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Huang J, Han Q, Cai M, Zhu J, Li L, Yu L, Wang Z, Fan G, Zhu Y, Lu J, Zhou G. Effect of Angiogenesis in Bone Tissue Engineering. Ann Biomed Eng 2022; 50:898-913. [PMID: 35525871 DOI: 10.1007/s10439-022-02970-9] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 04/17/2022] [Indexed: 12/20/2022]
Abstract
The reconstruction of large skeletal defects is still a tricky challenge in orthopedics. The newly formed bone tissue migrates sluggishly from the periphery to the center of the scaffold due to the restrictions of exchange of oxygen and nutrition impotent cells osteogenic differentiation. Angiogenesis plays an important role in bone reconstruction and more and more studies on angiogenesis in bone tissue engineering had been published. Promising advances of angiogenesis in bone tissue engineering by scaffold designs, angiogenic factor delivery, in vivo prevascularization and in vitro prevascularization are discussed in detail. Among all the angiogenesis mode, angiogenic factor delivery is the common methods of angiogenesis in bone tissue engineering and possible research directions in the future.
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Affiliation(s)
- Jianhao Huang
- Department of Orthopedics, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, 210002, People's Republic of China
| | - Qixiu Han
- Department of Orthopedics, Jinling Hospital, Nanjing Medical University, Nanjing, 210002, People's Republic of China
| | - Meng Cai
- Department of Orthopedics, Jinling Hospital, School of Medicine, Southeast University, Nanjing, 210002, People's Republic of China
| | - Jie Zhu
- Department of Orthopedics, Jinling Hospital, Nanjing Medical University, Nanjing, 210002, People's Republic of China
| | - Lan Li
- State Key Laboratory of Pharmaceutical Biotechnology, Department of Sports Medicine and Adult Reconstructive Surgery, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, People's Republic of China
| | - Lingfeng Yu
- Department of Orthopedics, Jinling Hospital, School of Medicine, Nanjing University, Nanjing, 210002, People's Republic of China
| | - Zhen Wang
- Department of Orthopedics, Jinling Hospital, Nanjing Medical University, Nanjing, 210002, People's Republic of China
| | - Gentao Fan
- Department of Orthopedics, Nanjing Jinling Hospital, 305 Zhongshan East Road, Nanjing, 210002, People's Republic of China
| | - Yan Zhu
- Department of Orthopedics, Nanjing Jinling Hospital, 305 Zhongshan East Road, Nanjing, 210002, People's Republic of China
| | - Jingwei Lu
- Department of Orthopedics, Jinling Hospital, Nanjing Medical University, Nanjing, 210002, People's Republic of China. .,Department of Orthopedics, Nanjing Jinling Hospital, 305 Zhongshan East Road, Nanjing, 210002, People's Republic of China.
| | - Guangxin Zhou
- Department of Orthopedics, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, 210002, People's Republic of China. .,Department of Orthopedics, Jinling Hospital, School of Medicine, Nanjing University, Nanjing, 210002, People's Republic of China. .,Department of Orthopedics, Nanjing Jinling Hospital, 305 Zhongshan East Road, Nanjing, 210002, People's Republic of China. .,The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, People's Republic of China.
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2
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 286] [Impact Index Per Article: 95.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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Gao J, Park JW, Kim K, Song SK, Park HR, Lee J, Park J, Chen F, Luo X, Sun Y, Yeom HW. Pseudogap and Weak Multifractality in 2D Disordered Mott Charge-Density-Wave Insulator. NANO LETTERS 2020; 20:6299-6305. [PMID: 32787162 DOI: 10.1021/acs.nanolett.0c01607] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We investigate electronic states of Se-substituted 1T-TaS2 by scanning tunneling microscopy/spectroscopy (STM/STS), where superconductivity emerges from the unique Mott-charge-density-wave (Mott-CDW) state. Spatially resolved STS measurements reveal that a pseudogap replaces the Mott gap with the CDW gaps intact. The pseudogap has little correlation with the unit-cell-to-unit-cell variation in the local Se concentration but appears globally. The correlation length of the local density of states (LDOS) is substantially enhanced at the Fermi energy and decays rapidly at high energies. Furthermore, the statistical analysis of LDOS indicates the weak multifractal behavior of the wave functions. These findings suggest a correlated metallic state induced by disorder and provide a new insight into the emerging superconductivity in two-dimensional materials.
