rezende, Rodrigo, Editorial Board Member, Wicker, Ryan, Editorial Board Member, Kosova Spahiu, Tatjana, Editorial Board Member, Bártolo, Helena, Editorial Board Member, Franchin, Giorgia, Editorial Board Member, Yasa, Evren, Editorial Board Member, Tan, Ming Jen, editor, Li, Mingyang, editor, Tay, Yi Wei Daniel, editor, Wong, Teck Neng, editor, and Bartolo, Paulo, editor

Published
2024
Full Text
View/download PDF

6. The Inversion Calculation Method of Core Temperature in 110 kV Cable Inter-mediate Joint

Author

Tao, Xiantao, Wang, Lei, Hu, Ying, Chang, Hangrui, Chen, Junhao, Ye, Gang, Zhang, Lizhi, Xing, Yunqi, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Dong, Xuzhu, editor, and Cai, Li Cai, editor

Published
2024
Full Text
View/download PDF

14. Realistic constraints, model selection, and detectability of modular network structures

Author

Zhang, Lizhi, De Paula Peixoto, Tiago, and Nunes, Matthew

Subjects
network analysis, Bayesian inference, clustering algorithms, machine learning
Abstract

Many real-world systems are complex, consisting of many entities with interactions among them. Our understanding of real-world complex systems has been significantly advanced by modelling these systems as networks. A network is a mathematical ab- straction of complex systems, representing entities and interactions by nodes and edges. Recent years have witnessed a rapid growth in the demand for analysing networks data, driven by the increased availability of large-scale, quality datasets. A common task in network analysis is to identify the "building blocks" of a network by finding divisions of nodes, such that nodes in the same division connect with the rest of the network in a similar way. This task is often referred to as community detection in networks. Community detection methods allow researchers to characterise network data from the perspective of connection pattern, which could convey important information about the functional and evolutionary mechanism of the underlying systems. Recently, Bayesian inference based on generative network model has attracted great attention as a community detection method, which is mainly due to its principle infer- ence nature and formal implementation of the Occam's razor. However, this method often relies on general models that simultaneously account for different kinds of com- munity structure. If the dominant structure in data is in fact restricted and simple, using general models could lead to sub-optimal fit to data. This thesis concerns with developing Bayesian inference community detection methods that are tailored for a particular kind of structure - the assortative structure. A net- work is said to be assortative if it can be divided into subgroups of nodes, such that connections inside each of division are dense while between distinct divisions are sparse. To this end, we develop the Bayesian formulation of the degree-corrected planted par- tition model. Such model assumes the probability of an edge between a pair of nodes is dependant on whether they are from the same subgroups as well as their node- wise propensity of receiving an edge. This formulation leads to a novel method for extracting assortative structures and this method is one of the main contributions of this thesis. Compared with other existing methods, our proposed method has the ad- vantage of being robust against overfitting, which means our method will not report spurious community structures in random networks while other non-statistical, heuris- tic methods usually do. In deriving our proposed method, we clarify on an established equivalence between the popular modularity maximisation approach and maximum likelihood inference. Our analysis shows that the equivalence result is tenuous, since it relies on subjective choices of model parameters which lack of principle justifications. We demonstrate the performance of our proposed method in both synthetic and empir- ical networks. In particular, we construct a large network corpus consisting of datasets which are diverse in terms of size and density. Using this network corpus, we find evidence that the degree-corrected planted partition model has the ability of achiev- ing better quality of fit in some empirical networks compared to existing models in some cases,. Moreover, the degree-corrected planted partition model has the potential of providing additional insight into data regarding high-resolution community struc- ture. Moreover, by conducting model selection in our network corpus, we find that assortativity is often too simplistic to be the dominant pattern in empirical networks. Finally, we study the detectability of assortative community structures. In networks where all nodes receive identical number of edges on average, there exists a detectabil- ity threshold of the strength of community structure, below which no polynomial algo- rithms can detect the planted community structure better than random guessing. We conduct a numerical study to examine the effect of heterogeneity in the number of edges attaching to nodes on the detectability of assortative structures. Such effect has been analytically studied in a special case where networks have two equal-size communities. Our results provide further numerical evidence for the existing theoretical analysis and open the door to investigation about the detectability of community structures in more general settings, e.g. in networks consisting of more than two communities, which could have different extents of heterogeneity in degree distribution.

