Electronic Coupling and Charge-Transfer Landscape of Graphene on Ge(001)/Si(001): Multiscale Analysis Assisted by Machine Learning
Abstract
Understanding and controlling charge transfer at graphene-semiconductor interfaces is essential for the integration of two-dimensional materials into silicon-compatible technologies. Here, we combine ultraviolet photoelectron spectroscopy (UPS), Kelvin probe force microscopy (KPFM), angle-resolved photoemission spectroscopy (ARPES), scanning tunneling spectroscopy (STS) and density functional theory (DFT) to resolve work-function modulation and electronic coupling in graphene grown on Ge(001)/Si(001). UPS and KPFM reveal a spatially non-uniform work-function landscape correlated with the nanofaceted morphology of the substrate. ARPES and DFT calculations for the pristine interface consistently indicate n-type doping and electron transfer from Ge to graphene. In contrast, modeling of the oxidized interface predicts a reversal to p-type doping, providing a plausible explanation for the different doping polarities reported in the literature. Machine-learning-assisted classification of the STS data resolves distinct local electronic regimes, ranging from nearly free-standing graphene on nanofacet tops to more strongly coupled inter-facet regions and nanoribbon-like regions with distinct local electronic responses. By identifying the mechanisms governing local graphene-substrate interactions and charge transfer, our study provides guidelines for tailoring the synthesis process and minimizing defect formation during delamination. These insights support the production of high-quality graphene layers for electronic devices and for transfer as protective coatings for air-sensitive materials.
Related
- Experimental evidence of forward energy transfer induced by core-edge coupling in multiscale tokamak turbulence
- Electronic transport in hBN-encapsulated graphene limited by remote phonon scattering
- Direct observation of the electronic structure of even-layer puckered SnTe monolayer films on graphene
- Electronic properties and topological aspects of graphene nanohelicoids
- Coherent Electronic Lensing in Proximitized Graphene
BibTeX
@misc{dabrowski2026electronic,
title = {{Electronic Coupling and Charge-Transfer Landscape of Graphene on Ge(001)/Si(001): Multiscale Analysis Assisted by Machine Learning}},
author = {Pawel Dabrowski and Przemysław Przybysz and Maciej Rogala and Iaroslav Lutsyk and Paweł Krukowski and Witold Kozłowski and Michał Piskorski and Piotr Milczarski and Iwona Pasternak and Jakub Sitek and Marek Kopciuszyński and Ryszard Zdyb and Jagoda Sławińska and Pawel J. Kowalczyk},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.37827},
eprint = {2609.37827},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.37827},
}
RIS
TY - UNPB
TI - Electronic Coupling and Charge-Transfer Landscape of Graphene on Ge(001)/Si(001): Multiscale Analysis Assisted by Machine Learning
AU - Pawel Dabrowski
AU - Przemysław Przybysz
AU - Maciej Rogala
AU - Iaroslav Lutsyk
AU - Paweł Krukowski
AU - Witold Kozłowski
AU - Michał Piskorski
AU - Piotr Milczarski
AU - Iwona Pasternak
AU - Jakub Sitek
AU - Marek Kopciuszyński
AU - Ryszard Zdyb
AU - Jagoda Sławińska
AU - Pawel J. Kowalczyk
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.37827
UR - https://arxiv.org/abs/2609.37827
AB - Understanding and controlling charge transfer at graphene-semiconductor interfaces is essential for the integration of two-dimensional materials into silicon-compatible technologies. Here, we combine ultraviolet photoelectron spectroscopy (UPS), Kelvin probe force microscopy (KPFM), angle-resolved photoemission spectroscopy (ARPES), scanning tunneling spectroscopy (STS) and density functional theory (DFT) to resolve work-function modulation and electronic coupling in graphene grown on Ge(001)/Si(001). UPS and KPFM reveal a spatially non-uniform work-function landscape correlated with the nanofaceted morphology of the substrate. ARPES and DFT calculations for the pristine interface consistently indicate n-type doping and electron transfer from Ge to graphene. In contrast, modeling of the oxidized interface predicts a reversal to p-type doping, providing a plausible explanation for the different doping polarities reported in the literature. Machine-learning-assisted classification of the STS data resolves distinct local electronic regimes, ranging from nearly free-standing graphene on nanofacet tops to more strongly coupled inter-facet regions and nanoribbon-like regions with distinct local electronic responses. By identifying the mechanisms governing local graphene-substrate interactions and charge transfer, our study provides guidelines for tailoring the synthesis process and minimizing defect formation during delamination. These insights support the production of high-quality graphene layers for electronic devices and for transfer as protective coatings for air-sensitive materials.
ER -