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Affiliation(s)
- Jianhua Gao
- Center for Artificial Low Dimensional Electronic Systems, Institute for Basic Science (IBS), Pohang 37673, Korea
| | - Jae Whan Park
- Center for Artificial Low Dimensional Electronic Systems, Institute for Basic Science (IBS), Pohang 37673, Korea
| | - Kiseok Kim
- Department of Physics, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Sun Kyu Song
- Center for Artificial Low Dimensional Electronic Systems, Institute for Basic Science (IBS), Pohang 37673, Korea
- Department of Physics, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Hae Ryong Park
- Center for Artificial Low Dimensional Electronic Systems, Institute for Basic Science (IBS), Pohang 37673, Korea
- Department of Physics, Pohang University of Science and Technology, Pohang 37673, Korea
| | - Jhinhwan Lee
- Center for Artificial Low Dimensional Electronic Systems, Institute for Basic Science (IBS), Pohang 37673, Korea
| | - Jewook Park
- Center for Artificial Low Dimensional Electronic Systems, Institute for Basic Science (IBS), Pohang 37673, Korea
| | - Fangchu Chen
- Key Laboratory of Materials Physics, Institute of Solid State Physics, Chinese Academy of Sciences, Hefei 230031, People's Republic of China
- University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Xuan Luo
- Key Laboratory of Materials Physics, Institute of Solid State Physics, Chinese Academy of Sciences, Hefei 230031, People's Republic of China
| | - Yuping Sun
- Key Laboratory of Materials Physics, Institute of Solid State Physics, Chinese Academy of Sciences, Hefei 230031, People's Republic of China
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei 230031, People's Republic of China
- Collaborative Innovation Centre of Advanced Microstructures, Nanjing University, Nanjing 210093, People's Republic of China
| | - Han Woong Yeom
- Center for Artificial Low Dimensional Electronic Systems, Institute for Basic Science (IBS), Pohang 37673, Korea
- Department of Physics, Pohang University of Science and Technology, Pohang 37673, Korea
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Li Y, Yin Z, Liu Z, Wang W, Xu Z, Song Y, Tian L, Huang Y, Shen D, Abernathy DL, Niedziela JL, Ewings RA, Perring TG, Pajerowski DM, Matsuda M, Bourges P, Mechthild E, Su Y, Dai P. Coexistence of Ferromagnetic and Stripe Antiferromagnetic Spin Fluctuations in SrCo_{2}As_{2}. PHYSICAL REVIEW LETTERS 2019; 122:117204. [PMID: 30951336 DOI: 10.1103/physrevlett.122.117204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Indexed: 06/09/2023]
Abstract
We use inelastic neutron scattering to study energy and wave vector dependence of spin fluctuations in SrCo_{2}As_{2}, derived from SrFe_{2-x}Co_{x}As_{2} iron pnictide superconductors. Our data reveal the coexistence of antiferromagnetic (AF) and ferromagnetic (FM) spin fluctuations at wave vectors Q_{AF}=(1,0) and Q_{FM}=(0,0)/(2,0), respectively. By comparing neutron scattering results with those of dynamic mean field theory calculation and angle-resolved photoemission spectroscopy experiments, we conclude that both AF and FM spin fluctuations in SrCo_{2}As_{2} are closely associated with a flatband of the e_{g} orbitals near the Fermi level, different from the t_{2g} orbitals in superconducting SrFe_{2-x}Co_{x}As_{2}. Therefore, Co substitution in SrFe_{2-x}Co_{x}As_{2} induces a t_{2g} to e_{g} orbital switching, and is responsible for FM spin fluctuations detrimental to the singlet pairing superconductivity.
