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1. Terahertz conductivity of monolayer MoS$$_2$$

   https://doi.org/10.1007/s10825-023-02023-x  

   Mitra, S.; Avazpour, L.; Knezevic, I. (March 2023, Journal of Computational Electronics)

   We calculate the electrical conductivity of suspended and supported monolayer MoS2 at terahertz (THz) frequencies by means of EMC–FDTD, a multiphysics simulation tool combining an ensemble Monte Carlo (EMC) solver for electron transport and a finite-difference time-domain (FDTD) solver for full-wave electrodynamics. We investigate the role of carrier and impurity densities, as well as substrate choice (SiO2 or hexagonal boron nitride, hBN), in frequency-dependent electronic transport. Owing to the dominance of surface-optical-phonon scattering, MoS2 on SiO2 has the lowest static conductivity, but also the weakest overall frequency dependence of the conductivity. In fact, at high THz frequencies, the conductivity of MoS2 on SiO2 exceeds that of either suspended or hBN-supported MoS2. We extract the parameters for Drude-model fits to the conductivity versus frequency curves obtained from microscopic simulation, which may aid in the experimental efforts toward MoS2 THz applications. 

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2. Photophysics and Electronic Structure of Lateral Graphene/MoS ₂ and Metal/MoS ₂ Junctions

   https://doi.org/10.1021/acsnano.0c02527  

   Subramanian, Shruti; Campbell, Quinn T.; Moser, Simon K.; Kiemle, Jonas; Zimmermann, Philipp; Seifert, Paul; Sigger, Florian; Sharma, Deeksha; Al-Sadeg, Hala; Labella, Michael; et al (December 2020, ACS Nano)

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3. Origin of Nanoscale Friction Contrast between Supported Graphene, MoS ₂ , and a Graphene/MoS ₂ Heterostructure

   https://doi.org/10.1021/acs.nanolett.9b02035  

   Vazirisereshk, Mohammad R.; Ye, Han; Ye, Zhijiang; Otero-de-la-Roza, Alberto; Zhao, Meng-Qiang; Gao, Zhaoli; Johnson, A. T.; Johnson, Erin R.; Carpick, Robert W.; Martini, Ashlie (July 2019, Nano Letters)
4. Phonon Dephasing Dynamics in MoS ₂

   https://doi.org/10.1021/acs.nanolett.0c04368  

   Sun, Liuyang; Kumar, Parveen; Liu, Zeyu; Choi, Junho; Fang, Bin; Roesch, Sebastian; Tran, Kha; Casara, Joshua; Priego, Eduardo; Chang, Yu-Ming; et al (February 2021, Nano Letters)

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5. Two-Terminal MoS ₂ Memristor and the Homogeneous Integration with a MoS ₂ Transistor for Neural Networks

   https://doi.org/10.1021/acs.nanolett.2c05007  

   Fu, Shuai; Park, Ji-Hoon; Gao, Hongyan; Zhang, Tianyi; Ji, Xiang; Fu, Tianda; Sun, Lu; Kong, Jing; Yao, Jun (July 2023, Nano Letters)

   Memristors are promising candidates for constructing neural networks. However, their dissimilar working mechanism to that of the addressing transistors can result in a scaling mismatch, which may hinder efficient integration. Here, we demonstrate two-terminal MoS2 memristors that work with a charge-based mechanism similar to that in transistors, which enables the homogeneous integration with MoS2 transistors to realize one-transistor-one-memristor addressable cells for assembling programmable network. The homogenously integrated cells are implemented in a 2×2 network array to demonstrate the enabled addressability and programmability. The potential for assembling scalable network is evaluated in a simulated neural network using obtained realistic device parameters, which achieves over 91% pattern recognition accuracy. This study also reveals a generic mechanism and strategy that can be applied to other semiconducting devices for the engineering and homogeneous integration of memristive systems. 

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