Published
2023

31. Self-regulated growth of candidate topological superconducting parkerite by molecular beam epitaxy

Author

Lapano, Jason, Pai, Yun-Yi, Mazza, Alessandro, Zhang, Jie, Isaacs-Smith, Tamara, Gemperline, Patrick, Zhang, Lizhi, Li, Haoxiang, Lee, Ho Nyung, Miao, Hu, Eres, Gyula, Yoon, Mina, Comes, Ryan, Ward, T. Zac, Lawrie, Benjamin J., McGuire, Michael, Moore, Robert G., Nelson, Christopher T., May, Andrew, and Brahlek, Matthew

Subjects
Condensed Matter - Materials Science
Abstract

Ternary chalcogenides such as the parkerites and shandites are a broad class of materials exhibiting rich diversity of transport and magnetic behavior as well as an array of topological phases including Weyl and Dirac nodes. However, they remain largely unexplored as high-quality epitaxial thin films. Here, we report the self-regulated growth of thin films of the strong spin-orbit coupled superconductor Pd3Bi2Se2 on SrTiO3 by molecular beam epitaxy. Films are found to grow in a self-regulated fashion, where, in excess Se, the temperature and relative flux ratio of Pd to Bi controls the formation of Pd3Bi2Se2 due to the combined volatility of Bi, Se, and Bi-Se bonded phases. The resulting films are shown to be of high structural quality, the stoichiometry is independent of the Pd:Bi and Se flux ratio and exhibit a superconducting transition temperature of 800 mK and critical field of 17.7 +/- 0.5 mT, as probed by transport as well as magnetometry. Understanding and navigating the growth of the chemically and structurally diverse classes of ternary chalcogenides opens a vast space for discovering new phenomena as well as enabling new applications.

Published
2021
Full Text
View/download PDF

46. Banff 2022 pancreas transplantation multidisciplinary report: Refinement of guidelines for T cell–mediated rejection, antibody-mediated rejection and islet pathology. Assessment of duodenal cuff biopsies and noninvasive diagnostic methods

Author

Drachenberg, Cinthia B., Buettner-Herold, Maike, Aguiar, Pedro Ventura, Horsfield, Catherine, Mikhailov, Alexei V., Papadimitriou, John C., Seshan, Surya V., Perosa, Marcelo, Boggi, Ugo, Uva, Pablo, Rickels, Michael, Grzyb, Krzyztof, Arend, Lois, Cuatrecasas, Miriam, Toniolo, Maria Fernanda, Farris, Alton B., Renaudin, Karine, Zhang, Lizhi, Roufousse, Candice, Gruessner, Angelika, Gruessner, Rainer, Kandaswamy, Raja, White, Steven, Burke, George, Cantarovich, Diego, Parsons, Ronald F., Cooper, Matthew, Kudva, Yogish C., Kukla, Aleksandra, Haririan, Abdolreza, Parajuli, Sandesh, Merino-Torres, Juan Francisco, Argente-Pla, Maria, Meier, Raphael, Dunn, Ty, Ugarte, Richard, Rao, Joseph Sushil, Vistoli, Fabio, Stratta, Robert, and Odorico, Jon

Published
2024
Full Text
View/download PDF

50. Revealing the chemical bonding in adatoms arrays via machine learning of 3D scanning tunneling spectroscopy data

Author

Roccapriore, Kevin M., Zou, Qiang, Zhang, Lizhi, Xue, Rui, Yan, Jiaqiang, Ziatdinov, Maxim, Fu, Mingming, Mandrus, David, Yoon, Mina, Sumpter, Bobby, Gai, Zheng, and Kalinin, Sergei V.

Subjects
Condensed Matter - Materials Science, Condensed Matter - Disordered Systems and Neural Networks
Abstract

The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information is readily available in hyperspectral scanning tunneling spectroscopy data, its analysis has been considerably impeded by a lack of suitable analytical tools. Here we develop a machine learning based workflow combining supervised feature identification in the spatial domain and un-supervised clustering in the energy domain to reveal the details of structure-dependent changes of the electronic structure in adatom arrays on the Co3Sn2S2 cleaved surface. This approach, in combination with first-principles calculations, provides insight for using artificial neural networks to detect adatoms and classifies each based on their local neighborhood comprised of other adatoms. These structurally classified adatoms are further spectrally deconvolved. The unexpected inhomogeneity of electronic structures among adatoms in similar configurations is unveiled using this method, suggesting there is not a single atomic species of adatoms, but rather multiple types of adatoms on the Co3Sn2S2 surface. This is further supported by a slight contrast difference in the images (or slight size variation) of the topography of the adatoms.

Published
2021
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results