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Affiliation(s)
- Yu Li
- Department of Physics and Astronomy, Rice University, Houston, Texas 77005, USA
- Department of Physics, Beijing Normal University, Beijing 100875, China
| | - Zhiping Yin
- Department of Physics, Beijing Normal University, Beijing 100875, China
| | - Zhonghao Liu
- State Key Laboratory of Functional Materials for Informatics and Center for Excellence in Superconducting Electronics, SIMIT, Chinese Academy of Sciences, Shanghai 200050, China
| | - Weiyi Wang
- Department of Physics and Astronomy, Rice University, Houston, Texas 77005, USA
| | - Zhuang Xu
- Department of Physics, Beijing Normal University, Beijing 100875, China
| | - Yu Song
- Department of Physics and Astronomy, Rice University, Houston, Texas 77005, USA
| | - Long Tian
- Department of Physics, Beijing Normal University, Beijing 100875, China
| | - Yaobo Huang
- Shanghai Synchrotron Radiation Facility, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201204, China
| | - Dawei Shen
- State Key Laboratory of Functional Materials for Informatics and Center for Excellence in Superconducting Electronics, SIMIT, Chinese Academy of Sciences, Shanghai 200050, China
| | - D L Abernathy
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - J L Niedziela
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - R A Ewings
- ISIS Pulsed Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0QX, United Kingdom
| | - T G Perring
- ISIS Pulsed Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0QX, United Kingdom
| | - Daniel M Pajerowski
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Masaaki Matsuda
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Philippe Bourges
- Laboratoire Léon Brillouin, CEA-CNRS, Université Paris-Saclay, CEA Saclay, 91191 Gif-sur-Yvette, France
| | - Enderle Mechthild
- Institut Laue-Langevin, 6 rue Jules Horowitz, Boîte Postale 156, 38042 Grenoble Cedex 9, France
| | - Yixi Su
- Jülich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ), Forschungszentrum Jülich, Lichtenbergstrasse 1, 85747 Garching, Germany
| | - Pengcheng Dai
- Department of Physics and Astronomy, Rice University, Houston, Texas 77005, USA
- Department of Physics, Beijing Normal University, Beijing 100875, China
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Song Y, Cao H, Chakoumakos BC, Zhao Y, Wang A, Lei H, Petrovic C, Birgeneau RJ. Intertwined Magnetic and Nematic Orders in Semiconducting KFe_{0.8}Ag_{1.2}Te_{2}. PHYSICAL REVIEW LETTERS 2019; 122:087201. [PMID: 30932606 DOI: 10.1103/physrevlett.122.087201] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 11/27/2018] [Indexed: 06/09/2023]
Abstract
Superconductivity in the iron pnictides emerges from metallic parent compounds exhibiting intertwined stripe-type magnetic order and nematic order, with itinerant electrons suggested to be essential for both. Here we use x-ray and neutron scattering to show that a similar intertwined state is realized in semiconducting KFe_{0.8}Ag_{1.2}Te_{2} (K_{5}Fe_{4}Ag_{6}Te_{10}) without itinerant electrons. We find that Fe atoms in KFe_{0.8}Ag_{1.2}Te_{2} form isolated 2×2 blocks, separated by nonmagnetic Ag atoms. Long-range magnetic order sets in below T_{N}≈35 K, with magnetic moments within the 2×2 Fe blocks ordering into the stripe-type configuration. A nematic order accompanies the magnetic transition, manifest as a structural distortion that breaks the fourfold rotational symmetry of the lattice. The nematic orders in KFe_{0.8}Ag_{1.2}Te_{2} and iron pnictide parent compounds are similar in magnitude and in how they relate to the magnetic order, indicating a common origin. Since KFe_{0.8}Ag_{1.2}Te_{2} is a semiconductor without itinerant electrons, this indicates that local-moment magnetic interactions are integral to its magnetic and nematic orders, and such interactions may play a key role in iron-based superconductivity.
