David Tománek
Phys. Rev. Applied 13, 030001 (2020) - Published 13 March, 2020
Guest Editor David Tománek introduces a collection of papers in Physical Review Applied and Physical Review Materials on two-dimensional materials and devices, in a snapshot of the leading edge of this hot field.
Matthew W. Daniels, Advait Madhavan, Philippe Talatchian, Alice Mizrahi, and Mark D. Stiles
Phys. Rev. Applied 13, 034016 (2020) - Published 5 March, 2020
Most computing schemes that employ superparamagnetic tunnel junctions control them with analog currents, leading to substantial Ohmic losses and requiring digital-to-analog converters. Here the authors forego current control and embed these junctions in digital circuits to produce programmable randomness, which leads to a neural network that can recognize handwritten digits at only 150 nJ per inference. This energy efficiency is made possible by the general insight that, while nanodevices provide useful dynamics for innovative computing, understanding their integration with digital logic systems is crucial to developing viable applications.
H.-H. Lin, A. Croy, R. Gutierrez, C. Joachim, and G. Cuniberti
Phys. Rev. Applied 13, 034024 (2020) - Published 9 March, 2020
Even in 2020, there’s still some room at the bottom: The miniaturization of machines remains an important goal that attracts interest in many fields. In particular, making ultrasmall gears to transmit mechanical motion is a challenge, with molecular gears being the ultimate target. Motivated by recent experimental studies on molecule gears, the authors employ atomistic molecular dynamics simulations and a nearly-rigid-body model to obtain insights into the dynamics of both single gears and trains of them. A train of such gears exhibits three qualitatively different regimes of motion, depending on the magnitude of the applied torque.
I. Ozfidan et al.
Phys. Rev. Applied 13, 034037 (2020) - Published 16 March, 2020
Demonstration of a Hamiltonian—one for which there exists no local basis in which all off-diagonal elements are nonpositive—is an important step toward the development of quantum annealers that are more computationally powerful, or even universal. (This condition is tied to the “sign problem” in quantum Monte Carlo techniques.) This work presents the implementation of such a Hamiltonian by coupling two flux qubits both inductively and capacitively. The signature of nonstoquastic behavior is observed through destructive interference in quantum coherent oscillations. This result would seem to bear strong implications for scalable quantum computing.
Muchen Xu, Andrew Grabowski, Ning Yu, Gintare Kerezyte, Jeong-Won Lee, Byron R. Pfeifer, and Chang-Jin “CJ” Kim
Phys. Rev. Applied 13, 034056 (2020) - Published 23 March, 2020
Trapping air under water, superhydrophobic surfaces on boats have long been coveted for hydrodynamic drag reduction without bubble injection. Despite many reports of drag reduction in lab tests, such surfaces have not been successful in real-world tests on open water, at high Reynolds numbers. The authors develop hydrophobic microtrenches that provide a large slip, with a subtle detail that maximizes air retention. Replacing a portion of a motorboat’s hull with the developed surface, they obtain large drag reduction under high-speed flows on the open sea. This has significant implications for energy efficiency in oceanic shipping.
Mattis Reisner, Do Hyeok Jeon, Carsten Schindler, Henning Schomerus, Fabrice Mortessagne, Ulrich Kuhl, and Tsampikos Kottos
Phys. Rev. Applied 13, 034067 (2020) - Published 26 March, 2020
Merging concepts from topological photonics with that of self-induced violation of time-reversal symmetry emerging from nonlinear losses, the authors aim to design receiver protectors (RPs) that shield sensitive electronics from high-power radiation. The proposed RPs are transparent to low-power incident signals, yet self-protected against electrical breakdown and overheating due to high-power input, which triggers an underdamping-to-overdamping transition and complete reflection of the incident radiation. Such an RP can be utilized to safeguard a broad range of sensitive communication systems, including radar and lidar installations and reflection altimeters.
Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre, J.P. Dodson, E.R. MacQuarrie, D.E. Savage, M.G. Lagally, S.N. Coppersmith, Mark A. Eriksson, and Jacob M. Taylor
Phys. Rev. Applied 13, 034075 (2020) - Published 31 March, 2020
A primary obstacle in scaling up quantum computing platforms based on semiconductor quantum dots (QDs) is the full automation of initialization and control. Using ideas from machine learning (ML), pattern recognition, and optimization, the authors implement an autotuning protocol that needs no human intervention to navigate between QD states in real time. A convolutional neural network identifies QD states from measurements; importantly, the network is trained exclusively on simulated data, and the scans used are significantly smaller that in manual tuning, and hence much faster. This development is critical to moving up to larger numbers of quantum dots.
