arXiv subjects

Pablo Solís-Fernández

Publications and source records attributed to Pablo Solís-Fernández.

3 recordsLinked to original sources

arXiv2026

Layer-by-layer growth of highly aligned MoS$_2$ nanoribbon arrays

One-dimensional transition metal dichalcogenides (TMD) nanoribbons (NRs) offer a promising route to aggressive channel-width scaling in nanoscale transistors. However, controlling their layer number, a key determinant of device performance, has remained elusive. Here, we demonstrate a chemical vapor deposition (CVD) approach for layer-by-layer growth of MoS$_2$ NRs on a low-symmetry sapphire surface. This approach enables the oriented growth of single-crystalline bilayer NRs with a 2H stacking configuration, while maintaining widths below 20 nm. Increasing the MoO$_3$ precursor supply was found to be crucial for promoting the growth of the second and subsequent layers. By controlling the growth time and precursor supply, the layer number of MoS$_2$ NRs can be increased up to four layers. This method can also be extended to the growth of vertical MoS$_2$/WS$_2$ hetero-NRs. The average carrier mobility and current density of bilayer NR field-effect transistors (FETs) are two- and three-times higher than those of monolayer counterparts, respectively, and a maximum mobility of 86.7 cm$^2$V$^{-1}$s$^{-1}$ was attained with a bilayer NR-FET. These results highlight layer number as a key parameter for optimizing NR device performance. Our thickness-controlled growth strategy provides a general route towards TMD NR-based transistors with enhanced scalability and performance.

cond-mat.mtrl-sci↗

arXiv2025

Lattice-guided growth of dense arrays of aligned transition metal dichalcogenide nanoribbons with high catalytic reactivity

Transition metal dichalcogenides (TMDs) exhibit unique properties and potential applications when reduced to one-dimensional (1D) nanoribbons (NRs), owing to quantum confinement and high edge densities. However, effective growth methods for self-aligned TMD NRs are still lacking. We demonstrate a versatile approach for lattice-guided growth of dense, aligned MoS2 NR arrays via chemical vapor deposition (CVD) on anisotropic sapphire substrates, without tailored surface steps. This method enables the synthesis of NRs with widths below 10 nm and longitudinal axis parallel to the zigzag direction, being also extensible to the growth of WS2 NRs and MoS2-WS2 hetero-nanoribbons. Growth is influenced by both substrate and CVD temperature, indicating the role of anisotropic precursor diffusion and substrate interaction. The 1D nature of the NRs was asserted by the observation of Coulomb blockade at low temperature. Pronounced catalytic activity was observed at the edges of the NRs, indicating their promise for efficient catalysis.

cond-mat.mes-hall↗

arXiv2022

Determining the Twist Angle of Bilayer Graphene by Machine Learning Analysis of its Raman Spectrum

With the increasing interest in twisted bilayer graphene (tBLG) of the past years, fast, reliable, and non-destructive methods to precisely determine the twist angle are required. Raman spectroscopy potentially provides such method, given the large amount of information about the state of the graphene that is encoded in its Raman spectrum. However, changes in the Raman spectra induced by the stacking order can be very subtle, thus making the angle identification tedious. In this work, we propose the use of machine learning (ML) analysis techniques for the automated classification of the Raman spectrum of tBLG into a selected range of twist angles. The ML classification proposed here is low computationally demanding, providing fast and accurate results with ~99 % of agreement with the manual labelling of the spectra. The flexibility and non-invasive nature of the Raman measurements, paired with the predictive accuracy of the ML, is expected to facilitate the exploration of the nascent research of tBLG. Moreover, the present work showcases how the currently available open-source tools facilitate the study and integration of ML-based techniques.

cond-mat.mtrl-sci↗