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Affiliation(s)
- Yu Song
- Department of Physics, University of California, Berkeley, California 94720, USA
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Huibo Cao
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - B C Chakoumakos
- Neutron Scattering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
| | - Yang Zhao
- NIST Center for Neutron Research, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
- Department of Materials Science and Engineering, University of Maryland, College Park, Maryland 20742, USA
| | - Aifeng Wang
- Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, New York 11973, USA
| | - Hechang Lei
- Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, New York 11973, USA
| | - C Petrovic
- Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, New York 11973, USA
| | - Robert J Birgeneau
- Department of Physics, University of California, Berkeley, California 94720, USA
- Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
- Department of Materials Science and Engineering, University of California, Berkeley, California 94720, USA
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6
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DEMAND, a Dimensional Extreme Magnetic Neutron Diffractometer at the High Flux Isotope Reactor. CRYSTALS 2018. [DOI: 10.3390/cryst9010005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A two-dimensional (2D) Anger camera detector has been used at the HB-3A four-circle single-crystal neutron diffractometer at the High Flux Isotope Reactor (HFIR) since 2013. The 2D detector has enabled the capabilities of measuring sub-mm crystals and spin density maps, enhanced the efficiency of data collection and phase transition detection, and improved the signal-to-noise ratio. Recently, the HB-3A four-circle diffractometer has been undergoing a detector upgrade towards a much larger area, magnetic-field-insensitive, Anger camera detector. The instrument will become capable of doing single-crystal neutron diffraction under ultra-low temperatures (50 mK), magnetic fields (up to 8 T), electric fields (up to 11 kV/mm), and hydrostatic high pressures (up to 45 GPa). Furthermore, half-polarized neutron diffraction is also available to measure weak ferromagnetism and local site magnetic susceptibilities. With the new high-resolution 2D detector, the four-circle diffractometer has become more powerful for studying magnetic materials under extreme sample environment conditions; hence, it has been given a new name: DEMAND.
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Wang Z, Okada Y, O'Neal J, Zhou W, Walkup D, Dhital C, Hogan T, Clancy P, Kim YJ, Hu YF, Santos LH, Wilson SD, Trivedi N, Madhavan V. Disorder induced power-law gaps in an insulator-metal Mott transition. Proc Natl Acad Sci U S A 2018; 115:11198-11202. [PMID: 30322914 PMCID: PMC6217382 DOI: 10.1073/pnas.1808056115] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
A correlated material in the vicinity of an insulator-metal transition (IMT) exhibits rich phenomenology and a variety of interesting phases. A common avenue to induce IMTs in Mott insulators is doping, which inevitably leads to disorder. While disorder is well known to create electronic inhomogeneity, recent theoretical studies have indicated that it may play an unexpected and much more profound role in controlling the properties of Mott systems. Theory predicts that disorder might play a role in driving a Mott insulator across an IMT, with the emergent metallic state hosting a power-law suppression of the density of states (with exponent close to 1; V-shaped gap) centered at the Fermi energy. Such V-shaped gaps have been observed in Mott systems, but their origins are as-yet unknown. To investigate this, we use scanning tunneling microscopy and spectroscopy to study isovalent Ru substitutions in Sr3(Ir1-xRux)2O7 (0 ≤ x ≤ 0.5) which drive the system into an antiferromagnetic, metallic state. Our experiments reveal that many core features of the IMT, such as power-law density of states, pinning of the Fermi energy with increasing disorder, and persistence of antiferromagnetism, can be understood as universal features of a disordered Mott system near an IMT and suggest that V-shaped gaps may be an inevitable consequence of disorder in doped Mott insulators.
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Affiliation(s)
- Zhenyu Wang
- Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL 61801
- Frederick Seitz Materials Research Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801
| | - Yoshinori Okada
- Quantum Materials Science Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan
| | - Jared O'Neal
- Mathematics Department, The Ohio State University, Columbus, OH 43210
| | - Wenwen Zhou
- Department of Physics, Boston College, Chestnut Hill, MA 02467
| | - Daniel Walkup
- Center for Nanoscale Science and Technology, National Institute of Standards and Technology, Gaithersburg, MD 20899
| | - Chetan Dhital
- Department of Physics, Kennesaw State University, Marietta, GA 30060
| | - Tom Hogan
- Materials Department, University of California, Santa Barbara, CA 93106
| | - Patrick Clancy
- Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada
| | - Young-June Kim
- Department of Physics, University of Toronto, Toronto, ON M5S 1A7, Canada
| | - Y F Hu
- Canadian Light Source, Saskatoon, SK S7N 2V3, Canada
| | - Luiz H Santos
- Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL 61801
- Institute for Condensed Matter Theory, University of Illinois Urbana-Champaign, Urbana, IL 61801
| | - Stephen D Wilson
- Materials Department, University of California, Santa Barbara, CA 93106
| | - Nandini Trivedi
- Department of Physics, The Ohio State University, Columbus, Ohio 43210
| | - Vidya Madhavan
- Department of Physics, University of Illinois Urbana-Champaign, Urbana, IL 61801;
- Frederick Seitz Materials Research Laboratory, University of Illinois Urbana-Champaign, Urbana, IL 61801
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