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Unraveling the Correlation between Raman and Photoluminescence in Monolayer MoS2 through Machine-Learning Models.
Lu, Ang-Yu; Martins, Luiz Gustavo Pimenta; Shen, Pin-Chun; Chen, Zhantao; Park, Ji-Hoon; Xue, Mantian; Han, Jinchi; Mao, Nannan; Chiu, Ming-Hui; Palacios, Tomás; Tung, Vincent; Kong, Jing.
Afiliación
  • Lu AY; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Martins LGP; Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Shen PC; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Chen Z; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Park JH; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Xue M; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Han J; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Mao N; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Chiu MH; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Palacios T; Physical Science and Engineering Division, King Abdullah University of Science & Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
  • Tung V; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
  • Kong J; Physical Science and Engineering Division, King Abdullah University of Science & Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
Adv Mater ; 34(34): e2202911, 2022 Aug.
Article en En | MEDLINE | ID: mdl-35790036
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Adv Mater Asunto de la revista: BIOFISICA / QUIMICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Alemania
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Adv Mater Asunto de la revista: BIOFISICA / QUIMICA Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Alemania