J. Marconi, E. Riva, M. Di Ronco, G. Cazzulani, F. Braghin, and M. Ruzzene
Phys. Rev. Applied 13, 031001 (2020) - Published 27 March, 2020
The breaking of reciprocity in elastic wave propagation can be achieved through modulation of stiffness in space and time, which is reflected in asymmetrical Bloch diagrams. Here this principle is experimentally demonstrated using controlled piezoelectric devices, which when connected to negative-capacitance circuits alter the effective material parameters locally, in the form of a traveling modulation. Thus the authors can tune nonreciprocal band gaps that are1 kHz wide and span a frequency range of 8–11 kHz, for one-way transmission of vibrations in phononic communication systems.
Meng Wen, Yousef I. Salamin, and Christoph H. Keitel
Phys. Rev. Applied 13, 034001 (2020) - Published 2 March, 2020
Gaurav Bhole, Takahiro Tsunoda, Peter J. Leek, and Jonathan A. Jones
Phys. Rev. Applied 13, 034002 (2020) - Published 2 March, 2020
Ashok Akarapu, Rohit Prakash Nighot, Lalsingh Devsoth, Mukul Yadav, Prem Pal, and Ashok Kumar Pandey
Phys. Rev. Applied 13, 034003 (2020) - Published 2 March, 2020
Mohammad Hosein Fakheri, Ali Abdolali, and Hooman Barati Sedeh
Phys. Rev. Applied 13, 034004 (2020) - Published 2 March, 2020
Ziheng Zhou, Yue Li, Ehsan Nahvi, Hao Li, Yijing He, Iñigo Liberal, and Nader Engheta
Phys. Rev. Applied 13, 034005 (2020) - Published 3 March, 2020
Badreddine Ratni, Zhuochao Wang, Kuang Zhang, Xumin Ding, André de Lustrac, Gérard-Pascal Piau, and Shah Nawaz Burokur
Phys. Rev. Applied 13, 034006 (2020) - Published 3 March, 2020
Yu Zhou, Zhihui Peng, Yuta Horiuchi, O.V. Astafiev, and J.S. Tsai
Phys. Rev. Applied 13, 034007 (2020) - Published 3 March, 2020
Xiao-Ling Pang, Ai-Lin Yang, Chao-Ni Zhang, Jian-Peng Dou, Hang Li, Jun Gao, and Xian-Min Jin
Phys. Rev. Applied 13, 034008 (2020) - Published 3 March, 2020
Shifang Guo, Xuyan Guo, Xin Wang, Xuan Du, Pengying Wu, Ayache Bouakaz, and Mingxi Wan
Phys. Rev. Applied 13, 034009 (2020) - Published 3 March, 2020
A. Gardill, M.C. Cambria, and S. Kolkowitz
Phys. Rev. Applied 13, 034010 (2020) - Published 4 March, 2020
Ramya Gurunathan, Riley Hanus, Maxwell Dylla, Ankita Katre, and G. Jeffrey Snyder
Phys. Rev. Applied 13, 034011 (2020) - Published 4 March, 2020
C. Guarcello and F.S. Bergeret
Phys. Rev. Applied 13, 034012 (2020) - Published 4 March, 2020
Hsin-Pin Lo, Takuya Ikuta, Nobuyuki Matsuda, Toshimori Honjo, William J. Munro, and Hiroki Takesue
Phys. Rev. Applied 13, 034013 (2020) - Published 4 March, 2020
D. Yu. Karpenkov, A. Yu. Karpenkov, K. P. Skokov, I. A. Radulov, M. Zheleznyi, T. Faske, and O. Gutfleisch
Phys. Rev. Applied 13, 034014 (2020) - Published 5 March, 2020
Matthew W. Daniels, Advait Madhavan, Philippe Talatchian, Alice Mizrahi, and Mark D. Stiles
Phys. Rev. Applied 13, 034016 (2020) - Published 5 March, 2020
Most computing schemes that employ superparamagnetic tunnel junctions control them with analog currents, leading to substantial Ohmic losses and requiring digital-to-analog converters. Here the authors forego current control and embed these junctions in digital circuits to produce programmable randomness, which leads to a neural network that can recognize handwritten digits at only 150 nJ per inference. This energy efficiency is made possible by the general insight that, while nanodevices provide useful dynamics for innovative computing, understanding their integration with digital logic systems is crucial to developing viable applications.
