This work reports an electronic and micro-structural study of an appealing system for optoelectronics: tungsten disulphide (WS₂) on epitaxial graphene (EG) on SiC(0001).

Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS₂, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur ( $${\rm{Co}}_{{{{{{{{\rm{S}}}}}}}}}^{0}$$Co S 0 ) and fabricate it with scanning tunneling microscopy (STM). The $${\rm{Co}}_{{{{{{{{\rm{S}}}}}}}}}^{0}$$Co S 0 electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects.

Stacking van der Waals crystals allows for the on-demand creation of a periodic potential landscape to tailor the transport of quasiparticle excitations. We investigate the diffusion of photoexcited electron-hole pairs, or excitons, at the interface of WS₂/WSe₂ van der Waals heterostructure over a wide range of temperatures. We observe the appearance of distinct interlayer excitons for parallel and antiparallel stacking and track their diffusion through spatially and temporally resolved photoluminescence spectroscopy from 30 to 250 K. While the measured exciton diffusivity decreases with temperature, it surprisingly plateaus below 90 K. Our observations cannot be explained by classical models like hopping in the moiré potential. A combination of ab initio theory and molecular dynamics simulations suggests that low-energy phonons arising from the mismatched lattices of moiré heterostructures, also known as phasons, play a key role in describing and understanding this anomalous behavior of exciton diffusion. Our observations indicate that the moiré potential landscape is dynamic down to very low temperatures and that the phason modes can enable efficient transport of energy in the form of excitons.

One of the major issues in graphene-based optoelectronics is to scale-up high-performing devices. In this work, we report an original approach for the fabrication of efficient optoelectronic devices from scalable tungsten disulfide (WS₂)/graphene heterostructures. Our approach allows for the patterned growth of WS₂ on graphene and facilitates the realization of ohmic contacts. Photodetectors fabricated with WS₂ on epitaxial graphene on silicon carbide (SiC) present, when illuminated with red light, a maximum responsivity R ∼220 A W⁻¹, a detectivity D* ∼2.0 × 10⁹ Jones and a −3 dB bandwidth of 250 Hz. The retrieved detectivity is 3 orders of magnitude higher than that obtained with graphene-only devices at the same wavelength. For shorter illumination wavelengths we observe a persistent photocurrent with a nearly complete charge retention, which originates from deep trap levels in the SiC substrate. This work ultimately demonstrates that WS₂/graphene optoelectronic devices with promising performances can be obtained in a scalable manner. Furthermore, by combining wavelength-selective memory, enhanced responsivity and fast detection, this system is of interest for the implementation of 2d-based data storage devices.

The scalable synthesis of two-dimensional (2D) materials remains a key challenge for their integration into solid-state technology. While exfoliation techniques have driven much of the scientific progress, they are impractical for large-scale applications. Advances in artificial intelligence (AI) now offer new strategies for materials synthesis. This study explores the use of an artificial neural network (ANN) trained via evolutionary methods to optimize graphene growth. The ANN autonomously refines a time-dependent synthesis protocol without prior knowledge of effective recipes. The evaluation is based on Raman spectroscopy, where outcomes resembling monolayer graphene receive higher scores. This feedback mechanism enables iterative improvements in synthesis conditions, progressively enhancing sample quality. By integrating AI-driven optimization into material synthesis, this work contributes to the development of scalable approaches for 2D materials, demonstrating the potential of machine learning in guiding experimental processes.