Journal article

Liquid-Metal Synthesized Ultrathin SnS Layers for High-Performance Broadband Photodetectors

Vaishnavi Krishnamurthi, Hareem Khan, Taimur Ahmed, Ali Zavabeti, Sherif Abdulkader Tawfik, Shubhendra Kumar Jain, Michelle JS Spencer, Sivacarendran Balendhran, Kenneth B Crozier, Ziyuan Li, Lan Fu, Md Mohiuddin, Mei Xian Low, Babar Shabbir, Andreas Boes, Arnan Mitchell, Christopher F McConville, Yongxiang Li, Kourosh Kalantar-Zadeh, Nasir Mahmood Show all

Advanced Materials | Wiley | Published : 2020


Atomically thin materials face an ongoing challenge of scalability, hampering practical deployment despite their fascinating properties. Tin monosulfide (SnS), a low‐cost, naturally abundant layered material with a tunable bandgap, displays properties of superior carrier mobility and large absorption coefficient at atomic thicknesses, making it attractive for electronics and optoelectronics. However, the lack of successful synthesis techniques to prepare large‐area and stoichiometric atomically thin SnS layers (mainly due to the strong interlayer interactions) has prevented exploration of these properties for versatile applications. Here, SnS layers are printed with thicknesses varying from ..

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Awarded by ARC

Funding Acknowledgements

The authors would like to acknowledge support from the ARC Discovery Project schemes DP180102752 (Y.L., M.J.S.S.), DP180104141 (S.B. and K.C.), DP170102138 (K.K.Z.) and the RMIT Vice Chancellor Fellowships (N.M. and S.W.). K.K.Z. also acknowledges support from the Australian Research Council Centre of Excellence FLEET. This work was performed in part at the RMIT Micro Nano Research Facility (MNRF) in the Victorian Node of the Australian National Fabrication Facility (ANFF). The authors also acknowledge the facilities, and the scientific and technical assistance of the RMIT Microscopy & Microanalysis Facility (RMMF), a linked laboratory of Microscopy Australia. The DFT computation was supported by the Multi-modal Australian Sciences Imaging and Visualisation Environment (MASSIVE) ( and was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which was supported by the Australian Government. Additional support and work were provided by the Pawsey Supercomputing Centre with funding from the Australian Government and the Government of Western Australia.