Anqi Huang, Ruoping Li, Vladimir Egorov, Serguei Tchouragoulov, Krtin Kumar, and Vadim Makarov
Phys. Rev. Applied 13, 034017 (2020) - Published 5 March, 2020
Sebastian Reichert, Jens Flemming, Qingzhi An, Yana Vaynzof, Jan-Frederik Pietschmann, and Carsten Deibel
Phys. Rev. Applied 13, 034018 (2020) - Published 6 March, 2020
Lan Dong, Qing Xi, Jun Zhou, Xiangfan Xu, and Baowen Li
Phys. Rev. Applied 13, 034019 (2020) - Published 6 March, 2020
Y. Luan, L. McDermott, F. Hu, and Z. Fei
Phys. Rev. Applied 13, 034020 (2020) - Published 6 March, 2020
Yong Zhang, Cheng-Long Zhou, Hong-Liang Yi, and He-Ping Tan
Phys. Rev. Applied 13, 034021 (2020) - Published 9 March, 2020
Thibault Capelle, Emmanuel Flurin, Edouard Ivanov, Jose Palomo, Michael Rosticher, Sheon Chua, Tristan Briant, Pierre-François Cohadon, Antoine Heidmann, Thibaut Jacqmin, and Samuel Deléglise
Phys. Rev. Applied 13, 034022 (2020) - Published 9 March, 2020
Binke Xia, Jingzheng Huang, Chen Fang, Hongjing Li, and Guihua Zeng
Phys. Rev. Applied 13, 034023 (2020) - Published 9 March, 2020
H.-H. Lin, A. Croy, R. Gutierrez, C. Joachim, and G. Cuniberti
Phys. Rev. Applied 13, 034024 (2020) - Published 9 March, 2020
Even in 2020, there’s still some room at the bottom: The miniaturization of machines remains an important goal that attracts interest in many fields. In particular, making ultrasmall gears to transmit mechanical motion is a challenge, with molecular gears being the ultimate target. Motivated by recent experimental studies on molecule gears, the authors employ atomistic molecular dynamics simulations and a nearly-rigid-body model to obtain insights into the dynamics of both single gears and trains of them. A train of such gears exhibits three qualitatively different regimes of motion, depending on the magnitude of the applied torque.
Martin Franckié and Jérôme Faist
Phys. Rev. Applied 13, 034025 (2020) - Published 10 March, 2020
Young Il Joe, Yizhi Fang, Sangjun Lee, Stella X.L. Sun, Gilberto A. de la Peña, William B. Doriese, Kelsey M. Morgan, Joseph W. Fowler, Leila R. Vale, Fanny Rodolakis, Jessica L. McChesney, Joel N. Ullom, Daniel S. Swetz, and Peter Abbamonte
Phys. Rev. Applied 13, 034026 (2020) - Published 10 March, 2020
Yenal Karaaslan, Haluk Yapicioglu, and Cem Sevik
Phys. Rev. Applied 13, 034027 (2020) - Published 10 March, 2020
Alireza Haghighat, Patrick Huber, Shengchao Li, Jonathan M. Link, Camillo Mariani, Jaewon Park, and Tulasi Subedi
Phys. Rev. Applied 13, 034028 (2020) - Published 11 March, 2020
Haowei Shi, Zheshen Zhang, and Quntao Zhuang
Phys. Rev. Applied 13, 034029 (2020) - Published 11 March, 2020
Qing Lin, Bing He, and Min Xiao
Phys. Rev. Applied 13, 034030 (2020) - Published 11 March, 2020
J. Torrejon, A. Solignac, C. Chopin, J. Moulin, A. Doll, E. Paul, C. Fermon, and M. Pannetier-Lecoeur
Phys. Rev. Applied 13, 034031 (2020) - Published 12 March, 2020
A. Romanenko, R. Pilipenko, S. Zorzetti, D. Frolov, M. Awida, S. Belomestnykh, S. Posen, and A. Grassellino
Phys. Rev. Applied 13, 034032 (2020) - Published 12 March, 2020
Shan Lin, Qinghua Zhang, Manuel A. Roldan, Sujit Das, Timothy Charlton, Michael R. Fitzsimmons, Qiao Jin, Sisi Li, Zhenping Wu, Shuang Chen, Haizhong Guo, Xin Tong, Meng He, Chen Ge, Can Wang, Lin Gu, Kui-juan Jin, and Er-Jia Guo
Phys. Rev. Applied 13, 034033 (2020) - Published 12 March, 2020
Xianqing Lin and David Tománek
Phys. Rev. Applied 13, 034034 (2020) - Published 13 March, 2020
Just as the propagation of coherent photons can be manipulated by a diffraction grid, so can propagation of coherent electrons be manipulated by periodic gating in bilayer graphene. Similar behavior should be expected of coherent electrons and photons, the main difference being that for electrons, the electrostatic potential can be modulated externally. The authors’ computations of the resistance map as a function of the voltage applied to the extended bottom gate, and to the periodic top gate, reveal an intriguing pattern reminiscent of Fabry-Perot interferometry. It seems, then, that periodically gated bilayer graphene could function as a distributed Bragg reflector for electrons.
