Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO(3) nanostructures doped in PVP interfacial layer.
- DOI
- 10.1038/s41598-023-41000-z
- Published
- 2023 Aug 22
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-023-41000-z,
title = {Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO(3) nanostructures doped in PVP interfacial layer.},
author = {Barkhordari A and Mashayekhi HR and Amiri P and Özçelik S and Altındal Ş and Azizian-Kalandaragh Y},
year = {2023},
journal = {Scientific reports},
doi = {10.1038/s41598-023-41000-z},
url = {https://doi.org/10.1038/s41598-023-41000-z}
}RIS
TY - JOUR TI - Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO(3) nanostructures doped in PVP interfacial layer. AU - Barkhordari A AU - Mashayekhi HR AU - Amiri P AU - Özçelik S AU - Altındal Ş AU - Azizian-Kalandaragh Y PY - 2023 JO - Scientific reports DO - 10.1038/s41598-023-41000-z UR - https://doi.org/10.1038/s41598-023-41000-z ER -
APA
A, B., HR, M., P, A., S, Ö., Ş, A., & Y, A. (2023). Machine learning approach for predicting electrical features of Schottky structures with graphene and ZnTiO(3) nanostructures doped in PVP interfacial layer.. Scientific reports. https://doi.org/10.1038/s41598-023-41000-z
Source records
- pubmed · retrieved 2026-09-26T04:51:54.069Z
- europe-pmc · retrieved 2026-09-26T04:51:54.084Z