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Mechanically reliable and electronically uniform monolayer MoS2 by passivation and defect healing

Boran Kumral, Nima Barri, Pedro Guerra Demingos, Gokay Adabasi, Andrew Grishko, Guorui Wang, Jimpei Kawase, Momoko Onodera, Tomoki Machida, Mehmet Z. Baykara, Chandra Veer Singh, Tobin Filleter

2025Nature Communications15 citationsDOIOpen Access PDF

Abstract

Monolayer molybdenum disulfide (MoS₂), a two-dimensional transition metal dichalcogenide (2D TMD), is at the forefront of logic device scaling efforts due to its semiconducting properties, good carrier mobility, and atomically thin structure. However, the high defect density of monolayer MoS2 hinders its reliability for long-term, device-scale applications. Here, we show that a superacid treatment, previously shown to enhance the photoluminescence efficiency of sulfur-based 2D TMDs by two orders of magnitude, also improves the mechanical reliability and electronic uniformity of monolayer MoS₂. Treated samples exhibit a ~2× increase in static fatigue reliability, a ~10× improvement in cyclic wear reliability, and no premature failure during mechanical testing. X-ray photoelectron spectroscopy confirms reduced defect density, while ab initio molecular dynamics and density functional theory suggest that passivation delays failure propagation and reduces vacancy-induced stress. Finally, atomic-resolution conductive atomic force microscopy shows a drastically more uniform current distribution due to elimination of midgap states. Nonoxidizing organic superacid treatments of 2D transition metal dichalcogenides have been shown to drastically boost their electrical and optical characteristics while passivating and repairing defects. Here, the authors demonstrate that these treatments can also be leveraged to boost the mechanical reliability and atomic-scale electronic uniformity of MoS2 monolayers.

Topics & Concepts

PassivationMonolayerMaterials scienceNanotechnologyOptoelectronicsLayer (electronics)2D Materials and ApplicationsMXene and MAX Phase MaterialsAdvanced Memory and Neural Computing