H. J. Waring, N. A. B. Johansson, I. J. Vera-Marun, and T. Thomson
Phys. Rev. Applied 13, 034035 (2020) - Published 13 March, 2020
Ali Alkurdi, Julien Lombard, François Detcheverry, and Samy Merabia
Phys. Rev. Applied 13, 034036 (2020) - Published 13 March, 2020
I. Ozfidan et al.
Phys. Rev. Applied 13, 034037 (2020) - Published 16 March, 2020
Demonstration of a Hamiltonian—one for which there exists no local basis in which all off-diagonal elements are nonpositive—is an important step toward the development of quantum annealers that are more computationally powerful, or even universal. (This condition is tied to the “sign problem” in quantum Monte Carlo techniques.) This work presents the implementation of such a Hamiltonian by coupling two flux qubits both inductively and capacitively. The signature of nonstoquastic behavior is observed through destructive interference in quantum coherent oscillations. This result would seem to bear strong implications for scalable quantum computing.
Lijun Zhu, Lujun Zhu, D.C. Ralph, and R.A. Buhrman
Phys. Rev. Applied 13, 034038 (2020) - Published 16 March, 2020
Tribhuwan Pandey and David S. Parker
Phys. Rev. Applied 13, 034039 (2020) - Published 16 March, 2020
Zhifeng Zhu, Kaiming Cai, Jiefang Deng, Venkata Pavan Kumar Miriyala, Hyunsoo Yang, Xuanyao Fong, and Gengchiau Liang
Phys. Rev. Applied 13, 034040 (2020) - Published 16 March, 2020
Jinwu Wei, Congli He, Xiao Wang, Hongjun Xu, Yizhou Liu, Yao Guang, Caihua Wan, Jiafeng Feng, Guoqiang Yu, and Xiufeng Han
Phys. Rev. Applied 13, 034041 (2020) - Published 17 March, 2020
Jianchen Zi, Yanfeng Li, Xi Feng, Quan Xu, Hongchao Liu, Xi-Xiang Zhang, Jiaguang Han, and Weili Zhang
Phys. Rev. Applied 13, 034042 (2020) - Published 17 March, 2020
Xi Zhao, Su-Ju Wang, Wei-Wei Yu, Hui Wei, Changli Wei, Bincheng Wang, Jigen Chen, and C. D. Lin
Phys. Rev. Applied 13, 034043 (2020) - Published 17 March, 2020
Sudipta Mondal, Qiliang Wei, Muhammad Ashiq Fareed, Hassan A. Hafez, Xavier Ropagnol, Shuhui Sun, Subhendu Kahaly, and Tsuneyuki Ozaki
Phys. Rev. Applied 13, 034044 (2020) - Published 18 March, 2020
Yu-Qin Chen, Kai-Li Ma, Yi-Cong Zheng, Jonathan Allcock, Shengyu Zhang, and Chang-Yu Hsieh
Phys. Rev. Applied 13, 034045 (2020) - Published 18 March, 2020
Hang Li, Collins Ashu Akosa, Peng Yan, Yuanxu Wang, and Zhenxiang Cheng
Phys. Rev. Applied 13, 034046 (2020) - Published 18 March, 2020
