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Phonon-Suppressed Auger Scattering of Charge Carriers in Defective Two-Dimensional Transition Metal Dichalcogenides.

Linqiu LiMing-Fu LinXiang ZhangAlexander BritzAravind KrishnamoorthyRuru MaRajiv K KaliaAiichiro NakanoPriya VashishtaPulickel M AjayanMatthias C HoffmannDavid M FritzUwe BergmannOleg V Prezhdo
Published in: Nano letters (2019)
Two-dimensional transition metal dichalcogenides (TMDs) draw strong interest in materials science, with applications in optoelectronics and many other fields. Good performance requires high carrier concentrations and long lifetimes. However, high concentrations accelerate energy exchange between charged particles by Auger-type processes, especially in TMDs where many-body interactions are strong, thus facilitating carrier trapping. We report time-resolved optical pump-THz probe measurements of carrier lifetimes as a function of carrier density. Surprisingly, the lifetime reduction with increased density is very weak. It decreases only by 20% when we increase the pump fluence 100 times. This unexpected feature of the Auger process is rationalized by our time-domain ab initio simulations. The simulations show that phonon-driven trapping competes successfully with the Auger process. On the one hand, trap states are relatively close to band edges, and phonons accommodate efficiently the electronic energy during the trapping. On the other hand, trap states localize around defects, and the overlap of trapped and free carriers is small, decreasing carrier-carrier interactions. At low carrier densities, phonons provide the main charge trapping mechanism, decreasing carrier lifetimes compared to defect-free samples. At high carrier densities, phonons suppress Auger processes and lower the dependence of the trapping rate on carrier density. Our results provide theoretical insights into the diverse roles played by phonons and Auger processes in TMDs and generate guidelines for defect engineering to improve device performance at high carrier densities.
Keyphrases
  • transition metal
  • public health
  • machine learning
  • molecular dynamics
  • clinical practice