Jun Lan, Liwei Wang, Xiaowei Zhang, Minghui Lu, and Xiaozhou Liu
Phys. Rev. Applied 13, 034047 (2020) - Published 18 March, 2020
Jimy Encomendero, Vladimir Protasenko, Farhan Rana, Debdeep Jena, and Huili Grace Xing
Phys. Rev. Applied 13, 034048 (2020) - Published 19 March, 2020
Gianluca Costagliola, Roberto Guarino, Federico Bosia, Konstantinos Gkagkas, and Nicola M. Pugno
Phys. Rev. Applied 13, 034049 (2020) - Published 19 March, 2020
Huiyan Peng, Senlin Liu, Yuze Wu, Yi Yan, Zichun Zhou, Xiaochao Li, Qiaoliang Bao, Lin Xu, and Huanyang Chen
Phys. Rev. Applied 13, 034050 (2020) - Published 19 March, 2020
Xiangjun Xing, Yan Zhou, and H.B. Braun
Phys. Rev. Applied 13, 034051 (2020) - Published 19 March, 2020
Takahiro Moriyama, Yu Shiratsuchi, Tatsuya Iino, Hikaru Aono, Motohiro Suzuki, Tetsuya Nakamura, Yoshinori Kotani, Ryoichi Nakatani, Kohji Nakamura, and Teruo Ono
Phys. Rev. Applied 13, 034052 (2020) - Published 20 March, 2020
M.D. Davydova, P.N. Skirdkov, K.A. Zvezdin, Jong-Ching Wu, Sheng-Zhe Ciou, Yi-Ru Chiou, Lin-Xiu Ye, Te-Ho Wu, Ramesh Chandra Bhatt, A.V. Kimel, and A.K. Zvezdin
Phys. Rev. Applied 13, 034053 (2020) - Published 20 March, 2020
Chao Zeng, Yong Sun, Guo Li, Yunhui Li, Haitao Jiang, Yaping Yang, and Hong Chen
Phys. Rev. Applied 13, 034054 (2020) - Published 20 March, 2020
Rachele Zaccherini, Andrea Colombi, Antonio Palermo, Vasilis K. Dertimanis, Alessandro Marzani, Henrik R. Thomsen, Bozidar Stojadinovic, and Eleni N. Chatzi
Phys. Rev. Applied 13, 034055 (2020) - Published 23 March, 2020
Muchen Xu, Andrew Grabowski, Ning Yu, Gintare Kerezyte, Jeong-Won Lee, Byron R. Pfeifer, and Chang-Jin “CJ” Kim
Phys. Rev. Applied 13, 034056 (2020) - Published 23 March, 2020
Trapping air under water, superhydrophobic surfaces on boats have long been coveted for hydrodynamic drag reduction without bubble injection. Despite many reports of drag reduction in lab tests, such surfaces have not been successful in real-world tests on open water, at high Reynolds numbers. The authors develop hydrophobic microtrenches that provide a large slip, with a subtle detail that maximizes air retention. Replacing a portion of a motorboat’s hull with the developed surface, they obtain large drag reduction under high-speed flows on the open sea. This has significant implications for energy efficiency in oceanic shipping.
Stuart Watt and Mikhail Kostylev
Phys. Rev. Applied 13, 034057 (2020) - Published 23 March, 2020
Antoine Riaud, Wei Wang, Anh L.P. Thai, and Valerie Taly
Phys. Rev. Applied 13, 034058 (2020) - Published 24 March, 2020
Yifei Yu, Tamzid Minhaj, Lujun Huang, Yiling Yu, and Linyou Cao
Phys. Rev. Applied 13, 034059 (2020) - Published 24 March, 2020
M. Yu. Basalaev, V.I. Yudin, A.V. Taichenachev, M.I. Vaskovskaya, D.S. Chuchelov, S.A. Zibrov, V.V. Vassiliev, and V.L. Velichansky
Phys. Rev. Applied 13, 034060 (2020) - Published 24 March, 2020
Chen-Rong Liu, Liang Huang, Honggang Luo, and Ying-Cheng Lai
Phys. Rev. Applied 13, 034061 (2020) - Published 25 March, 2020
Markus Rosskopf, Till Mohr, and Wolfgang Elsäßer
Phys. Rev. Applied 13, 034062 (2020) - Published 25 March, 2020
Zhixiang Mao, Haiyu Yu, Meng Xia, Shengzhe Pan, Di Wu, Yaling Yin, Yong Xia, and Jianping Yin
Phys. Rev. Applied 13, 034063 (2020) - Published 25 March, 2020
Ryo Ishikawa, Naoya Shibata, Takashi Taniguchi, and Yuichi Ikuhara
Phys. Rev. Applied 13, 034064 (2020) - Published 26 March, 2020
Qun Wei, Guang Yang, and Xihong Peng
Phys. Rev. Applied 13, 034065 (2020) - Published 26 March, 2020
Or Shafir, Alexey Shopin, and Ilya Grinberg
Phys. Rev. Applied 13, 034066 (2020) - Published 26 March, 2020
Mattis Reisner, Do Hyeok Jeon, Carsten Schindler, Henning Schomerus, Fabrice Mortessagne, Ulrich Kuhl, and Tsampikos Kottos
Phys. Rev. Applied 13, 034067 (2020) - Published 26 March, 2020
Merging concepts from topological photonics with that of self-induced violation of time-reversal symmetry emerging from nonlinear losses, the authors aim to design receiver protectors (RPs) that shield sensitive electronics from high-power radiation. The proposed RPs are transparent to low-power incident signals, yet self-protected against electrical breakdown and overheating due to high-power input, which triggers an underdamping-to-overdamping transition and complete reflection of the incident radiation. Such an RP can be utilized to safeguard a broad range of sensitive communication systems, including radar and lidar installations and reflection altimeters.
Arne Hollmann, Tom Struck, Veit Langrock, Andreas Schmidbauer, Floyd Schauer, Tim Leonhardt, Kentarou Sawano, Helge Riemann, Nikolay V. Abrosimov, Dominique Bougeard, and Lars R. Schreiber
Phys. Rev. Applied 13, 034068 (2020) - Published 27 March, 2020
G.A.H. Wetzelaer
Phys. Rev. Applied 13, 034069 (2020) - Published 27 March, 2020
J.P. Vasco, D. Gerace, K. Seibold, and V. Savona
Phys. Rev. Applied 13, 034070 (2020) - Published 30 March, 2020
Arash Sayyah, Mohammad Mirzadeh, Yi Jiang, Warren V. Gleason, William C. Rice, and Martin Z. Bazant
Phys. Rev. Applied 13, 034071 (2020) - Published 30 March, 2020
Yumeng Yang, Hang Xie, Yanjun Xu, Ziyan Luo, and Yihong Wu
Phys. Rev. Applied 13, 034072 (2020) - Published 30 March, 2020
M.M. Aziz and C. McKeever
Phys. Rev. Applied 13, 034073 (2020) - Published 30 March, 2020
Shoma Tateno and Yukio Nozaki
Phys. Rev. Applied 13, 034074 (2020) - Published 31 March, 2020
Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre, J.P. Dodson, E.R. MacQuarrie, D.E. Savage, M.G. Lagally, S.N. Coppersmith, Mark A. Eriksson, and Jacob M. Taylor
Phys. Rev. Applied 13, 034075 (2020) - Published 31 March, 2020
A primary obstacle in scaling up quantum computing platforms based on semiconductor quantum dots (QDs) is the full automation of initialization and control. Using ideas from machine learning (ML), pattern recognition, and optimization, the authors implement an autotuning protocol that needs no human intervention to navigate between QD states in real time. A convolutional neural network identifies QD states from measurements; importantly, the network is trained exclusively on simulated data, and the scans used are significantly smaller that in manual tuning, and hence much faster. This development is critical to moving up to larger numbers of quantum dots.
Ivan M. Sopko, Daria O. Ignatyeva, Grigory A. Knyazev, and Vladimir I. Belotelov
Phys. Rev. Applied 13, 034076 (2020) - Published 31 March, 2020
Mark Um, Qi Zhao, Junhua Zhang, Pengfei Wang, Ye Wang, Mu Qiao, Hongyi Zhou, Xiongfeng Ma, and Kihwan Kim
Phys. Rev. Applied 13, 034077 (2020) - Published 31 March, 2020
Yong-Jun Qian, De-Yong He, Shuang Wang, Wei Chen, Zhen-Qiang Yin, Guang-Can Guo, and Zheng-Fu Han
Phys. Rev. Applied 13, 039901 (2020) - Published 20 March, 2020
